Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma
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PROVIDER: S-EPMC11334810 | biostudies-literature | 2024 Jul
REPOSITORIES: biostudies-literature
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| S-EPMC10659623 | biostudies-literature
Project description:Resistance to anoikis is a key characteristic of many cancer cells, promoting cell survival. However, the mechanism of anoikis in hepatocellular carcinoma (HCC) remains unknown. In this study, we applied differentially expressed overlapping anoikis-related genes to classify The Cancer Genome Atlas (TCGA) samples using an unsupervised cluster algorithm. Then, we employed weighted gene coexpression network analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazards regression. This model was validated using external datasets from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). Finally, we used a CIBERSORT algorithm to investigate the correlation between risk score and immune infiltration. Our results showed that the TCGA cohorts could be divided into two subgroups, with subgroup A having a lower survival probability. Five genes (BAK1, SPP1, BSG, PBK and DAP3) were identified as anoikis-related prognostic genes. Moreover, the prognostic risk model effectively predicted overall survival, which was validated using ICGC and GEO datasets. In addition, there was a strong correlation between infiltrating immune cells and prognostic genes and risk score. In conclusion, we identified anoikis-related subgroups and prognostic genes in HCC, which could be significant for understanding the molecular mechanisms and treatment of HCC.
| S-EPMC9918018 | biostudies-literature
Project description:BackgroundDysregulation of autophagy is important in the pathogenesis of many diseases, including cancer. Several aspects of the biological role of autophagy are however still unclear and the relationship between apoptosis and autophagy, particularly in the liver has yet to be thoroughly explored. In this study we evaluated the expression of Beclin 1 (one of the main autophagocytic agents, which bridges autophagy, apoptosis and both differentiation), and both pro- (Bad, Bax) and anti-apoptotic (Bcl-2, Bcl-xL) factors in liver samples from patients with different stages of liver disease.MethodsThe study concerned 93 patients from 49 cases of chronic hepatitis (CH) (30 HCV and 19 HBV-related), 13 of cirrhosis (CIRR) (10 HCV and 3 HBV-related), 21 of hepatocellular carcinoma (both HCC and peritumoral tissues [PHCC]), and 10 controls (CONTR). Real-time PCR and Western blotting were used to measure mRNA and protein expression levels.ResultsBeclin 1 mRNA levels were lower in HCC than in CH (P = 0.010) or CIRR (P = 0.011), and so were the Bcl-xL transcripts (P < 0.0001). Bad mRNA levels were higher in CH and CIRR than in CONTR, while Bax transcripts were increased in all tissues (P = 0.036). PHCC expressed the highest Bcl-2 mRNA levels. HBV-related CH tissues showed significantly higher Bcl-xL and Bad mRNA levels than HCV-related CH (P = 0.003 and P = 0.016, respectively).ConclusionsHigh Beclin 1, Bcl-xL and Bad levels in CH and CIRR tissues suggest an interaction between autophagy and apoptosis in the early and intermediate stages of viral hepatitis. In HCC these processes seem to be downregulated, probably enabling the survival and growth of neoplastic hepatocytes.
| S-EPMC3449193 | biostudies-literature
Project description:BackgroundDespite the large-scale clinical application of programmed death-ligand 1 (PD-L1) monoclonal antibody, reduction in its clinical response rate has become a gradual problem. As such, use of PD-L1 monoclonal antibody in combination with other anticarcinoma drugs has been the main strategy in improving its efficacy. Interleukin 10 (IL10) is a recognized inflammatory and immunosuppressive factor. Previous studies have suggested that there is a link between PD-L1 and IL10.ObjectiveThis study was aimed at clarifying the relationship between PD-L1 and IL10 in liver hepatocellular carcinoma (LIHC) and whether IL10 enhances the efficacy of PD-L1 inhibitor.MethodsExpression levels of PD-L1 and IL10 in carcinoma and adjacent tissues were tested by immunochemistry, Western blotting, and RT-PCR. Survival duration and follow-up data of each patient were recorded. LIHC cell lines Bel7405 and MHCC 97-H were used for in vitro experiments. Exogenous IL10 and anti-IL10 were added to cell supernatant. Expression level of PD-L1 in the LIHC cell lines was determined using Western blotting and ELISA. CCK8 and transwell assays were adopted to examine the effect of PD-L1 combined with IL10 on proliferation, invasion, and metastasis of LIHC cells.ResultsThe survival period of patients with low expression of IL10 was longer than that of patients with high expression (P = 0.01). Overexpression of PD-L1 increased the IL10 and Met levels in LIHC tissues and cell lines. IL10 downregulated the expression level of PD-L1 and enhanced the efficacy of crizotinib via the Met signaling pathway in the LIHC cells.ConclusionsA combination of IL10 and PD-L1 inhibitor holds great promise as an effective treatment for LIHC.
