Bioinformatic Analysis of Prognostic and Immune-Related Genes in Pancreatic Cancer.
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ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC8355962 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pancreatic cancer remains to have a high mortality, which is partly due to the lack of effective treatment strategies. In this study, genes with potential associations with immunophenotyping of pancreatic cancer were screened through bioinformatics analysis and the correlation between immune-related genes and the prognosis of pancreatic cancer patients was assessed. Firstly, differentially expressed immune genes were extracted from the pancreatic cancer-related datasets obtained for purposes of this study. The samples were processed by the "Consensus Cluster Plus" R package to determine the number of immune subtypes. Then, the pancreatic cancer immunophenotyping-related gene modules were determined. Differential analysis of immune gene modules was performed, and the function of genes related to pancreatic cancer immune subtypes was identified. The number of immune cells in the samples was calculated, followed by the differential expression analysis of immune cell numbers in each immune subtype of pancreatic cancer. The immune infiltration score was also estimated, and the correlation between the immune infiltration score and the patient prognosis with different immune subtypes was determined. Gene differences between each immune subtype were identified by differential expression analysis, and key immune genes affecting immunophenotyping were obtained. Following the analysis, 426 immune-related genes were identified to have potential involvement in the occurrence and development of pancreatic cancer, of which CD19 may be the most critical gene affecting the immunophenotyping of pancreatic cancer. CD19 played a significant role in the occurrence and development of IS2 and IS3 immune subtypes of pancreatic cancer through its action on B cells and T cells. Moreover, the expression of CD19 was increased in the collected pancreatic cancer tissues. Overall, our findings uncovered the critical role of CD19 in the prognosis of pancreatic cancer patients.
| S-EPMC8560963 | biostudies-literature
Project description:BackgroundThe prognosis of pancreatic adenocarcinoma (PAAD) is extremely poor and has not been improved. Thus, an effective method to assess the prognosis of patients must be established to improve their survival rate.MethodThis study investigated immune-related genes that could be used as potential therapeutic targets for PAAD. Level 3 gene expression data from the PAAD cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. For validation, other PAAD datasets (DSE62452) were downloaded from the Gene Expression Omnibus (GEO) database. The PAAD datasets from TCGA and GEO were used to screen immune-related genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping immune-related genes of the two datasets were identified. Coexpression networks of the immune-related genes were constructed.ResultsA signature of three immune-related genes (CKLF, ERAP2, and EREG) was identified in patients with PAAD. The signature could be used to divide the patients with PAAD into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of PAAD. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model based on the clinical characteristics and risk score of the TCGA PAAD dataset. The accuracy of the prognostic signature was further evaluated through functional evaluation and principal component analysis.ConclusionsThe results indicated that the signature of three immune-related genes had excellent predictive value for PAAD. These findings might help improve personalized treatment and medical decisions.
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Project description:Diabetic foot ulcer (DFU) is a complex and devastating complication of diabetes mellitus that are usually stagnant in the inflammatory phase. However, oral wound healing, which is characterized by a rapid and scarless healing process, is regarded an ideal model of wound healing. Thus, we performed a comprehensive bioinformatics analysis of the previously published data regarding oral ulcers and DFUs and found that compared to oral wound healing, the activated pathways of DFUs were enriched in cellular metabolism-related pathways but lacked the activation of inflammatory and immune-related pathways. We also found that CXCL11, DDX60, IFI44, and IFI44L were remarkable nodes since they had the most connections with other members of the module. Meanwhile, CXCL10, IRF7, and DDX58 together formed a closed-loop relationship and occupied central positions in the entire network. The real-time polymerase chain reaction and western blot was applied to validate the gene expression of the hub immune-related genes in the DFU tissues, it was found that CXCL11, IFI44, IFI44L, CXCL10 and IRF7 have a significant difference compared with normal wound tissues. Our research reveals some novel potential immune-related biomarkers and provides new insights into the molecular basis of this debilitating disease.
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Project description:BackgroundPancreatic cancer (PC) is a malignant tumor with extremely poor prognosis, exhibiting resistance to chemotherapy and immunotherapy. Nowadays, it is ranked as the third leading cause of cancer-related mortality. Glycation is a common epigenetic modification that occurs during the tumor transformation. Many studies have demonstrated a strong correlation between glycation modification and tumor progression. However, the expression status of glycosylation-related genes (GRGs) in PC and their potential roles in PC microenvironment have not been extensively investigated.MethodWe systematically integrated RNA sequencing data and clinicopathological parameters of PC patients from TCGA and GTEx databases. A GRGs risk model based on glycosylation related genes was constructed and validated in 60 patients from Pancreatic biobank via RT-PCR. R packages were used to analyze the relationships between GRGs risk scores and overall survival (OS), tumor microenvironment, immune checkpoint, chemotherapy drug sensitivity and tumor mutational load in PC patients. Panoramic analysis was performed on PC tissues. The function of B3GNT8 in PC was detected via in vitro experiments.ResultsIn this study, we found close correlations between GRGs risk model and PC patients' overall survival and tumor microenvironment. Multifaceted predictions demonstrated the low-risk cohort exhibits superior OS compared to high-risk counterparts. Meanwhile, the low-risk group was characterized by high immune infiltration and may be more sensitive to immunotherapy or chemotherapy. Panoramic analysis was further confirmed a significant relationship between the GRGs risk score and both the distribution of PC tumor cells as well as CD8 + T cell infiltration. In addition, we also identified a unique glycosylation gene B3GNT8, which could suppress PC progression in vitro and in vivo.ConclusionWe established a GRGs risk model, which could predict prognosis and immune infiltration in PC patients. This risk model may provide a new tool for PC precision treatment.
| S-EPMC10720206 | biostudies-literature
Project description:In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.
| S-EPMC6874454 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2'-5'-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.
| S-EPMC7590433 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.MethodsThe gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan-Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.ResultsFour immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs.ConclusionA total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.
