Prognostic Implications of Immune-Related Gene Pairs Signatures in Bladder Cancer.
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ABSTRACT:
SUBMITTER: Xiong H
PROVIDER: S-EPMC8331311 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMetastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention.MethodsA total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity.ResultsBased on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC.ConclusionIn conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.
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Project description:BackgroundThe characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer.MethodsBased on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation.ResultsIn total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts.ConclusionsThe method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.
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Project description:Background: Bladder cancer (BLCA) is the sixth most common cancer in men, with an increasing incidence of morbidity and mortality. Necroptosis is a type of programmed cell death and plays a critical role in the biological processes of bladder cancer (BLCA). However, current studies focusing on long noncoding RNA (lncRNA) and necroptosis in cancer are limited, and there is no research about necroptosis-related lncRNAs (NRLs) in BLCA. Methods: We obtained the RNA-seq data and corresponding clinical information of BLCA from The Cancer Genome Atlas (TCGA) database. The seven determined prognostic NLRs were analyzed by several methods and verified by RT-qPCR. Then, a risk signature was established based on the aforementioned prognostic NLRs. To identify it, we evaluated its prognostic value by Kaplan-Meier (K-M) survival curve and receiver operating characteristics (ROC) curve analysis. Moreover, the relationships between risk signature and clinical features, functional enrichment, immune landscape, and drug resistance were explored as well. Results: We constructed a signature based on seven defined NLRs (HMGA2-AS1, LINC02489, ETV7-AS1, EMSLR, AC005954.1, STAG3L5P-PVRIG2P-PILRB, and LINC02178). Patients in the low-risk cohort had longer survival times than those in the high-risk cohort, and the area under the ROC curve (AUC) value of risk signature was higher than other clinical variables. Functional analyses, the infiltrating level of immune cells and functions, ESTIMATE score, and immune checkpoint analysis all indicated that the high-risk group was in a relatively immune-activated state. In terms of treatments, patients in the high-risk group were more sensitive to immunotherapy, especially anti-PD1/PD-L1 immunotherapy and conventional chemotherapy. Conclusion: The novel NLR signature acts as an invaluable tool for predicting prognosis, immune microenvironment, and drug resistance in muscle-invasive bladder cancer (MIBC) patients.
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Project description:BackgroundBladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated.MethodsThe integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA.ResultsFRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.ConclusionsFRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.
| S-EPMC10765654 | biostudies-literature
Project description:In recent years, genes associated with N6-methyladenosine (m6A) modification were found to participate in modulation of multiple tumor biological processes. Concomitantly, the significantly complicated dual effects of tumor microenvironment have been observed on cancer progression. The present study aims to investigate m6A-related immune genes (m6AIGs) for their signatures and prognostic values in bladder cancer (BC). Out of 2856 differentially expressed genes (DEGs) of BC, a total of 85 genes were obtained following intersection of DEGs, immune genes and m6A-related genes. The results of multivariate Cox regression analysis illustrated four genes (BGN, GRK5, IL32, and SREBF1) were significantly associated with the prognosis of BC patients. The BC samples were divided into two types based on the consensus clustering, and the principal component analysis demonstrated a separation between them. It was found that high expression of BGN and GRK5 were linked with advanced T and N stage, and the expression of SREBF1 in early T stage was higher than that in advanced T stage. Subsequently, the nomogram to predict 3- and 5-year survival probability of BC patients was developed and calibrated. GSEA analysis for risk subgroups showed WNT and TGF-beta signaling pathways were involved in regulation of BC progression in high risk level group. In the low risk level group, cytosolic DNA-Sensing cGAS-STING and RIG-I-like receptors signaling pathways were found to be correlated with BC development. These findings provide a novel insight on studies for BC progression.
| S-EPMC8806594 | biostudies-literature
Project description:Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.
| S-EPMC7957197 | biostudies-literature
Project description:BackgroundTumor-infiltrating leukocytes can either limit cancer growth or facilitate its spread. Diagnostic strategies that comprehensively assess the functional complexity of tumor immune infiltrates could have wide-reaching clinical value. In previous work we identified distinct immune gene signatures in breast tumors that reflect the relative abundance of infiltrating immune cells and exhibited significant associations with patient outcomes. Here we hypothesized that immune gene signatures agnostic to tumor type can be identified by de novo discovery of gene clusters enriched for immunological functions and possessing internal correlation structure conserved across solid tumors from different anatomic sites.MethodsWe assembled microarray expression datasets encompassing 5,295 tumors of the breast, colon, lung, ovarian and prostate. Unsupervised clustering methods were used to determine number and composition of gene clusters within each dataset. Immune-enriched gene clusters (signatures) identified by gene ontology enrichment were analyzed for internal correlation structure and conservation across tumors then compared against expression profiles of: 1) flow-sorted leukocytes from peripheral blood and 2) >300 cancer cell lines from solid and hematologic cancers. Cox regression analysis was used to identify signatures with significant associations with clinical outcome.ResultsWe identified nine distinct immune-enriched gene signatures conserved across all five tumor types. The signatures differentiated specific leukocyte lineages with moderate discernment overall, and naturally organized into six discrete groups indicative of admixed lineages. Moreover, seven of the signatures exhibit minimal and uncorrelated expression in cancer cell lines, suggesting that these signatures derive predominantly from infiltrating immune cells. All nine immune signatures achieved statistically significant associations with patient prognosis (p<0.05) in one or more tumor types with greatest significance observed in breast and skin cancers. Several signatures indicative of myeloid lineages exhibited poor outcome associations that were most apparent in brain and colon cancers.ConclusionsThese findings suggest that tumor infiltrating immune cells can be differentiated by immune-specific gene expression patterns that quantify the relative abundance of multiple immune infiltrates across a range of solid tumor types. That these markers of immune involvement are significantly associated with patient prognosis in diverse cancers suggests their clinical utility as pan-cancer markers of tumor behavior and immune responsiveness.