| S-EPMC7381951 | biostudies-literature
Project description:BackgroundExosomes are involved in cell-to-cell communication, neovascularization, cancer metastasis, and drug resistance, which all play an important role in the occurrence and progression of hepatocellular carcinoma (HCC). Because there are few mechanistic studies about the function of exosomes in HCC, the goals of this study were to identify exosome-related genes in HCC, to establish a reliable prognostic model for HCC, and to explore underlying mechanisms.MethodsThe exoRBase and The Cancer Genome Atlas (TCGA) databases were used to analyze differentially expressed genes (DEGs). Cox regression and least absolute shrinkage and selection operator analyses were used to identify DEGs closely related to the overall survival of patients with HCC. An exosome-related prognostic model was then constructed in TCGA and validated in the International Cancer Genome Consortium database. A nomogram was developed to predict survival. CIBERSORT was used to estimate the abundance of different types of immune cells. Immunotherapy-related DEGs were used to predict the effect of immunotherapy.ResultsForty-eight exosome-related DEGs were obtained; of them, five [exportin 1 (XPO1), lysosomal thiol reductase (IFI30), F-box protein 16 (FBXO16), calmodulin 1 (CALM1), MORC family CW-type zinc finger 3 (MORC3)] were selected to construct a predictive model. Patients with HCC were then divided into low- and high-risk groups using the best cut-off value, as determined by the X-tile software. Prognosis was significantly poorer in the high-risk than in the low-risk group (P=0.009; hazard ratio =2.65). Features related to exosomes were found to positively regulate immune response. Further analysis showed a higher risk score was associated with higher expression of immune checkpoint molecules, including programmed death ligand 1 (PD-L1), programmed death ligand 2 (PD-L2), T cell Ig and ITIM domain (TIGIT), and indoleamine-2,3-dioxygenase 1 (IDO1).ConclusionsThis study has identified a novel signature based on exosome-related genes that has potential as a prognostic biomarker for HCC. Our research provides an immunological perspective for the development of precision treatment for HCC.
| S-EPMC9403937 | biostudies-literature
Project description: Not available
| S-EPMC10491882 | biostudies-literature
Project description:Considering the high fatality of hepatocellular carcinoma (HCC), current prognostic systems are insufficient to accurately forecast HCC patients' outcomes. In our study, nine anoikis‑related genes (PTRH2, ITGAV, ANXA5, BIRC5, BDNF, BSG, DAP3, SKP2, and EGF) were determined to establish a risk scoring model using LASSO regression, which could be validated in ICGC dataset. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curve analysis confirmed the risk score possessed an accurate predictive value for the prognosis of HCC patients. The high-risk group showed a higher infiltration of aDCs, macrophages, T-follicular helper cells, and Th2 cells. Besides, PD-L1 was significantly higher in the high-risk group compared to the low-risk group. Several anoikis‑related genes, such as ANX5, ITGAV, BDNF and SKP2, were associated with drug sensitivity in HCC. Finally, we identified BIRC5 and SKP2 as hub genes among the nine model genes using WGCNA analysis. BIRC5 and SKP2 were over-expressed in HCC tissues, and their over-expression was associated with poor prognosis, no matter in our cohort by immunohistochemical staining or in the TCGA cohort by mRNA-Seq. In our cohort, BIRC5 expression was highly associated with the T stage, pathologic stage, histologic grade and AFP of HCC patients. In general, our anoikis-related risk model can enhance the ability to predict the survival outcomes of HCC patients and provide a feasible therapeutic strategy for immunotherapy and drug resistance in HCC. BIRC5 and SKP2 are hub genes of anoikis‑related genes in HCC.
| S-EPMC10484901 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most serious malignant tumors threatening human life with a high mortality rate. The liver regenerative capacity after hepatectomy in early-stage HCC patients is influenced by various factors, including surgical methods and energy metabolism. This study aims to provide a prognostic model based on genes related to liver regeneration that can predict the prognosis of non-tumor tissues in HCC patients.Patients and methodsA total of 584 non-tumor tissues from HCC patients were collected from three independent databases. Kaplan-Meier survival curves were used to identify prognostic liver-regeneration genes. Subsequently, a prognostic indicator, designated as the Liver Regeneration score (LR score), was determined using single-sample gene set enrichment analysis (ssGSEA). Independent cohorts were used to verify the relationship between LR score and prognosis in non-tumor tissues of HCC patients. Furthermore, a liver regeneration-related model was established to validate key genes identified through LASSO Cox regression analysis.ResultsWe constructed a gene set comprising 24 liver regeneration-related genes, and the LR score was utilized to predict the prognosis of HCC patients based on its levels in non-tumor tissues. In non-tumor tissues of HCC patients, higher LR scores were associated with improved prognosis. Higher LR scores in non-tumor tissues indicate improved liver metabolism in HCC patients, revealed by Enrichment analysis. LASSO Cox regression analysis identified two key genes, DHTKD1 (dehydrogenase E1 and transketolase domain containing 1) and PHYH (phytanoyl-CoA 2-hydroxylase), and higher expression levels of these genes in non-tumor tissues were correlated with better prognosis. The expression levels of these two genes also changed corresponding to the progression of liver regeneration.ConclusionIn summary, our study has introduced a novel LR gene signature for HCC patients, providing a predictive model for estimating clinical prognosis from non-tumor tissues. The LR score demonstrates promise as a reliable indicator for predicting overall survival in HCC.
| S-EPMC10712262 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.Materials and methodsWe downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.ResultsA total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.ConclusionThe novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
| S-EPMC10172511 | biostudies-literature
Project description:Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evaluated HCC through a non-negative matrix factorization algorithm. Through further bioinformatics analyses, we identified causes for prognostic differences between subtypes. The results illustrate a significant difference in prognosis based on immunotypes, which may stem from metabolic disorders and increased tumor invasion associated with the high expression of genes related to stem cell characteristics. In conclusion, we identified a novel HBV-related HCC immune subtype and determined its immunological characteristics, which provides ideas for further individualized immunotherapy research.
| S-EPMC7538624 | biostudies-literature