Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.
Background. Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC). Methods. Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas–pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. Results. We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan–Meier method ( p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart. Conclusions. We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.
Background. Hypoxia contributes to tumor progression and confers drug resistance. We attempted to microdissect the hypoxia landscape in colon cancer (CC) and explore its correlation with immunotherapy response. Materials and Methods. The hypoxia landscape in CC patients was microdissected through unsupervised clustering. The “xCell” algorithms were applied to decipher the tumor immune infiltration characteristics. A hypoxia-related index signature was developed via the LASSO (least absolute shrinkage and selection operator) Cox regression in The Cancer Genome Atlas (TCGA)-colon adenocarcinoma (COAD) cohort and validated in an independent dataset from the Gene Expression Omnibus (GEO) database. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to evaluate the correlation between the hypoxia-related index (HRI) signature and immunotherapy response. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting were performed to verify the mRNA expression levels of five key genes. The Cell Counting Kit-8 (CCK-8) assay and flow cytometry were performed to examine the cell viability and cell apoptosis. Results. Patients were classified into hypoxia-high, hypoxia-median, and hypoxia-low clusters in TCGA-COAD and verified in the GSE 17538 dataset. Compared with the hypoxia-low cluster, the hypoxia-high cluster consistently presented an unfavorable prognosis, higher immune scores, and stromal scores and elevated infiltration levels of several critical immune and stromal cells. Otherwise, we also found 600 hypoxia-related differentially expressed genes (HRDEGs) between the hypoxia-high cluster and the hypoxia-low cluster. Based on the 600 HRDEGs, we constructed the HRI signature which consists of 11 genes and shows a good prognostic value in both TCGA-COAD and GSE 17538 (AUC of 6-year survival prediction >0.75). Patients with low HRI scores were consistently predicted to be more responsive to immunotherapy. Of the 11 HRI signature genes, RGS16, SNAI1, CDR2L, FRMD5, and FSTL3 were differently expressed between tumors and adjacent tissues. Low expression of SNAI1, CDR2L, FRMD5, and FSTL3 could induce cell viability and promote tumor cell apoptosis. Conclusion. In our study, we discovered three hypoxia clusters which correlate with the clinical outcome and the tumor immune microenvironment in CC. Based on the hypoxia cluster and HRDEGs, we constructed a reliable HRI signature that could accurately predict the prognosis and immunotherapeutic responsiveness in CC patients and discovered four key genes that could affect tumor cell viability and apoptosis.
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