Mitochondrial-Related Transcriptome Feature Correlates with Prognosis, Vascular Invasion, Tumor Microenvironment, and Treatment Response in ...
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Hindawi Oxidative Medicine and Cellular Longevity Volume 2022, Article ID 1592905, 28 pages https://doi.org/10.1155/2022/1592905 Research Article Mitochondrial-Related Transcriptome Feature Correlates with Prognosis, Vascular Invasion, Tumor Microenvironment, and Treatment Response in Hepatocellular Carcinoma Yizhou Wang ,1 Feihong Song ,2 Xiaofeng Zhang ,1 and Cheng Yang 2 1 Fourth Department of Hepatic Surgery, Third Aﬃliated Hospital of Second Military Medical University, Shanghai 200438, China 2 Department of Special Treatment, Third Aﬃliated Hospital of Second Military Medical University, Shanghai 200438, China Correspondence should be addressed to Xiaofeng Zhang; email@example.com and Cheng Yang; firstname.lastname@example.org Yizhou Wang and Feihong Song contributed equally to this work. Received 8 November 2021; Accepted 30 March 2022; Published 30 April 2022 Academic Editor: Junmin Zhang Copyright © 2022 Yizhou Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, which was highly correlated with metabolic dysfunction. Nevertheless, the association between nuclear mitochondrial-related transcriptome and HCC remained unclear. Materials and Methods. A total of 147 nuclear mitochondrial-related genes (NMRGs) were downloaded from the MITOMAP: A Human Mitochondrial Genome Database. The training dataset was downloaded from The Cancer Genome Atlas (TCGA), while validation datasets were retrieved from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The univariate and multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied to construct a NMRG signature, and the value of area under receiver operating characteristic curve (AUC) was utilized to assess the signature and nomogram. Then, data from the Genomics of Drug Sensitivity in Cancer (GDSC) were used for the evaluation of chemotherapy response in HCC. Results. Functional enrichment of diﬀerentially expressed genes (DEGs) between HCC and paired normal tissue samples demonstrated that mitochondrial dysfunction was signiﬁcantly associated with HCC development. Survival analysis showed a total of 35 NMRGs were signiﬁcantly correlated with overall survival (OS) of HCC, and the LASSO Cox regression analysis further identiﬁed a 25- NMRG signature and corresponding prognosis score based on their transcriptional proﬁling. HCC patients were divided into high- and low-risk groups according to the median prognosis score, and high-risk patients had signiﬁcantly worse OS (median OS: 27.50 vs. 83.18 months, P < 0:0001). The AUC values for OS at 1, 3, and 5 years were 0.79, 0.77, and 0.77, respectively. The prognostic capacity of NMRG signature was veriﬁed in the GSE14520 dataset and ICGC-HCC cohort. Besides, the NMRG signature outperformed each NMRG and clinical features in prognosis prediction and could also diﬀerentiate whether patients presented with vascular invasions (VIs) or not. Subsequently, a prognostic nomogram (C-index: 0.753, 95% CI: 0.703~0.804) by the integration of age, tumor metastasis, and NMRG prognosis score was constructed with the AUC values for OS at 1, 3, and 5 years were 0.82, 0.81, and 0.82, respectively. Notably, signiﬁcant enrichment of regulatory and follicular helper T cells in high-risk group indicated the potential treatment of immune checkpoint inhibitors for these patients. Interestingly, the NMRG signature could also identify the potential responders of sorafenib or transcatheter arterial chemoembolization (TACE) treatment. Additionally, HCC patients in high-risk group appeared to be more sensitive to cisplatin, vorinostat, and methotrexate, reversely, patients in low-risk group had signiﬁcantly higher sensitivity to paclitaxel and bleomycin instead. Conclusions. In summary, the development of NMRG signature provided a more comprehensive understanding of mitochondrial dysfunction in HCC, helped predict prognosis and tumor microenvironment, and provided potential targeted therapies for HCC patients with diﬀerent NMRG prognosis scores.
2 Oxidative Medicine and Cellular Longevity GEPIA2 Database: 369 HCC tissues vs 50 paired normal tissues 2, 207 DEGs identified Functional dysfunctions of mitochondria EGs rial related D ochond 147 Mit Training cohort Univariate cox regression analysis TCGA-LIHC cohort: 365 HCC patients 35 NMRGs associated with OS LASSO cox regression analysis Multivariate cox regression analysis ients Coeffic Validation cohorts Identification of a 25-NMRGs signature ICGC-HCC cohort & GSE14520 Stratify patients into high- and low- risk groups Macro-VI Group VS Micro-VI Group VS None-VI Group Kaplan-Meier curves Prognostic nomogram Prognosis score Kaplan-meier curves + + ROC curves Functional enrichment + + Clinical association Tumor microenvironment + Target therapy Figure 1: The ﬂow-process diagram for the construction of the NMRG signature and exploration of clinicopathological association and potential targeted therapy. 1. Introduction recurrence and poor performance status . However, the limitation in the detection of VI hinders its application as Globally, primary liver cancer is one of the most aggressive a robust biomarker for determining the clinical outcomes and diﬃcult-to-treat malignant cancers, with a 5-year sur- of HCC patients. Therefore, novel prognostic models and vival rate of less than 21% . Hepatocellular carcinoma better prognostic molecular markers are urgently required (HCC) comprises the most common type of primary liver to improve the HCC management and accurately predict cancer, accounting for 90% of all liver cancer cases . clinical outcomes of HCC, especially for the AFP-negative Besides, patients with HCC were often diagnosed in HCC. advanced stage owing to no apparent symptoms in early The liver and mitochondria are the two centers of stage, probably leading to the poor survival. With the metabolism at the whole organism and cellular levels, approval of sorafenib, lenvatinib, and other immunotherapy respectively. Emerging evidences clearly suggested that regimens for advanced HCC patients, the survival of metas- mitochondrial dysfunction or maladaptation contributed to tatic or unresectable HCC patients has been improved in the detrimental eﬀects on hepatocyte bioenergetics, reactive these years, but the therapeutic outcomes are still largely oxygen species (ROS) homeostasis, endoplasmic reticulum unsatisfactory [3, 5]. As is known, alpha-fetoprotein (AFP) (ER) stress, inﬂammation, and cell death [7–9]. The liver is the most widely used serum biomarker for the HCC detec- mitochondria have unique features because the liver plays tion and treatment evaluation; however, it is not a robust a central role in the regulation of a variety of metabolic func- and speciﬁc biomarker for HCC . In addition, vascular tions including maintaining the homeostasis of carbohy- invasion (VI), as a critical risk factor, is the main herald of drate, lipid, amino acid, and protein. Previous studies have HCC recurrence though for HCC patients receiving surgical revealed critical roles of mitochondrial genes in the carcino- resection . Vascular invasion could be divided into two genesis and development of HCC. For example, mitochon- subtypes, macroscopic vascular invasion and microscopic drial trans-2-enoyl-CoA reductase (MECR) had been vascular invasion, both were highly associated with tumor identiﬁed as an oncogene which was signiﬁcantly
