Gene signature to predict prognostic survival of hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

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Blocking siglec-10hi tumor-associated macrophages improves anti-tumor immunity and enhances immunotherapy for hepatocellular carcinoma

Experimental Hematology and Oncology ◽

10.1186/s40164-021-00230-5 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Nan Xiao ◽

Xiaodong Zhu ◽

Kangshuai Li ◽

Yifan Chen ◽

Xuefeng Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Function ◽

Cox Regression ◽

Human Tumor ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Tumor Associated Macrophages ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Background Tumor-associated macrophages (TAMs) promote key processes in the modulation of tumor microenvironment (TME). However, the clinical significance of heterogeneous subpopulations of TAMs in hepatocellular carcinoma (HCC) remains unknown. Methods HCC tissues from Zhongshan Hospital and data from The Cancer Genome Atlas were obtained and analyzed. Immunohistochemistry and flow cytometry were performed to detect the characteristics of sialic acid-binding immunoglobulin-like lectin 10high (Siglec-10hi) TAMs and explore their impact on the TME of HCC. The effect of Siglec-10 blockade was evaluated in vitro based on fresh human tumor tissues. Results Our data revealed that Siglec-10 was abundant in a large proportion of HCC specimens and prominently distributed on macrophages. Kaplan–Meier curves and Cox regression analysis showed that intratumoral Siglec-10+ cell enrichment was associated with unfavorable prognosis in patients with HCC. Notably, multiple anti-inflammatory cytokines and inhibitory receptors were enriched in Siglec-10hi TAMs. RNA sequencing data also revealed that numerous M2-like signaling pathways were significantly upregulated in Siglec-10hi TAMs. High infiltration of Siglec-10hi TAMs was associated with impaired CD8+ T cell function in HCC. Of note, blocking Siglec-10 with the competitive binding antibody Siglec-10 Fc led to decreased expression of immunosuppressive molecules and increased the cytotoxic effects of CD8+ T cells against HCC cells. Moreover, blocking Siglec-10 promoted the anti-tumor efficacy of the programmed cell death protein 1 (PD-1) inhibitor pembrolizumab. Conclusions Siglec-10hi TAMs are associated with immune suppression in the TME, and indicate poor prognosis in patients with HCC. Targeting Siglec-10hi TAMs may serve as a promising immunotherapy approach for HCC.

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Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20202870 ◽

2021 ◽

Author(s):

Xiaohan Zhou ◽

Chengdong Liu ◽

Hanyi Zeng ◽

Dehua Wu ◽

Li Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

R Package ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Pathway Enrichment Analysis ◽

Score Model ◽

Predictive Efficiency

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: lasso regression analysis followed by univariate cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package “clusterProfiler” was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by ssGSEA of R package “GSVA”. Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

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A Signature of Seven Lipid Metabolism Genes to Predict Hepatocellular Carcinoma Prognosis

10.21203/rs.3.rs-468421/v1 ◽

2021 ◽

Author(s):

Muqi Li ◽

Xiwen Wu ◽

Shufen Liao ◽

shutong wang ◽

shuirong lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Lipid Metabolism ◽

Cox Regression ◽

External Validation ◽

Metabolic Process ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Cox Regression Analysis ◽

Lipid Metabolism Genes

Abstract BackgroundLipid metabolism is important in tumor progression. However, its role in hepatocellular carcinoma (HCC) remains unknown. We attempt to build a lipid metabolism-related signature to evaluate its role in predicting the prognosis of HCC patients. MethodsWe obtained differential expression genes (DEGs) through differential analysis of mRNA expression between tumor tissues and paraneoplastic tissue of patients with HCC. The lipid metabolism-related genes were obtained from KEGG and MisDB. The corresponding gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Prognosis-related genes were obtained by COX regression analysis. Intersecting genes were defined as genes shared by DEGs and prognosis-related genes. The least absolute shrinkage and selection operator (LASSO) technique was used to calculate the prognostic genes and coefficients for forming a prognostic assessment signature. Kaplan–Meier survival analysis was applied to assess the model’s credibility. ICGC database was also used for external validation. ResultsA total of 39 lipid metabolism-related DEGs were analyzed that showed significant enrichment in the phospholipid metabolic process, glycerolipid metabolic process and glycerophospholipid pathways. Seven lipid metabolism genes (ELOVL3, LCLAT1, ME1, PPARGC1A, PTDSS2, SRD5A3, SLC2A1) closely related with prognosis were identified to construct the signature. Patients with low-risk scores showed better survival rates, which was also validated in the ICGC database. ConclusionWe established a signature composed of seven lipid metabolism-related genes to predict the prognosis of HCC patients, providing a new biomarker for the diagnosis and treatment of HCC.

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Identification of an Autophagy-related Gene Signature Predicting Overall Survival for Hepatocellular Carcinoma

Bioscience Reports ◽

10.1042/bsr20203231 ◽

2020 ◽

Author(s):

Wenfang Xu ◽

Wenke Guo ◽

Ping Lu ◽

Duan Ma ◽

Lei Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Model ◽

Cox Regression ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Cox Regression Analysis ◽

Prognostic Features ◽

Tcga Dataset

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (Hazard Ratio=2.32, 95% Confidence Interval=1.76–3.05, p<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index=0.701). In this study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.

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Identification of prognostic alternative splicing events in sarcoma

Scientific Reports ◽

10.1038/s41598-021-94485-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hongshuai Li ◽

Jie Yang ◽

Guohui Yang ◽

Jia Ren ◽

Yu Meng ◽

...

Keyword(s):

Alternative Splicing ◽

Cox Regression ◽

Univariate Analysis ◽

Area Under The Curve ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Functional Roles ◽

Cancer Pathogenesis ◽

Rare Malignancy

AbstractSarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08351-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Liang Hong ◽

Yu Zhou ◽

Xiangbang Xie ◽

Wanrui Wu ◽

Changsheng Shi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Molecular Subtypes ◽

Prognostic Significance ◽

Predictive Performance ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cumulative Distribution ◽

Gene Module ◽

Expression Levels

Abstract Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

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Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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