Combating inconsistent evaluation of intra-tumor immune status by a novel transcriptomic signature in hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC9911634 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
Publications
Combating inconsistent evaluation of intra-tumor immune status by a novel transcriptomic signature in hepatocellular carcinoma.
Zhang Linmeng L Tang Ning N Yang Chen C Geng Haigang H Hang Hualian H Qin Wenxin W Wang Cun C
Signal transduction and targeted therapy 20230210 1
Similar Datasets
Project description:BackgroundHypoxia and angiogenesis, as prominent characteristics of malignant tumors, are implicated in the progression of hepatocellular carcinoma (HCC). However, the role of hypoxia in the angiogenesis of liver cancer is unclear. Therefore, we explored the regulatory mechanisms of hypoxia-related angiogenic genes (HRAGs) and the relationship between these genes and the prognosis of HCC.MethodsThe transcriptomic and clinical data of HCC samples were downloaded from public datasets, followed by identification of hypoxia- and angiogenesis-related genes in the database. A gene signature model was constructed based on univariate and multivariate Cox regression analyses, and validated in independent cohorts. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate the model's predictive capability. Gene set enrichment analysis (GSEA) was performed to explore signaling pathways regulated by the gene signature. Furthermore, the relationships among gene signature, immune status, and response to anti-angiogenesis agents and immune checkpoint blockade (ICB) were analyzed.ResultsThe prognostic model was based on three HRAGs (ANGPT2, SERPINE1 and SPP1). The model accurately predicted that low-risk patients would have longer overall survival than high-risk patients, consistent with findings in other cohorts. GSEA indicated that high-risk group membership was significantly associated with hypoxia, angiogenesis, the epithelial-mesenchymal transition, and activity in immune-related pathways. The high-risk group also had more immunosuppressive cells and higher expression of immune checkpoints such as PD-1 and PD-L1. Conversely, the low-risk group had a better response to anti-angiogenesis and ICB therapy.ConclusionsThe gene signature based on HRAGs was predictive of prognosis and provided an immunological perspective that will facilitate the development of personalized therapies.
| S-EPMC9468769 | biostudies-literature
Project description:BackgroundThis study clarified whether EMT-related genes can predict immunotherapy efficacy and overall survival in patients with HCC.MethodsThe RNA-sequencing profiles and patient information of 370 samples were derived from the Cancer Genome Atlas (TCGA) dataset, and EMT-related genes were obtained from the Molecular Signatures database. The signature model was constructed using the least absolute shrinkage and selection operator Cox regression analysis in TCGA cohort. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. Kaplan-Meier analysis and multivariate Cox analyses were employed to estimate the prognostic value. Immune status and tumor microenvironment were estimated using a single-sample gene set enrichment analysis (ssGSEA). The expression of prognostic genes was verified using qRT-PCR analysis of HCC cell lines.ResultsA signature model was constructed using EMT-related genes to determine HCC prognosis, based on which patients were divided into high-risk and low-risk groups. The risk score, as an independent factor, was related to tumor stage, grade, and immune cells infiltration. The results indicated that the most prognostic genes were highly expressed in the HCC cell lines, but GADD45B was down-regulated. Enrichment analysis suggested that immunoglobulin receptor binding and material metabolism were essential in the prognostic signature.ConclusionOur novel prognostic signature model has a vital impact on immune status and prognosis, significantly helping the decision-making related to the diagnosis and treatment of patients with HCC.
| S-EPMC9349121 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the world's malignant tumors with high morbidity and mortality. Cuproptosis is a novel form of cell death. However, the prognostic evaluation and immune relevance of cuproptosis-related genes (CRGs) in HCC are largely unknown. In our study, we constructed a prognostic model of CRGs in HCC and performed immune infiltration, functional analysis, immune checkpoint and drug sensitivity analysis. Systematically elaborated the prognostic and immune correlation of CRGs in HCC. The results showed that 15 CRGs were up-regulated or down-regulated in HCC, and the mutation frequency of CRGs reached 10.33% in HCC, with CDKN2A having the highest mutation frequency. These 19 CRGs were mainly involved in the mitochondrion, immune response and metabolic pathways. Five selected genes (CDKN2A, DLAT, DLST, GLS, PDHA1) were involved in constructing a prognostic CRGs model that enables the overall survival in HCC patients to be predicted with moderate to high accuracy. Prognostic CRGs, especially CDKN2A, the independent factor of HCC prognosis, may be closely associated with immune-cell infiltration, tumor mutation burden (TMB), microsatellite instability(MSI), and immune checkpoints. CD274, CTLA4, LAG3, PDCD1, PDCD1LG2 and SIGLEC15 may be identified as potential therapeutic targets and CD274 correlated highly with prognostic genes. Quantitative Real-Time PCR (qRT-PCR) and immunohistochemical were performed to validate the mRNA and protein expression levels of CDKN2A in adjacent normal tissues and HCC tissues, and the results were consistent with gene difference analysis. In conclusion, CRGs, especially CDKN2A, may serve as potential prognostic predictors in HCC patients and provide novel insights into cancer therapy.