Oxidative Medicine and Cellular Longevity 3 Table 1: Clinicopathological features of 365 HCC patients from the In this study, we initially analyzed the transcriptome TCGA. proﬁling of 147 NMRGs and the corresponding clinical data of patients with HCC from TCGA and then identiﬁed 35 Variables Number NMRGs having signiﬁcant inﬂuence on the survival of Total 365 HCC patients by the univariate Cox regression analysis. Sub- Age Median (range) 61 [16, 90] sequently, we used the least absolute contraction and selec- Gender Male 246 tion operator (LASSO) regression analysis and ﬁnally Female 119 developed a novel 25-NMRG prognosis signature. Besides, Alcohol consumption Yes 115 the prediction eﬃcacy of the established NMRG prognosis signature was veriﬁed in the validation datasets, including No 250 ICGC-HCC cohort from the ICGC and GSE14520 from AFP Median (range) 15 [14, 203540] ng/mL the GEO. Based on the NMRG signature, a nomogram was VI Non-VI 211 further constructed to predict the prognosis of HCC. More- Micro-VI 94 over, the good AUC values demonstrated the reliable and Macro-VI 17 stable predicting ability of the prognosis signature and Clinical stage Stage I 170 nomogram. The functional diﬀerentiation, tumor microen- Stage II 84 vironment, and treatment response of precision therapy between high- and low-risk groups were further investigated Stage III 83 to promote the precision medicine for HCC patients. The Stage IV 4 study design was mainly exhibited in a work ﬂowchart NA 24 (Figure 1). Histological grading G1 55 G2 175 2. Materials and Methods G3 118 2.1. Data Collection. The gene expression data and the clin- G4 12 ical information of 365 HCC patients were collected from NA 15 the Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort T stage T1 180 from TCGA which was regarded as the training dataset T2 91 (Table 1), while the ICGC-HCC (namely, LIRI-JP) cohort T3 78 with 260 patients and GSE14520 with 242 patients from T4 13 the GEO were deﬁned as two independent validation data- sets. A comprehensive list of NMRGs was downloaded from NA 3 the MITOMAP: A Human Mitochondrial Genome Database N stage N0 248 (https://www.mitomap.org/MITOMAP, last updated date: N1 4 January 15th, 2021), which comprised a total of 147 NMRGs. NA 113 M stage M0 263 2.2. The Analysis of Diﬀerentially Expressed Genes in HCC. The transcriptome data analysis between 369 HCC tumor M1 3 tissues and 50 adjacent paired normal tissues was conducted NA 99 online in the GEPIA (http://gepia2.cancer-pku.cn) for the Hepatitis_B Yes 102 identiﬁcation of the diﬀerentially expressed genes No 263 (DEGs, ∣log 2 − fold change ðFCÞ ∣ >1, Q − value < 0:01) Hepatitis_C Yes 56 between the HCC samples and normal samples. The visual- No 309 ization of the volcano plot and heatmap was performed HCC: hepatocellular carcinoma; VI: vascular invasion; NA: not applicable. using the “ggplot” package. 2.3. Signature Construction Based on Nuclear Mitochondrial- Related Genes. The univariate Cox regression was used to overexpressed in HCC cell lines . Likewise, overexpres- identify OS-associated NMRGs. Next, the LASSO regression sion of mitofusin1 (MFN1) in HCC cells promoted mito- model was selected to minimize the overﬁtting and identify chondrial fusion and inhibited cell proliferation, invasion, the most signiﬁcant survival-associated NMRGs in HCC and migration via modulating metabolic shift from aerobic via the “glmnet” package. Meanwhile, the multivariate Cox glycolysis to oxidative phosphorylation . In addition, it regression analysis was then used to determine the corre- has been proved that upregulation of aspartyl-tRNA synthe- sponding coeﬃcients. The following formula based on a tase (DARS2) promoted hepatocarcinogenesis through the combination of coeﬃcient and gene expression was used to MAPK/NFAT5 pathway . However, most of these stud- calculate the prognosis score: ies focused on a single gene instead of the integrated cluster of mitochondrial-related genes. Therefore, it will be of more n value to evaluate the role of all the mitochondrial-related Prognosis score = 〠 Genei ∗ coef i, ð1Þ genes in the prognosis of HCC. i=1
4 Oxidative Medicine and Cellular Longevity Group 15 10 5 0 –5 –5 Group N T (a) Figure 2: Continued.
Oxidative Medicine and Cellular Longevity 5 150 100 –Log10 (adjp) 50 0 –5 0 5 Log2 (fold change) (b) qvalue Mitochondrial inner membrane ATP metabolic process Mitochondrial protein complex Electron transport chain Respiratory electron transport chain Inner mitochondrial membrane protein complex 0.03 ATP synthesis coupled electron transport Electron transfer activity Mitochondrial ATP synthesis coupled electron transport Mitochondrial respirasome Protein localization to mitochondrion Establishment of protein localization to mitochondrion Mitochondrial respiratory chain complex assembly 0.02 DNA–dependent ATPase activity ATP–dependent chromatin remodeling Protein targeting to mitochondrion Establishment of protein localization to mitochodrial membrane Mitochondrial respiratory chain complex I Mitochondrial respiratory chain complex I assembly Mitochondrial electron transport, NADH to ubiquinone Protein insertion into mitochondrial membrane 0.01 Aeribic electron transport chain Outer mitochondrial membrane protein complex Mitochondrial electron tansport, cytochrome c to oxygen Proton–transporting ATPase activity, rotational mechanism ATPase activity, coupled to transmembrane Movement of ions, rotational mechanism Proton–transporting V–type ATPase complex Mitochondrial respiratory chain complexIV 20 60 40 80 (c) Figure 2: Mitochondrial dysfunction potentially promoted the hepatocarcinogenesis. (a) Transcriptional proﬁling of HCC and adjacent paired normal tissues. (b) Diﬀerentially expressed genes (DEGs) between HCC and adjacent paired normal tissues. Red dots represented signiﬁcant upregulation and blue dots represented signiﬁcant downregulation of DEGs in HCC tissues. (c) Identiﬁcation of biological functions via the GO pathway enrichment analysis.
6 Oxidative Medicine and Cellular Longevity ⁎ MRPL3 HR: 8.62 (2.12 − 34.98) ⁎ LARS HR: 7.48 (2.11 − 26.48) ⁎ LRPPRC HR: 6.34 (1.68 − 23.92) ⁎ PDSS1 HR: 4.89 (2.17 − 11.01) ⁎ GARS HR: 4.38 (1.70 − 11.26) ⁎ TRMT10C HR: 4.05 (1.22 − 13.49) ⁎ HARS2 HR: 3.95 (1.11 − 14.04) ⁎ PARS2 HR: 3.85 (1.44 − 10.25) ⁎ C12orf65 HR: 3.73 (1.05 − 13.27) 34 34 33 32 29 25 21 19 15 11 2 ⁎ DLP1 HR: 3.61 (1.15 − 11.30) ⁎ HSPD1 HR: 3.57 (1.29 − 9.84) ⁎ MGME1 HR: 3.14 (1.03 − 9.56) ⁎ ATAD3 HR: 3.02 (1.39 − 6.57) 0.65 ⁎ HR: 2.69 (1.19 − 6.09) Survival related NMRGs CARS2 ⁎ MPV17 HR: 2.67 (1.43 − 4.96) ⁎ DARS2 HR: 2.50 (1.1 − 5.65) C−index ⁎ TRMU HR: 2.11 (1.03 − 4.29) 0.60 ⁎ UQCRQ HR: 0.5 (0.27 − 0.93) ⁎ CABC1 HR: 0.5 (0.27 − 0.93) ⁎ FRDA HR:0.48 (0.23 − 1.00) ⁎ COQ9 HR: 0.47 (0.22 − 0.99) ⁎ 0.55 COQ4 HR: 0.37 (0.16 − 0.85) ⁎ DNAJC19 HR: 0.36 (0.13 − 0.96) ⁎ ANT1 HR: 0.36 (0.17 − 0.74) ⁎ COQ6 HR: 0.34 (0.13 − 0.88) ⁎ COQ5 HR: 0.34 (0.12 − 0.99) −7 −6 −5 −4 −3 −2 ⁎ SPG7 HR: 0.32 (0.11 − 0.92) ⁎ TK2 HR: 0.31 (0.12 − 0.75) NDUFV2 ⁎ Log (λ) HR: 0.28 (0.11 − 0.70) ⁎ COX15 HR: 0.21 (0.06 − 0.82) ⁎ VARS2 HR: 0.21 (0.07 − 0.60) ⁎ ISCU HR: 0.19 (0.06 − 0.62) ⁎ COQ7 HR: 0.15 (0.04 − 0.55) ⁎ NDUFAF1 HR: 0.12 (0.05 − 0.30) ⁎ MTFMT HR: 0.11 (0.03 − 0.42) 0.1 1.0 10.0 Hazard ratio (a) (b) 1.00 +++ 1.0 +++ + ++++++++++++ + ++ ++++ ++ ++++ ++++++++ 0.8 0.75 ++ ++ Survival probability +++ +++++++ + +++++ ++ ++ ++ +++++++++ ++ +++++ 0.6 Sensitivity +++ 0.50 ++++++ + ++++ ++++++ ++ 0.4 + +++ + 0.25 + +++ ++ 0.2 p < 0.0001 0.00 0.0 0 30 60 90 120 0.0 0.2 0.4 0.6 0.8 1.0 Time Specificity Prognosis_score 1 year OS, AUC = 0.79 + Low 3 years OS, AUC = 0.77 + High 5 years OS, AUC = 0.77 (c) (d) Figure 3: Continued.