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Project description:Bladder cancer (BLCA) is a common malignancy arising from the urinary bladder and therapeutic options are limited. However, the mechanisms underlying BLCA development are poorly understood. In this study, robust rank aggregation was used to integrate five GEO BLCA microarray datasets for identifying differentially expressed genes (DEGs) between non-muscular invasive BLCA and muscular invasive BLCA. One-hundred fifty-four DEGs related to the degree of BLCA infiltration, including 24 immune-related genes (IRGs), were identified. Missense mutations were the most common type in IRGs. Ten hub IRGs were identified by protein-protein interaction network analysis. Gene set enrichment analysis and gene set variation analysis of two novel BLCA-related genes (TYROBP and FCER1G) revealed that they were related to immunity. Nine survival-related IRGs were identified, and their potential regulation by transcription factors was analyzed. An immune-related gene-based prognostic index (IRGPI) comprising CTSE, CXCL10, FAM3B, MMP9, OLR1, and S100P was constructed using multivariate analysis. The reliability of the IRGPI was evaluated using independent datasets, and correlations between the IRGPI and clinicopathological characteristics, as well as the immune microenvironment, were evaluated. Finally, a nomogram was established to evaluate the prognosis of patients with BLCA. Our data provide new insights into the pathogenesis of BLCA and target genes for immunotherapy. The application of molecular markers for hierarchical prediction paves the way for precision medicine.
| S-EPMC7540134 | biostudies-literature
Project description:Few prognostic biomarkers are approved for clinical use primarily because their initial performance cannot be repeated in independent datasets. We posited that robust biomarkers could be obtained by identifying deregulated biological processes shared among tumor types having a common etiology. We performed a gene set enrichment analysis in 20 publicly available gene expression datasets comprising 1968 patients having one of the three most common tobacco-related cancers (lung, bladder, head and neck) and identified cell cycle related genes as the most consistently prognostic class of biomarkers in bladder (BL) and lung adenocarcinoma (LUAD). We also found the prognostic value of 13 of 14 published BL and LUAD signatures were dependent on cell cycle related genes, supporting the importance of cell cycle related biomarkers for prognosis. Interestingly, no prognostic gene classes were identified in squamous cell lung carcinoma or head and neck squamous cell carcinoma. Next, a specific 31 gene cell cycle proliferation (CCP) signature, previously derived in prostate tumors was evaluated and found predictive of outcome in BL and LUAD cohorts in univariate and multivariate analyses. Specifically, CCP score significantly enhanced the predictive ability of multivariate models based on standard clinical variables for progression in BL patients and survival in LUAD patients in multiple cohorts. We then generated random CCP signatures of various sizes and found sets of 10-15 genes had robust performance in these BL and LUAD cohorts, a finding that was confirmed in an independent cohort. Our work characterizes the importance of cell cycle related genes in prognostic signatures for BL and LUAD patients and identifies a specific signature likely to survive additional validation.
| S-EPMC3898982 | biostudies-literature
Project description:BackgroundBladder carcinoma (BLCA) is a common malignant tumor with high morbidity and mortality in the urinary system. Pyroptosis is a pattern of programmed cell death that is closely associated with progression of tumors. Therefore, it is significant to probe the expression of pyroptosis-related genes (PRGs) in BLCA.MethodsThe differentially expressed genes in normal and BLCA tissues were first obtained from the Cancer Genome Atlas (TCGA) database analysis, as well as PRGs from the National Center for Biotechnology Information (NCBI) database, intersecting to obtain differentially expressed pyroptosis-related genes (DEPRGs) in BLCA. With the construction of a prognostic model of pyroptosis by regression analysis, we derived and validated key genes, which were ascertained as a separate prognostic marker by individual prognostic and clinical relevance analysis. In addition, we gained six immune cells from the Tumor Immune Evaluation Resource (TIMER) website and analyzed the relationship between pyroptosis prognostic genes and immune infiltration.ResultOur results revealed that 31 DEPRGs were available by comparing normal and BLCA tissues with |log2 (fold change, FC)| > 0.5 and FDR <0.05. Four key genes (CRTAC1, GSDMB, AIM2, and FOXO3) derived from the pyroptosis prognostic model were experimentally validated for consistent expression in BLCA patients. Following risk scoring, the low-risk group of BLCA patients had noticeably higher overall survival (OS) than the high-risk group (p < 0.001). Risk score was still an independent prognostic factor (HR = 1.728, 95% CI =1.289-2.315, p < 0.001). In addition, we found remarkable correlations among the expression of pyroptosis-related prognostic genes and the immune infiltration of CD4+ T cells, CD8+ T cells, B cells, dendritic cells, macrophages, and neutrophils.ConclusionGenes (CRTAC1, GSDMB, AIM2, and FOXO3) associated with pyroptosis are potential BLCA prognostic biomarkers that act as an essential part in the predictive prognosis of survival and immunotherapy of BLCA.
| S-EPMC9280833 | biostudies-literature