| S-EPMC9795230 | biostudies-literature
Project description:BackgroundsThe pandemic of overweight and obesity (quantified by body mass index (BMI) ≥ 25) has rapidly raised the patient number of non-alcoholic fatty hepatocellular carcinoma (HCC), and several clinical trials have shown that BMI is associated with the prognosis of HCC. However, whether overweight/obesity is an independent prognostic factor is arguable, and the role of overweight/obesity-related metabolisms in the progression of HCC is scarcely known.Materials and methodsIn the present study, clinical information, mRNA expression profile, and genomic data were downloaded from The Cancer Genome Atlas (TCGA) as a training cohort (TCGA-HCC) for the identification of overweight/obesity-related transcriptome. Machine learning and the Cox regression analysis were conducted for the construction of the overweight/obesity-associated gene (OAG) signature. The Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and the Cox regression analysis were performed to assess the prognostic value of the OAG signature, which was further validated in two independent retrospective cohorts from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Subsequently, functional enrichment, genomic profiling, and tumor microenvironment (TME) evaluation were utilized to characterize biological activities associated with the OAG signature. GSE109211 and GSE104580 were retrieved to evaluate the underlying response of sorafenib and transcatheter arterial chemoembolization (TACE) treatment, respectively. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic response.ResultsOverweight/obesity-associated transcriptome was mainly involved in metabolic processes and noticeably and markedly correlated with prognosis and TME of HCC. Afterward, a novel established OAG signature (including 17 genes, namely, GAGE2D, PDE6A, GABRR1, DCAF8L1, DPYSL4, SLC6A3, MMP3, RIBC2, KCNH2, HTRA3, PDX1, ATHL1, PRTG, SHC4, C21orf29, SMIM32, and C1orf133) divided patients into high and low OAG score groups with distinct prognosis (median overall survival (OS): 24.87 vs. 83.51 months, p < 0.0001), and the values of area under ROC curve (AUC) in predicting 1-, 2-, 3-, and 4-year OS were 0.81, 0.80, 0.83, and 0.85, respectively. Moreover, the OAG score was independent of clinical features and also exhibited a good ability for prognosis prediction in the ICGC-LIHC-JP cohort and GSE54236 dataset. Expectedly, the OAG score was also highly correlated with metabolic processes, especially oxidative-related signaling pathways. Furthermore, abundant enrichment of chemokines, receptors, MHC molecules, and other immunomodulators as well as PD-L1/PD-1 expression among patients with high OAG scores indicated that they might have better responses to immunotherapy. However, probably exclusion of T cells from infiltrating tumors resulting in lower infiltration of effective T cells would restrict immunotherapeutic effects. In addition, the OAG score was significantly associated with the response of sorafenib and TACE treatment.ConclusionsOverall, this study comprehensively disclosed the relationship between BMI-guided transcriptome and HCC. Moreover, the OAG signature had the potential clinical applications in the future to promote clinical management and precision medicine of HCC.
| S-EPMC9877416 | biostudies-literature
Project description:BackgroundThe accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC.MethodsIn this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy.ResultsA total of 1009 pairs of DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients.ConclusionsThe nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients.
| S-EPMC8684648 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.
| S-EPMC8019928 | biostudies-literature
Project description:The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.