Oxidative Medicine and Cellular Longevity 7 1.0 1.00 +++ ++++++++++++ +++ ++++++++++++++++++ +++++ ++++ 0.8 +++ +++++++ ++++ 0.75 +++++++++++ Survival probability ++ 0.6 Sensitivity + ++ +++ 0.50 ++ + + 0.4 0.25 p < 0.0001 0.2 0.00 0.0 0 20 40 60 80 0.0 0.2 0.4 0.6 0.8 1.0 Time Specificity Prognosis_score 1 year OS, AUC = 0.78 + Low 2 years OS, AUC = 0.74 + High 3 years OS, AUC = 0.78 (e) (f) 1.0 1.00 ++ ++ + ++ + + 0.8 + 0.75 + ++ ++ Survival probability +++++++++++++ + + + +++++ ++++ + 0.6 Sensitivity + + + ++++ 0.50 ++++++++++++++ 0.4 0.25 p = 0.012 0.2 0.00 0.0 0 20 40 60 0.0 0.2 0.4 0.6 0.8 1.0 Time Specificity Prognosis_score 1 year OS, AUC = 0.61 + Low 3 years OS, AUC = 0.56 + High 5 years OS, AUC = 0.58 (g) (h) Figure 3: Construction and validation of the nuclear mitochondrial-related gene (NMRG) signature. (a) Univariate Cox regression analysis for selection of NMRGs correlated with overall survival of HCC patients. (b) LASSO Cox regression analysis determined a total of 25 NMRGs as the optimal combination for the NMRG signature construction. The Kaplan-Meier curves for HCC patients in high- and low-risk groups, from the TCGA cohort (c), from the ICGC-HCC cohort (e), and from the GSE14520 dataset (g). The ROC curves for OS at 1, 3, and 5 years in TCGA cohort (d), in ICGC-HCC cohort (f), and in GSE14520 dataset (h). where n, Genei, and coefi represent the number of genes 2.4. Establishment of a Novel Prognostic Nomogram for HCC. involved in the signature, the level of gene expression, and Several predominant prognostic factors in clinic including the coeﬃcient value, respectively. age, gender, AFP, vascular invasion, histological grading, To stratify patients into low- and high-risk groups, a clinical stages, TNM stages, alcohol consumption, and hepa- median value of prognosis score was set for the cutoﬀ value. titis status, together with prognosis score of NMRGs signa- The Kaplan-Meier survival curve analysis was conducted by ture were investigated via the univariate and multivariate using the “survival” and “survminer” packages, and log-rank Cox regression analyses using the “rms” and “survival” pack- test was performed to evaluate the survival rates between the ages, to ﬁnd the independent prognostic factors. Next, we low- and high-risk groups. The AUC values were calculated established a prognostic nomogram based on the indepen- via using the “timeROC” package. dent prognostic factors.
8 Oxidative Medicine and Cellular Longevity p = 0.21 p = 1.00 75 0.9 Age 0.6 50 0.3 25 0.0 High Low High Low Group Gender High Female Low Male (a) (b) p = 0.57 p < 0.01 0.9 100000 AFP 0.6 1000 0.3 10 0.0 High Low High Low Alcohol_consumption Group No High Yes Low (c) (d) p = 0.05 p < 0.01 0.9 0.9 0.6 0.6 0.3 0.3 0.0 0.0 High Low High Low Stage I Stage IV G1 G4 Stage II NA G2 NA Stage III G3 (e) (f) p = 0.15 p = 0.62 0.9 0.9 0.6 0.6 0.3 0.3 0.0 0.0 High Low High Low T_stage N_stage T1 T4 N0 T2 NA N1 T3 NA (g) (h) Figure 4: Continued.
Oxidative Medicine and Cellular Longevity 9 p = 0.12 p = 0.82 0.9 0.9 0.6 0.6 0.3 0.3 0.0 0.0 High Low High Low M_stage Hepatitis_B M0 No M1 Yes NA (i) (j) p = 0.25 0.9 0.6 0.3 0.0 High Low Hepatitis_C No Yes (k) Figure 4: Association analysis between the NMRG signature and clinical features. (a) The boxplots showed the distribution of age at diagnosis between the high- and low-risk groups. (b) The percentage-staked bar plots for gender distribution between the high- and low- risk groups. (c) The percentage-staked bar plots for the distribution of alcohol consumption between the high- and low-risk groups. (d) The boxplots showed the distribution of AFP concentration between the high- and low-risk groups. The percentage-staked bar plots for the distribution of neoplasm cancer stages (e), histological grading (f), T stages (g), N stages (h), M stages (i), Hepatitis_B status (j), and Hepatitis_C status (k) between the high- and low-risk groups. 2.5. Functional Enrichment Analysis. The GO (Gene Ontol- tumor inﬁltrated cells. Then, a heatmap of gene signature ogy) enrichment analysis was performed to determine sig- expression proﬁles denoting the activities of angiogenesis niﬁcantly enriched GO terms for the diﬀerentially and immune further clariﬁed the diﬀerentiation of tumor expressed genes between normal and tumor tissue samples. microenvironment between the high- and low-risk groups In order to investigate any changes in biological functions . Finally, the CIBERSORT algorithm analysis was and related pathways between the high- and low-risk groups, employed to explore 22 types of tumor-inﬁltrating immune HALLMARK gene set (including 50 gene sets from Molecu- cells. lar Signature Database, https://www.gsea-msigdb.org/gsea/ msigdb/, ) enrichment analysis (GSEA), and KEGG 2.7. The Evaluation of Precision Treatment and (Kyoto Encyclopedia of Genes and Genomes) pathway Chemotherapy Response. The GSE104580 dataset, including enrichment analysis were performed. GSEA normalized the the transcriptomic data of 147 HCC patients (81 responders Enrichment Score for each gene set to account for the varia- vs. 66 nonresponders) treated with TACE treatment, was tion in gene set sizes, yielding a normalized enrichment enrolled in the present study to explore the predictive ability score (NES). Enrichment analysis was performed by the of novel prognosis score in the treatment response. Besides, “clusterproﬁler” package and visualized using the “ggplot2”. GSE109211 dataset, including a total of 67 HCC patient The diﬀerentially expressed genes were deﬁned with ∣log2 samples treated with sorafenib (21 responders vs. 46 nonre- − fold change ðFCÞ ∣ >1, P < 0:05 in the functional enrich- sponders) from the phase III STORM clinical trial ment analysis. (NCT00692770), was investigated to evaluate the capacity of prognosis score to predict sorafenib eﬃcacy . Mean- 2.6. Tumor Microenvironment Analysis in HCC. The stro- while, the cell line data from the Genomics of Drug Sensitiv- mal, immune, and ESTIMATE scores were calculated using ity in Cancer (GDSC, https://www.cancerrxgene.org/) were ESTIMATE , which could illustrate the properties of downloaded to predict the treatment response of
10 Oxidative Medicine and Cellular Longevity p < 0.01 p = 0.0062 5 0.9 Prognosis_score 4 0.6 3 0.3 0.0 2 High Low 1 Vascular_invasion None VI Macro None Vascular_invasion Micro NA None VI (a) (b) p = 0.0058 1.00 + +++ 6 ++ ++++ ++++++ p = 0.041 +++++++++++ p = 0.062 ++++++++++ Prognosis_score 0.75 +++++++++++ +++++ Survival probability ++++++ ++++++ 4 ++++++ ++ + ++ +++++++ ++++++++ 0.50 ++++ + 2 ++ +++ 0.25 p = 0.40 None Micro Macro Vascular_invasion 0.00 None 0 30 60 90 120 Micro Time Macro + Micro-VI group + None-VI group (c) (d) 1.00 + +++ 1.00 + +++++++ ++ +++ ++ ++++++ +++ ++++ ++++ 0.75 +++++++ 0.75 ++++++++ ++++ + Survival probability Survival probability ++++++++ ++++++ ++++ ++ ++ + +++++++ + + +++ + + + ++++ ++ ++ 0.50 ++++ 0.50 + +++ + + ++ + +++ 0.25 0.25 + p = 0.024 p = 0.16 0.00 0.00 0 30 60 90 120 0 25 50 75 100 Time Time + Micro-VI group + Micro-VI group + None-VI group + None-VI group (e) (f) Figure 5: The application of the NMRGs signature in the groups with vascular invasions (VIs) or not. (a) The percentage-staked bar plots for the distribution of VIs between high- and low- risk groups. (b) Comparison of prognosis score between groups with VIs or not. (c) Comparison of prognosis score between groups with macro-VIs, micro-VIs, and without VIs. The Kaplan-Meier curves for HCC patients between micro-VI and none-VI groups (d). (e) Green and purple lines represent macro-VI group and none-VI group, respectively. (f) Green and purple lines represent macro-VI group and micro-VI group, respectively.