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Project description:Despite the development in hepatocellular carcinoma (HCC) treatment in recent years, the therapeutic outcome of HCC remains unfavourable. This study examines the prognosis of HCC from a genetic level using clinical databases and single-cell data to identify genes with a high prognostic value. Three up-regulated genes (UBE2S, PTTG1, and CDC20) and two down-regulated genes (SOCS2 and DNASE1L3) in HCC tissues were identified. Various analyses confirmed its correlation with tumour stage (p < 0.01) and patient survival time (log-rank p < 0.001). Immune analysis, single-cell analysis, and gene set enrichment analysis (GSEA) were employed to provide insight on how they affect cancer progression, and we observed a close relation between these genes and tumour immune infiltration. Eventually, we constructed a risk score system that risk score = (0.0465) × UBE2S + (0.1851) × CDC20 + (-0.0461) × DNASE1L3 + (-0.2279) × SOCS2 (5-year area under curve = 0.706). The risk score system may serve as an effective novel prognostic system for HCC patients. This study might provide novel ideas for prognostic or therapeutic biomarkers for HCC.
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Project description:Pancreatic ductal adenocarcinoma (PDAC), as an intractable malignancy, still causes an extremely high mortality worldwide. The ubiquitin-specific protease (USP) family constitutes the major part of deubiquitinating enzymes (DUBs) which has been reported to be involved in initiation and progression of various malignancies via the function of deubiquitination. However, the biological function and clinical values of USPs in PDAC have not been comprehensively elucidated. In this study, Gene Expression Profiling Interactive Analysis (GEPIA), Gene Expression Omnibus (GEO) datasets, UALCAN database, and the Human Protein Atlas (HPA) online tool were used to analyze the expression level and the relationship between USP expression and clinicopathological features in PDAC. Survival module of HPA and Kaplan-Meier plotter (KMP) databases was recruited to explore the prognostic value of USPs. Tumor Immune Estimation Resource (TIMER) online tool and KMP databases were utilized to elucidate tumor immune infiltration and immune-related survival of USPs. CBioPortal online tool was used to identify the gene mutation level of USPs in PDAC. Both cBioPortal and LinkedOmics were used to confirm the potential biological functions of USPs in PDAC. Our study showed that USP10, USP14, USP18, USP32, USP33, and USP39 (termed as six-USPs) expressions were significantly elevated in tumor tissues. The high expression of the four USPs (USP10, USP14, USP18, and USP39) indicated a poor prognosis. A significant relationship was indicated between the expression of six-USPs and clinicopathological features. Also, the expression of six-USPs was related to promoter methylation level. Moreover, more than 40% genetic alterations and mutations were discovered in six-USPs. Furthermore, the six-USP expression was correlated with immune infiltration and immune-related prognosis. The functional analysis found that the six-USPs were involved in various biological processes and signaling pathways, such as nucleocytoplasmic transport, choline metabolism in cancer, cell cycle, ErbB signaling pathway, RIG-I-like receptor signaling pathway, TGF-β signaling pathway, and TNF signaling pathway. In conclusion, the results showed that six-USPs are potential prognostic biomarkers and can be recruited as possible therapeutic targets of PDAC.
| S-EPMC9794441 | biostudies-literature
Project description:Pancreatic cancer consists one of tumors with the highest degree of malignancy and the worst prognosis. To date, immunotherapy has become an effective means to improve the prognosis of patients with pancreatic cancer. Long non-coding RNAs (lncRNAs) have also been associated with the immune response. However, the role of immune-related lncRNAs in the immune response of pancreatic cancer remains unclear. In this study, we identified immune-related lncRNA pairs through a new combinatorial algorithm, and then clustered and deeply analyzed the immune characteristics and functional differences between subtypes. Subsequently, the prognostic model of 3 candidate lncRNA pairs was determined by multivariate COX analysis. The results showed significant prognostic differences between the C1 and C2 subtypes, which may be due to the differential infiltration of CTL and NK cells and the activation of tumor-related pathways. The prognostic model of the 3 lncRNA pairs (AC244035.1_vs._AC063926.1, AC066612.1_vs._AC090124.1, and AC244035.1_vs._LINC01885) was established, which exhibits stable and effective prognostic prediction performance. These 3 lncRNA pairs may regulate the anti-tumor effect of immune cells through ion channel pathways. In conclusion, our research demonstrated the panoramic differences in immune characteristics between subtypes and stable prognostic models, and identified new potential targets for immunotherapy.
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