| S-EPMC10624694 | biostudies-literature
Project description:RNA modification of m6A/m5C/m1A contributes to the occurrence and development of cancer. Consequently, this study aimed to investigate the functions of m6A/m5C/m1A regulated genes in the prognosis and immune microenvironment of hepatocellular carcinoma (HCC). The expression levels of 45 m6A/m5C/m1A regulated genes in HCC tissues were determined. The functional mechanisms and protein-protein interaction network of m6A/m5C/m1A regulated genes were investigated. The Cancer Genome Atlas (TCGA) HCC gene set was categorized based on 45 m6A/m5C/m1A regulated genes, and survival analysis was used to determine the relationship between the overall survival of HCC patients in subgroups. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the risk model and nomogram for m6A/m5C/m1A regulated genes. The relationships between m6A/m5C/m1A regulated gene subsets and risk model and immune cell infiltration were analyzed using CIBERSORT. m6A/m5C/m1A regulated genes were involved in mRNA and RNA modifications, mRNA and RNA methylation, mRNA and RNA stability, and other processes. There was a statistically significant difference between cluster1 and cluster2 groups of genes regulated by m6A/m5C/m1A. The prognosis of cluster1 patients was significantly better than that of cluster2 patients. There were statistically significant differences between the two cluster groups in terms of fustat status, grade, clinical stage, and T stage of HCC patients. The risk model comprised the overexpression of YBX1, ZC3H13, YTHDF1, TRMT10C, YTHDF2, RRP8, TRMT6, LRPPRC, and IGF2BP3, which contributed to the poor prognosis of HCC patients. The high-risk score was associated with prognosis, fustat status, grade, clinical stage, T stage, and M stage and was an independent risk factor for poor prognosis in HCC patients. High-risk score mechanisms included spliceosome, RNA degradation, and DNA replication, among others, and high-risk was closely related to stromal score, CD4 memory resting T cells, M0 macrophages, M1 macrophages, resting mast cells, CD4 memory activated T cells, and follicular helper T cells. In conclusion, the cluster subgroup and risk model of m6A/m5C/m1A regulated genes were associated with the poor prognosis and immune microenvironment in HCC and are expected to be the new tools for assessing the prognosis of HCC patients.
| S-EPMC9272990 | biostudies-literature
Project description:The current understanding of the prognostic significance of natural killer (NK) cells and their tumor microenvironment (TME) in hepatocellular carcinoma (HCC) is limited. Thus, we screened for NK-cell-related genes by single-cell transcriptome data analysis and developed an NK-cell-related gene signature (NKRGS) using multi-regression analyses. Patients in the Cancer Genome Atlas cohort were stratified into high- and low-risk groups according to their median NKRGS risk scores. Overall survival between the risk groups was estimated using the Kaplan-Meier method, and a NKRGS-based nomogram was constructed. Immune infiltration profiles were compared between the risk groups. The NKRGS risk model suggests significantly worse prognoses in patients with high NKRGS risk (p < 0.05). The NKRGS-based nomogram showed good prognostic performance. The immune infiltration analysis revealed that the high-NKRGS-risk patients had significantly lower immune cell infiltration levels (p < 0.05) and were more likely to be in an immunosuppressive state. The enrichment analysis revealed that immune-related and tumor metabolism pathways highly correlated with the prognostic gene signature. In this study, a novel NKRGS was developed to stratify the prognosis of HCC patients. An immunosuppressive TME coincided with the high NKRGS risk among the HCC patients. The higher KLRB1 and DUSP10 expression levels correlated with the patients' favorable survival.
| S-EPMC10253630 | biostudies-literature
Project description:In this study, a transcriptomic group classification based on a European population is tested on a Singapore cohort. The results highlight the genotype/phenotype correlation in a Southeast Asian population. The G1-G6 transcriptomic classification derived from hepatocellular carcinoma (HCC) resected from European patients, robustly reflected group-specific clinical/pathological features. We investigated the application of this molecular classification in Southeast Asian HCC patients.Gene expression analysis was carried out on HCC surgically resected in Singapore patients who were grouped into G1-G6 transcriptomic categories according to expression of 16 predictor genes (illustrated in Supplementary Table 1, http://links.lww.com/MD/B413 and Supplementary Fig. 1, http://links.lww.com/MD/B413) using quantitative reverse transcription polymerase chain reaction (RT-PCR). Univariate and multivariate polytomous logistic regression was used to investigate association between clinical variables and pooled transcriptomic classes G12, G3, and G456.HCC from Singapore (n?=?82) were distributed (%) into G1 (13.4), G2 (24.4), G3 (15.9), G4 (24.4), G5 (14.6), and G6 (7.3) subgroups. Compared to the European data, the Singapore samples were relatively enriched in G1-G3 versus G4-G6 tumors (53.7% vs 46.3%) reflecting the higher proportion of hepatitis B virus (HBV) patients in Singapore versus Europe samples (43% vs 30%). Pooled classes were defined as G12, G3, and G456. G12 was associated with higher alpha-fetoprotein (AFP) concentrations (OR?=?1.69, 95% CI: 1.30-2.20; P?<?0.0001) and G3 with microvascular invasion (OR?=?4.91, 95% CI: 1.06-24.8; P?=?0.047).The European and Singapore cohorts were generally similar relative to associations between transcriptomic groups and clinical features. This lends credence to the G1-G6 transcriptomic classifications being applicable regardless of the ethnic origin of HCC patients. The G3 group was associated with microvascular invasion and holds potential for investigation into the underlying mechanisms and selection for therapeutic clinical trials.
| S-EPMC5134855 | biostudies-other