Oxidative Medicine and Cellular Longevity 11 Macro-VI Group Micro-VI Group 1.00 1.00 + + +++ + + + + + +++ +++++++ +++ + 0.75 0.75 +++++ + ++++ + ++ Survival probability Survival probability +++ + + + ++ + ++ 0.50 0.50 + + + ++ 0.25 + + 0.25 p = 0.037 p = 0.15 0.00 0.00 0 10 20 30 40 50 0 25 50 75 100 Time Time + Low risk group + Low risk group + High risk group + High risk group (a) (b) Figure 6: Comparison of overall survival between high- and low-risk HCC patients in the groups with macro-VIs or micro-VIs. The Kaplan-Meier curves between high- and low-risk HCC patients in the macro-VI group (a) and micro-VI group (b). Table 2: Hazard ratios for the NMRG signature and clinical dance index, time-dependent ROC, and calibration were also features via the multivariate Cox regression analysis. important indicators used to assess the nomogram. P < 0:05 Index Hazard ratio 95% CI P value was considered statistically signiﬁcant. Prognosis score 4.65 2.59-8.34 1, Q − value T stage 1.08 0.1-11.61 0.61 < 0:01, Supplementary Table 1), and it was demonstrated that there were 1,482 genes signiﬁcantly upregulated and N stage 0.43 0.04-4.48 0.48 725 genes signiﬁcantly downregulated in the HCC tumor M stage 14.53 1.31-160.62 0.03 samples (Figures 2(a), 2(b)). In addition, biological Hepatitis_B 0.69 0.31-1.54 0.36 functions and involved pathways of these identiﬁed 2,207 Hepatitis_C 1.41 0.49-4.08 0.52 DEGs were analyzed by GO enrichment analysis, revealing Vascular invasion 1.43 0.68-3.01 0.34 that the DEGs were abundantly enriched in the pathways CI: conﬁdence interval. Bold for “signiﬁcant” in statistical analysis. related to cell metabolisms, including mitochondrial inner membrane, ATP-dependent chromatin remodeling, and mitochondrial electron transport, NADH to ubiquinone chemotherapeutic regimens between high- and low-risk pathways (Figure 2(c)), indicating that mitochondrial groups, and the chemical drugs utilized in HCC, such as cis- dysfunction was closely related to the carcinogenesis and platin, paclitaxel, and gemcitabine, for HCC patients were development of HCC. investigated. The index of half-maximal inhibitory concen- tration (IC50) was used for the response evaluation. 3.2. Construction of a Novel Nuclear Mitochondrial-Related Gene Prognosis Signature for HCC. Univariate Cox regres- 2.8. Statistical Analysis. All statistical analyses were con- sion analysis was performed to analyze the correlation ducted with the R package (v. 3.4.3, https://rstudio.com/). between the transcriptional expression level of 147 NMRGs Fisher’s test was executed for the comparison of categorical and the overall survival (OS) of HCC patients from the variables. The Kaplan-Meier curve analysis by using the TCGA cohort. It was found that the elevated expression of log-rank test was used to evaluate the statistical signiﬁcance 17 NMRGs was signiﬁcantly correlated with the poorer of the survival rates between diﬀerent risk groups. Concor- prognosis of HCC patients, whereas the overexpression of
12 Oxidative Medicine and Cellular Longevity ROC for 1 year OS ROC for 3 years OS 1.0 1.0 0.8 0.8 0.6 0.6 Sensitivity Sensitivity 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Specificity Specificity Prognosis score AUC = 0.79 Prognosis score AUC = 0.77 Vascular invasion AUC = 0.55 Vascular invasion AUC = 0.55 AFP AUC = 0.60 AFP AUC = 0.61 Histologic grade AUC = 0.51 Histologic grade AUC = 0.51 Gender AUC = 0.52 Gender AUC = 0.48 Alcohol consumption AUC = 0.48 Alcohol consumption AUC = 0.49 T stage AUC = 0.67 T stage AUC = 0.66 N stage AUC = 0.51 N stage AUC = 0.52 M stage AUC = 0.51 M stage AUC = 0.53 Hepatitis B AUC = 0.41 Hepatitis B AUC = 0.36 Hepatitis C AUC = 0.49 Hepatitis C AUC = 0.53 (a) (b) ROC for 5 years OS 0 10 20 30 40 50 60 70 80 90 100 Points 1.0 Prognosis score 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 0.8 Age 10 40 70 M1 M stage 0.6 M0 Sensitivity Total points 0 10 20 30 40 50 60 70 80 90 100 0.4 1 year OS 0.9 0.7 0.5 0.3 0.1 3 years OS 0.2 0.9 0.7 0.5 0.3 0.1 5 years OS 0.9 0.7 0.5 0.3 0.1 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Specificity Prognosis score AUC = 0.77 Vascular invasion AUC = 0.52 AFP AUC = 0.62 Histologic grade AUC = 0.56 Gender AUC = 0.48 Alcohol consumption AUC = 0.54 T stage AUC = 0.65 N stage AUC = 0.52 M stage AUC = 0.52 Hepatitis B AUC = 0.39 Hepatitis C AUC = 0.54 (c) (d) 1.0 1.0 0.8 0.8 Actual survival 0.6 0.6 Sensitivity 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted survival Specificity 1 year OS 1 year OS, AUC = 0.82 3 years OS 3 years OS, AUC = 0.81 5 years OS 5 years OS, AUC = 0.82 (e) (f) Figure 7: Construction of a novel nomogram for HCC patients based on the NMRG signature. The ROC curves of a variety of clinical features for overall survival (OS) at 1 (a), 3 (b), and 5 years (c). (d) The NMRG-based nomogram was constructed to predict the OS of HCC patients. (e) The calibration plots for the evaluation of predicted OS at 1, 3, and 5 years. (f) The ROC curves of the nomogram for OS at 1, 3, and 5 years in the analysis of TCGA-HCC cohort.
Oxidative Medicine and Cellular Longevity 13 Altered in 162 (89.01%) of 182 samples in high-risk group. 1147 NA p = 0.34 0 83 1000 0 TP53 46% TTN 29% CTNNB1 21% NA MUC16 17% LRP1B 12% CSMD3 Mutation_count 12% 100 FAT3 11% MT−ND5 11% RYR2 11% ARID1A 10% OBSCN 10% ABCA13 10% DNAH7 10% ALB 9% 10 FRAS1 9% MUC4 9% APOB 9% CACNA1E 9% HSPG2 8% SPTA1 8% High Low Missense_mutation In_frame_ins Group Frame_shift_del In_frame_del a High Nonsense_mutation Translation_start_site a Low Splice_site Multi_hit Frame_shift_ins (a) (b) CTNNB1 Altered in 155 (86.59%) of 179 samples in low-risk group. 0.30 1276 TTN 0 57 Low-risk group 0 CTNNB1 32% 0.20 TTN 27% TP53 15% ALB 13% TP53 PCLO 12% PCLO APOB 12% 0.10 MUC16 MUC16 12% MT−ND5 XIRP2 MT−ND5 10% ARID2 RYR2 XIRP2 9% FBN1 MT−CO3 CSMD3 LRP1B CACNA1E 8% MAGEL2 FRAS1 RYR1 8% ANK2 FAT3 USH2A 8% 0.00 DNAH17 MCTP2 AHNAK2 8% ARID2 8% KMT2D 8% 0.00 0.10 0.20 0.30 0.40 0.50 RYR2 8% BAP1 7% High-risk group COL11A1 7% HMCN1 7% Fisher’s test OBSCN 7% ns Missense_mutation Nonsense_mutation P < 0.05 Frame_shift_del Splice_site ABS (Log OR) Frame_shift_ins In_Frame_ins 0.25 0.75 In_frame_del Multi_hit 0.50 1.00 (c) (d) Figure 8: Continued.
14 Oxidative Medicine and Cellular Longevity 0.30 CTNNB1 Altered in 46 (12.33%) of 373 samples. 1276 0.10 Low-risk group 0 15 AXIN1 0 APC ATR 0.03 RECQL4 BRCA2 PTEN ATR 4% BRCA1 ATM DEPDC5 TSC2 BRCA2 3% MTOR 0.01 ATM 2% AMER1 0.01 0.03 0.10 BRCA1 2% High-risk group RECQL4 2% Fisher’s test ns Group P < 0.05 ABS (LogOR) Pathway Missense_mutation In_frame_del 0.20 DDR Splice_site Frame_shift_del 0.40 PI3K Frame_shift_ins Multi_hit 0.60 WNT Group High Low (e) (f) Altered in 57 (15.28%) of 373 samples. 1155 0 13 0 PIK3CA 3% TSC2 3% PTEN 3% MTOR 3% TSC1 2% DEPDC5 2% Group Missense_mutation Frame_shift_ins Frame_shift_del In_frame_del Nonsense_mutation Multi_hit Splice_site Group High Low (g) Figure 8: Continued.
Oxidative Medicine and Cellular Longevity 15 Altered in 133 (35.66%) of 373 samples. 1276 0 97 0 CTNNB1 26% AXIN1 6% APC 3% WIF1 2% AMER1 2% Group Missense_mutation Nonsense_mutation Splice_site Frame_shift_ins Frame_shift_del Multi_hit In_Frame_del Group High Low (h) Figure 8: The analysis of genomic alterations between the high- and low-risk groups. (a) The boxplots showed the mutation counts between the high- and low-risk groups. The genomic proﬁling of the top 20 most frequently altered genes in the high-risk group (b) and in the low- risk group (c). (d) Genomic alteration enrichment of altered genes between the high- and low-risk groups. (e) Genomic alteration enrichment of altered signaling pathways between the high- and low-risk groups. The genomic proﬁles of altered events in DDR (f), PI3K (g), and WNT signaling pathways (h). other 18 NMRGs signiﬁcantly contributed to the improved OS: 48.02 months vs. unreached, P < 0:0001, Figure 3(e)). survival (P < 0:05, Figure 3(a)). These 35 OS-related NMRGs The AUC values for predicting OS at the 1-, 2-, and 3-year were then enrolled in the LASSO Cox regression analysis, timepoints were 0.78, 0.74, and 0.78, respectively ﬁnally constructing a NMRG prognosis signature for HCC (Figure 3(f)). Furthermore, the NMRG signature was veri- patients based on the transcriptional proﬁling of selected ﬁed in another independent dataset of GSE14520 from the 25 NMRGs (NDUFV2, NDUFAF1, COX15, LRPPRC, GEO database. It could be also observed that patients in MPV17, CARS2, DARS2, GARS, HARS2, LARS, PARS2, high-risk group had signiﬁcantly worse OS (median OS: VARS2, MTFMT, TRMT10C, TRMU, C12ORF65, MRPL3, unreached vs. unreached, P = 0:012, Figure 3(g)). The AUC FRDA, ISCU, COQ6, COQ7, PDSS1, CABC1, SPG7, and values for predicting OS at 1, 3, and 5 years were 0.61, ATAD3), with the optimal value of λ ðλ = 0:0106127Þ 0.56, and 0.58, respectively (Figure 3(h)). (Figure 3(b)). This novel prognosis score was calculated by multiplying the gene expression of each gene and its corre- 3.4. Comparison of Clinicopathological Features between the sponding coeﬃcient (Supplementary Table 2), which was High- and Low-Risk Groups. The diﬀerences of clinicopath- obtained by the multivariate Cox regression analysis. ological features of patients from the high- and low-risk groups, in the TCGA cohort, were subsequently analyzed. 3.3. Survival Analysis and Validation of the NMRG The age at diagnosis of patients in the high-risk group did Signature. According to the median prognosis score value, not diﬀer with that in the low-risk group (median age: 60 365 HCC patients were divided into high-risk group and [18, 85] vs. 63 [16, 90] months, P = 0:21, Figure 4(a)). Mean- low-risk group. The analysis of the Kaplan-Meier curve while, there was no statistically signiﬁcant diﬀerence in gen- showed that patients in high-risk group had signiﬁcantly der between these two groups (P > 0:05, Figure 4(b)). worse OS (median OS: 27.50 vs. 83.18 months, P < 0:0001, Besides, no signiﬁcant diﬀerence of the alcohol consumption Figure 3(c)). Time-dependent ROC analysis was used to level was found between the high- and low-risk groups, evaluate the prognostic evaluation ability of the NMRG sig- either (P = 0:57, Figure 4(c)). As for the level of AFP, it dem- nature (Figure 3(d)), and the AUC values at 1, 3, and 5 years onstrated that patients in high-risk group had the signiﬁ- for predicting OS were 0.79, 0.77, and 0.77, respectively. Fur- cantly higher level of AFP (median level: 28 vs. 7 ng/mL, thermore, two independent cohorts were retrieved to vali- P < 0:01, Figure 4(d)). Moreover, there were more patients date the NMRG signature. The Kaplan-Meier curve ̲ analysis demonstrated that patients in high-risk group, from from the high-risk group having advanced neoplasm cancer the ICGC cohort, had the signiﬁcantly worse OS (median stages (45.35% vs. 54.44% in stage I, 23.84% vs. 25.44% in
16 Oxidative Medicine and Cellular Longevity HALLMARK_E2F_TARGETS HALLMARK_G2M_CHECKPOINT HALLMARK_MYC_TARGETS_V1 HALLMARK_MITOTIC_SPINDLE HALLMARK_MYC_TARGETS_V2 HALLMARK_MTORC1_SIGNALING HALLMARK_DNA_REPAIR HALLMARK_UNFOLDED_PROTEIN_RESPONSE HALLMARK_SPERMATOGENESIS HALLMARK_UV_RESPONSE_UP HALLMARK_MYOGENESIS HALLMARK_UV_RESPONSE_DN HALLMARK_ANDROGEN_RESPONSE HALLMARK_COMPLEMENT HALLMARK_INTERFERON_GAMMA_RESPONSE HALLMARK_INTERFERON_ALPHA_RESPONSE HALLMARK_HEME_METABOLISM HALLMARK_PEROXISOME HALLMARK_OXIDATIVE_PHOSPHORYLATION HALLMARK_COAGULATION HALLMARK_ADIPOGENESIS HALLMARK_FATTY_ACID_METABOLISM HALLMARK_XENOBIOTIC_METABOLISM HALLMARK_BILE_ACID_METABOLISM −4 −2 0 2 NES (a) Figure 9: Continued.
Oxidative Medicine and Cellular Longevity 17 p-value 0.005 0.010 0.015 KEGG_CELL_CYCLE KEGG_DNA_REPLICATION KEGG_SPLICEOSOME KEGG_MISMATCH_REPAIR KEGG_HOMOLOGOUS_RECOMBINATION KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION KEGG_NON_HOMOLOGOUS_END_JOINING KEGG_RNA_DEGRADATION KEGG_OOCYTE_MEIOSIS KEGG_PYRIMIDINE_METABOLISM KEGG_BASE_EXCISION_REPAIR KEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION KEGG_BLADDER_CANCER KEGG_AMINOACYL_TRNA_BIOSYNTHESIS KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS KEGG_PURINE_METABOLISM KEGG_REGULATION_OF_ACTIN_CYTOSKELETON KEGG_PATHWAYS_IN_CANCER KEGG_ALZHEIMERS_DISEASE KEGG_ABC_TRANSPORTERS KEGG_RENIN_ANGIOTENSIN_SYSTEM KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS KEGG_PHENYLALANINE_METABOLISM KEGG_GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM KEGG_BIOSYNTHESIS_OF_UNSATURATED_FATTY_ACIDS KEGG_GLYCEROLIPID_METABOLISM KEGG_LYSINE_DEGRADATION KEGG_GLYCOLYSIS_GLUCONEOGENESIS KEGG_OXIDATIVE_PHOSPHORYLATION KEGG_PORPHYRIN_AND_CHLOROPHYLL_METABOLISM KEGG_PARKINSONS_DISEASE KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY KEGG_ARACHIDONIC_ACID_METABOLISM KEGG_ASCORBATE_AND_ALDARATE_METABOLISM KEGG_STARCH_AND_SUCROSE_METABOLISM KEGG_CITRATE_CYCLE_TCA_CYCLE KEGG_LIMONENE_AND_PINENE_DEGRADATION KEGG_PYRUVATE_METABOLISM KEGG_DRUG_METABOLISM_OTHER_ENZYMES KEGG_HISTIDINE_METABOLISM KEGG_ARGININE_AND_PROLINE_METABOLISM KEGG_LINOLEIC_ACID_METABOLISM KEGG_TYROSINE_METABOLISM KEGG_BETA_ALANINE_METABOLISM KEGG_STEROID_HORMONE_BIOSYNTHESIS KEGG_PPAR_SIGNALING_PATHWAY KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS KEGG_BUTANOATE_METABOLISM KEGG_PROPANOATE_METABOLISM KEGG_TRYPTOPHAN_METABOLISM KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM KEGG_PEROXISOME KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION KEGG_RETINOL_METABOLISM KEGG_DRUG_METABOLISM_CYTOCHROME_P450 KEGG_FATTY_ACID_METABOLISM KEGG_COMPLEMENT_AND_COAGULATION_CASCADES −2 0 2 NES (b) Figure 9: Functional enrichment analysis between the high- and low-risk groups. The HALLMARK gene set enrichment analysis (a) and the KEGG pathway enrichment analysis (b). P < 0:05 was considered statistically signiﬁcant. stage II, 30.23% vs. 18.34% in stage III, and 0.58% vs. 1.78% Figure 4(f)). However, no statistically signiﬁcant diﬀerence in stage IV, P = 0:05, Figure 4(e)) and higher histological in the tumor stage, lymph node invasion, and metastasis grading (G1: 7.18% vs. 23.46%, G2: 44.75% vs. 52.51%, G3: (TNM stage) was observed between these two groups 43.09% vs. 22.34%, and G4: 4.97% vs. 1.68%, P < 0:01, (P > 0:05, Figures 4(g)–4(i)). Finally, it was found that there
18 Oxidative Medicine and Cellular Longevity ns ns ns 2000 Value 0 −2000 Stromal_score Immune_score ESTIMATE_score Group High Low (a) Figure 10: Continued.
Oxidative Medicine and Cellular Longevity 19 M_stage N_stage 2 T_stage Group VEGFA KDR ESM1 PECAM1 FLT1 ANGPTL4 1 CD34 CD8A CD27 IFNG GZMA GZMB PRF1 EOMES CXCL9 0 CXCL10 CXCL11 CD274 CTLA4 FOXP3 TIGIT IDO1 PSMB8 PSMB9 −1 TAP1 TAP2 CXCL1 CXCL2 CXCL3 CXCL8 IL6 PTGS2 −2 M_stage N_stage T_stage Group M0 N0 T1 High M1 N1 T2 Low T3 T4 Pathway Angiogenesis Immune_and_antigen_presentation Myeloid_inflammation (b) Figure 10: Continued.
20 Oxidative Medicine and Cellular Longevity ⁎ ⁎ ns ns ns ns ns ⁎⁎ ⁎⁎⁎⁎ ns ⁎ ⁎ ⁎⁎ ⁎⁎⁎⁎ ⁎⁎ ns ns ns ns ns ns ⁎⁎ 0.6 0.4 Value 0.2 0.0 8+ ve e a g d s) elta ing d te M0 M1 M2 g d ted g hil hil ry r v sm tin stin tin a te a te a te lpe nai reg ocy mo nai op op CD i va est d res res age ge age pla v tiv tiv e l re T sin utr n 4+ i me ma h ell r act act a y( Mo ell l ac l ac ph ph ph ry l ell lar ell cel cel Eo Ne Bc CD gam Tc tor ell mo ry ell st c cro cro cro Bc licu cel cel NK tic Bc mo st c ula ell me Ma Ma Ma Ma ell NK tic fol dri me Tc reg Ma Tc dri 4+ den ell 4+ ell den Tc CD id Tc CD elo id ell elo ell My Tc My Tc Group High Low (c) Figure 10: Comparison of tumor microenvironment (TME) between the high- and low-risk groups. (a) The statistical analyses of the stromal score, immune score, and ESTIMATE score between the high- and low-risk groups. (b) Heatmap demonstrated the expression of genes related to angiogenesis (purple), immune and antigen presentation (blue), and myeloid inﬂammation (brown). (c) The analysis of 22 immune inﬁltrated cells between high- and low-risk groups. ∗∗∗∗ P < 0:0001, ∗∗∗ P < 0:001, ∗∗ P < 0:01, ∗ P < 0:05. was no signiﬁcant diﬀerence in the ratio of patients infected group: 37.75 vs. 81.67 months, P = 0:011; none-VI group: with hepatitis B, nor with hepatitis C between the high- and 55.35 vs. 83.51 months, P = 0:0018, Supplementary low-risk groups (P > 0:05, Figures 4(j) and 4(k)). Figure 1A-1B). Of note, in macro-VI group, patients with high prognosis score exhibited extremely poorer OS (15.77 3.5. Association between NMRG Prognosis Signature and VIs. vs. 48.95 months, P = 0:037, Figure 6(a)). Similarly, patients In the TCGA cohort, there were 111 patients presented with in micro-VI group having high prognosis score also had VIs (17 patients with macrovascular invasions and 94 worse OS, however, with no statistically signiﬁcant patients with microvascular invasions), and 211 patients diﬀerence (45.89 vs. 81.67 months, P = 0:15, Figure 6(b)), did not present with VIs. Further investigation for histopa- mainly owing to the limited patient number. thological subtypes found that more HCC patients with VIs were included in the high-risk group (macro-VI: 8.05% 3.6. Establishment of a Prognostic Nomogram. The multivar- vs. 2.47%, micro-VI: 33.56% vs. 24.69%, and none-VI: iate Cox regression analysis exhibited that the prognosis 58.39% vs. 72.84%, P < 0:01, Figure 5(a)). Remarkably, it score was an independent prognostic indicator for OS in was revealed that patients with VIs had the signiﬁcantly HCC patients from the TCGA cohort (Table 2) and the higher prognosis score, compared to those without VIs ROC curve analysis revealed that the NMRG signature had (Figure 5(b)), while patients with macrovascular invasions the highest sensitivity and speciﬁcity in predicting the OS had the highest prognosis score (Figure 5(c)). The survival of HCC patients, compared with clinic-related features, analysis demonstrated that patients with macro-VI pheno- including AFP, VI, histological grading, and TNM clinical type had signiﬁcantly worse OS than those without VIs stages (Figures 7(a)–7(c)). Meanwhile, the NMRG signature (median OS macro-VI vs. none-VI: 48.95 vs. 70.01 also had better sensitivity and speciﬁcity than each single months, P = 0:024), while there was no signiﬁcant diﬀer- NMRG alone in the prognosis prediction (Supplementary ence in the OS between patients with micro-VI and none- Figure 2A-2C). Subsequently, we combined three VI, neither between patients with micro-VI and macro-VI independent prognostic indexes, including the age, tumor (Figures 5(d)–5(f)). Nevertheless, it was shown that HCC metastasis status, and prognosis score to construct a patients having high prognosis score had worse OS, regard- nomogram to predict the OS of HCC patients less of whether presenting with VI or not (median OS in VI (Figure 7(d)). Each patient had an integrated score
Oxidative Medicine and Cellular Longevity 21 Treatment: Sorafenib Treatment: TACE p = 0.0066 p = 2.1e−07 7.50 12.00 Prognosis_score Prognosis_score 5.00 10.00 8.00 2.50 6.00 0.00 4.00 Responder Non−responder Non-responder Responder Responders Responders Outcome: responder Outcome: responder Outcome: non−responder Outcome: non-responder (a) (b) Treatment: Paclitaxel p < 0.0001 −2.40 Treatment: Cisplatin p < 0.0001 4.00 IC50 −2.80 3.60 IC50 −3.20 3.20 2.80 High Low High Low Group Group High-risk group High-risk group Low-risk group Low-risk group (c) (d) Figure 11: Continued.
22 Oxidative Medicine and Cellular Longevity Treatment: Doxorubicin Treatment: Gemcitabine p < 0.0001 p < 0.0001 −1.50 Treatment: Methotrexate −1.00 2.00 p < 0.0001 −1.75 −2.00 1.00 IC50 IC50 IC50 −2.00 −3.00 0.00 −2.25 −4.00 −1.00 High Low High Low High Low Group Group Group High-risk group High-risk group High-risk group Low-risk group Low-risk group Low-risk group (e) (f) (g) Treatment: Vorinostat Treatment: Bleomycin p < 0.0001 Treatment: Vinblastine p < 0.0001 p < 0.05 2.40 −4.00 2.00 2.00 −4.10 IC50 IC50 IC50 1.60 −4.20 1.00 −4.30 1.20 −4.40 High Low High Low High Low Group Group Group high-risk group High-risk group High-risk group Low-risk group Low-risk group Low-risk group (h) (i) (j) Figure 11: The evaluation of treatment responses by the novel prognosis score based on NMRG signature. (a) The treatment response prediction of the sorafenib therapy in the GSE109211 dataset. (b) The treatment response prediction of the transcatheter arterial chemoembolization (TACE) therapy in the GSE104580 dataset. (c–j) The boxplots of the evaluated IC50 for commonly used chemodrugs between the high- and low-risk groups by the analysis of cell line data from the GDSC database. ∗∗∗∗ P < 0:0001, ∗ P < 0:05. according to the prognostic parameters, and the higher the P < 0:05, Supplementary Tables 3–4), whereas a higher total score indicated a worse outcome. The calibration prevalence of CTNNB1 was presented in the low-risk chart showed that the OS probability predicted by the group (frequency: 32% vs. 21%, P < 0:05, Supplementary nomogram approximated the actual OS probability very Tables 3–4). Then, the altered events of patients between well (Figure 7(e)). The C-index of the nomogram was the high- and low-risk groups were compared (genes were 0.753 (95% CI, 0.703~0.804), and the AUC values of the excluded if their alteration event count less than 5 times nomogram were 0.82, 0.81, and 0.82 at the 1-, 3-, and 5- happened simultaneously in both groups), demonstrating year timepoints, respectively (Figure 7(f)). that the prevalence of a total of 61 genes was signiﬁcantly diﬀerent between the high- and low-risk groups (P < 0:05, 3.7. Genomic Feature Associated with the NMRG Signature. Supplementary Table 5). The result showed that 24 altered Statistical analysis displayed that there was no signiﬁcant genes, including CTNNB1, FBN1, and MT-CO3, were diﬀerence of the mutation count between the high- and signiﬁcantly prevalent in the low-risk group (P < 0:05, low-risk groups (P = 0:34, Figure 8(a)), but mutation proﬁles Figure 8(d), Supplementary Table 5), whereas 37 altered revealed that the most frequently altered genes between the genes, for instance, TP53, LRP1B, and FAT3, were high- and low-risk groups were distinct (Figures 8(b) and signiﬁcantly prevalent in the high-risk group (P < 0:05, 8(c)). HCC patients in the high-risk group had a signiﬁ- Figure 8(d), Supplementary Table 5). Subsequently, cantly higher prevalence of TP53 (frequency: 46% vs. 15%, genomic alterations of the known cancer-related signaling
Oxidative Medicine and Cellular Longevity 23 pathways, such as DNA Damage Repair (DDR), which might be highly correlated with the response of Phosphatidylinositol-3-Kinase (PI3K), and WNT signaling sorafenib therapy. Moreover, gene set enrichment analysis pathway, were further investigated. Of note, it was found revealed that the upregulated pathways of xenobiotic that WNT signaling-related gene CTNNB1 was more metabolism, oxidative phosphorylation, apoptosis, and frequently altered in the low-risk group (P < 0:05, coagulation (by HALLMARK, Supplementary Figure 5C), Figure 8(e)), but TSC2 and MTOR associated with PI3K ribosome and glycine, serine and threonine metabolism (by signaling pathway were signiﬁcantly enriched in the high- KEGG, Supplementary Figure 5D), besides, the risk group (P < 0:05, Figure 8(e)). The genomic alteration downregulated pathways of KRAS signaling_DN (by proﬁles describing the altered events in DDR, PI3K, and HALLMARK, Supplementary Figure 5E) and olfactory WNT signaling pathways were exhibited in Figures 8(f)–8(h). transduction (by KEGG, Supplementary Figure 5F) were enriched, which was associated with treatment response of 3.8. Identiﬁcation of Diﬀerential Biological Functions. Fur- sorafenib. ther analysis of DEGs revealed a total of 599 genes were sig- niﬁcantly upregulated and 487 genes were downregulated in 3.11. Treatment Response Prediction of TACE Therapy and low-risk groups (Supplementary Figure 3). Based on the Chemotherapy. Another independent cohort (GSE104580 identiﬁed DEGs, the diﬀerential molecular mechanisms dataset) of 147 HCC patients who received the treatment between two groups were further elucidated via of TACE was further employed in the present study. Of HALLMARK gene set and KEGG pathway enrichment note, it was found that HCC patients responding to TACE analyses. The HALLMARK gene set enrichment analysis therapy had markedly lower prognosis score (P < 0:0001, showed the signiﬁcant enrichment of E2F targets, G2M Figure 11(b)), further showing the robust capacity of progno- checkpoint, and Myc targets. (Figure 9(a)), while the sis score to predict treatment response. In addition, cell line KEGG pathway enrichment analysis exhibited a signiﬁcant data from the GDSC database were employed to predict the abundance of cell cycle, DNA replication, and spliceosome IC50 of commonly used chemodrugs for HCC patients from (Figure 9(b)). In addition, both HALLMARK gene set TCGA cohort, wherein six chemodrugs (cisplatin, gemcita- enrichment analysis and KEGG pathway enrichment bine, doxorubicin, methotrexate, vorinostat, and vinblastine) analysis showed that the metabolism-related pathways were exhibited signiﬁcantly lower IC50 in the high-risk group, signiﬁcantly enriched, especially for fatty acid metabolism indicating that those patients seemed to be more sensitive (Figures 9(a) and 9(b)). to the chemotherapeutic regimens containing these drugs (Figures 11(c)–11(j)). Conversely, the signiﬁcantly lower 3.9. Correlation between the NMRG Signature and Tumor estimated IC50 values in the low-risk group demonstrated Microenvironment. Notably, the stromal score, immune that patients with lower prognosis score could beneﬁt more score, and ESTIMATE score were nearly equivalent between from paclitaxel and bleomycin (Figures 11(d) and 11(h)). the high- and low-risk groups (Figure 10(a)). The gene Subsequently, the chemodrug eﬃcacy under VI stratiﬁca- expression proﬁles of angiogenesis, immune and antigen tion (macro-VI, micro-VI, or non-VI) was further evaluated. presentation, and myeloid inﬂammation signatures between The sensitivities to those investigated drugs were nearly equiv- the high- and low-risk groups demonstrated that there were alent between micro-VI and non-VI groups (Supplementary no distinct diﬀerences in these tumor microenvironment- Figure 6). However, four chemodrugs (including cisplatin, related pathways (Figure 10(b)). The CIBERSORT algorithm gemcitabine, vorinostat, and methotrexate) had signiﬁcantly analysis revealed that B cell memory, T cell follicular helper, lower IC50 in the macro-VI group (Supplementary regulatory T cells (Tregs), activated NK cells, macrophage Figure 6), while patients from micro-VI or non-VI group M0, and neutrophils were signiﬁcantly enriched in the high- seemed to be more sensitive to paclitaxel (Supplementary risk group (P < 0:05, Figure 10(c)). Besides, the low-risk group Figure 6). Furthermore, among patients presented with the had a signiﬁcant abundance of naive B cells, resting NK cells, non-VI or micro-VI phenotype, lower estimated IC50 values monocyte, and macrophage M1 (P < 0:05, Figure 10(c)). of cisplatin, vorinostat, and methotrexate were observed in the high-risk group, whereas the low-risk group had lower 3.10. The Signaling Pathways Potentially Targeted by estimated IC50 values of paclitaxel and bleomycin instead Sorafenib Therapy. An independent cohort (GSE109211), (Supplementary Figure 7 & 8). Besides, among non-VI including 67 HCC patients treated with sorafenib, was uti- patients, the lower IC50 values of gemcitabine, doxorubicin, lized to evaluate the eﬃcacy of sorafenib therapy in and vinblastine were further found in the high-risk group NMRG-risk groups. Notably, HCC patients who responded (Supplementary Figure 7). Owing to the limited number of to sorafenib had signiﬁcantly lower prognosis score macro-VI patients (N = 17), there was no signiﬁcant (P = 0:0066, Figure 11(a)). Subsequently, the speciﬁc signal- diﬀerence observed in the IC50 values of nearly all ing pathways potentially targeted by sorafenib were further investigated chemodrugs between the high- and low-risk investigated. The DEG analysis showed a total of 1399 genes groups, except bleomycin (Supplementary Figure 9). signiﬁcantly upregulated and 1547 genes downregulated in the responders (Supplementary Figure 4). By the statistical 4. Discussion analysis, the overlapping gene cluster between the low-risk and responder groups included 519 upregulated genes and A robust prognostic predictor for HCC patients is urgently 457 downregulated genes (Supplementary Figure 5A-5B), needed due to the heterogeneous outcomes of HCC patients
24 Oxidative Medicine and Cellular Longevity and the diﬃculties in the management and treatment strat- with metabolic diseases or neurological disorders [35–42]. egy selection. Evidences from preclinical research supported Further studies are merited to give deep insights on how mitochondrial dysfunction as a key factor in the pathogene- they involve in the development of HCC and whether they sis of metabolic liver disease and cancer, which further sug- could be targeted for treatment. In the present study, com- gested the development of targeting treatments for prehensive transcriptomic proﬁling of NMRGs oﬀered a mitochondrial genes as an attractive strategy to suppress deep insight for the role of mitochondria in HCC. the HCC progression . In the current study, functional Clinical association analysis demonstrated that the high enrichment analysis of DEGs between HCC tumors and prognosis score could discriminate HCC patients with normal tissue samples revealed that mitochondrial dysfunc- inferior outcomes. Furthermore, some known biomarkers tion was pivotal in the development of HCC, and aberrant such as AFP and des-carboxy prothrombin had very low expression of 35 NMRGs exerted notable inﬂuences on the sensitivity in detecting the HCC invasiveness . VI, as prognosis of HCC. By the optimal combination, a 25- an aggressive histopathological subtype of HCC, accounts NMRG signature based on their transcriptional proﬁling for nearly 25% ~50% of HCC [5, 44]. In the present study was eventually constructed with the good performance in the prognosis score of NMRG signature had the ability to predicting prognosis and diﬀerentiating patients with or diﬀerentiate HCC patients presented with or without VIs, without VIs in HCC. The clinical association analysis also especially for patients with macro-VIs. In addition, the showed that higher NMRG prognosis score was positively higher NMRG signature prognosis score indicated the correlated with advanced stages and tumor progression, poorer OS of HCC patients no matter whether patients which could help improve the management of patients with presented with macro-VIs, micro-VIs, or not. In short, HCC and provide decision-making guidance on the treat- the novel constructed NMRG signature, which was not ment selection. Moreover, the NMRG signature had rela- only a prognostic biomarker but also a VI predictor, would tively better sensitivity and speciﬁcity as an independent help clinicians and/or physicians better manage the HCC prognostic predictor compared to the traditionally clinico- patients. pathological features. The NMRG signature-based prognos- In addition to the enriched pathways of cell cycle and tic nomogram was ﬁnally constructed, with better AUC DDR which were of importance to carcinogenesis and pro- values and great potential to be applied to clinical practices. gression of tumor [45, 46], it could be conspicuously found A pan-cancer study by Yuan et al. revealed that the coex- that fatty acid metabolism was the top-ranked enriched pression networks of mitochondrial genes and their related pathway. The recent study revealed that RIPK3, playing an nuclear genes were distinct across 13 cancer types, and in important role in necroptosis, could regulate fatty acid HCC the coexpression of mitochondrial genes was highly metabolism including fatty acid oxidation in hepatocarcino- correlated with cancer-related signaling pathways, such as genesis , and the abnormal regulation of fatty acid oxida- PI3K . Besides, the enriched pathways were further tion causing the large amount of ROS promoted HCC cell found to be implicated with cell cycle, such as E2F targets, migration and invasion . Therefore, the elimination of G2/M checkpoint, MYC targets, mitotic spindle, and DDR- ROS via antioxidant drugs  and/or the blockade of fatty related pathways in multiple cancer types , consistent acid metabolism  could, as an eﬀective treatment strat- with the results of functional enrichment of DEGs between egy, suppress the HCC progression to improve the HCC the high- and low-risk groups in the present study. As prognosis, simultaneously regulating the cell cycle and/or reported previously, some certain mitochondrial-related DDR-related pathway via CDK inhibitors . Moreover, genes have been proved to be strongly associated with prog- the accumulation of ROS could induce tumor-associated nosis in certain cancer types. For example, NDUFV2, known macrophage M2 polarization in the tumor microenviron- as NADH ubiquinone oxidoreductase core subunit V2, ment of HCC , which would enhance the progression might act as a prognostic factor in uveal melanoma . of HCC . Thus, the regulation of mitochondrial respira- The aberrant expression of NADH dehydrogenase 1 alpha tion or ROS level, as a treatment strategy for HCC, also subcomplex assembly factor 1 (NDUFAF1) caused mito- could restrain the immunosuppressive activities of tumor- chondrial respiration deﬁciency, which was correlated with associated macrophages and improve the tumor microenvi- the carcinogenesis of primary pancreatic cancer . Some ronment. In the present study, the high-risk group had the other NMRGs, such as LRPPRC , DARS2 , GARS higher fraction of B cell memory, T cell follicular helper, , ATAD3 , TRMU , and PDSS1  had been and regulatory T cells (Tregs). These tumor-inﬁltrating lym- identiﬁed to be correlated with the carcinogenesis and pro- phocytes (TILs) were suggested to be related to the response gression in HCC. Moreover, the aberrant expression of of immune checkpoints such as PD-1 and PD-L1 [25, 52], so COX15 , LARS , PARS2 , MRPL3 , ISCU that the eﬃcacy of PD-1/PD-L1 inhibitors may be diﬀered , COQ7 , SPG7 , TRMT10C , and COQ6 between high- and low-risk patients. Meanwhile, in patients  were found to have certain inﬂuence on the tumor inva- from the high-risk group there was a signiﬁcantly higher sions in many other cancer types. However, in the present abundance of Tregs indicating the suppressive immunother- study, it was the ﬁrst time that the expressions of these apy in HCC as reported before , while tivozanib  and NMRGs, including HARS2, MPV17, MTFMT, C12ORF65, cystathionine β-synthase  could decrease Tregs inﬁltra- FRDA, CARS2, VARS2, and CABC1, were found to have tion. Therefore, the combined treatment of immune check- inﬂuence on the progression of HCC patients. Although point inhibitors, such as PD-1/PD-L1 inhibitors, with the some of them had already been identiﬁed to be associated antioxidant drugs and tivozanib or cystathionine β-synthase
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