Identification of a Costimulatory Molecule-Related Signature for Predicting Prognostic Risk in Prostate Cancer.
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ABSTRACT:
SUBMITTER: Ge S
PROVIDER: S-EPMC8415313 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Costimulatory molecules can promote the activation and proliferation of T cells and play an essential role in immunotherapy. However, their role in the prognosis of colon adenocarcinoma remains elusive. In this study, the expression data of costimulatory molecules and clinicopathological information of 429 patients with colon adenocarcinoma were obtained from The Cancer Genome Atlas database. The patients were divided into training and verification cohorts. Correlation, Cox regression, and Lasso regression analyses were performed to identify costimulatory molecules related to prognosis. After mentioning the construction of the risk mode, a nomogram integrating the clinical characteristics and risk scores of patients was constructed to predict prognosis. Eventually, three prognostic costimulatory molecules were identified and used for constructing a risk model. High expression of these three molecules indicated a poor prognosis. The predictive accuracy of the risk model was verified in the GSE17536 dataset. Subsequently, multivariate regression analysis showed that the signature based on the three costimulatory molecules was an independent risk factor in the training cohort (HR = 2.12; 95% CI = 1.26, 3.56). Based on the risk model and clinicopathological data, the AUC values for predicting the 1-, 3-, and 5-year survival probability of patients with colon adenocarcinoma were 0.77, 0.77, and 0.71, respectively. To the best of our knowledge, this study is the first to report a risk signature constructed based on the costimulatory molecules TNFRSF10c, TNFRSF13c, and TNFRSF11a. This risk signature can serve as a prognostic biomarker for colon adenocarcinoma and is related to the immunotherapeutic response of patients.
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Project description:BackgroundBreast cancer (BRCA) is the most common malignant tumor in the world. Because of its substantial heterogeneity, its clinical treatment is faced with various problems. Only a small number of patients can benefit from the treatment of immune checkpoint inhibitor (ICI). Costimulatory molecule signature (CMS) plays an essential role in T cell activation and antitumor immune response. Previous studies found that CMS is associated with prognosis-related immune response markers, suggesting that CMS may be a potential therapeutic target. However, the research on their function in BRCA subtype is still inadequate. Our study aims to analyze CMS in BRCA and establish an effective prognostic model.MethodsWe extracted 1,222 messenger RNA (mRNA) samples of 1,110 patients registered in the BRCA cohort of The Cancer Genome Atlas (TCGA), including 1,109 tumor tissue mRNA samples and 113 standard tissue samples for model construction and verification. The prognostic significance was determined by least absolute shrinkage and selection operator (LASSO)-Cox proportional hazard regression, which showed that the overall survival (OS) of the high-risk group was shorter than that of the low group (P<0.01).ResultsAlthough the CMS prognostic model can predict the prognosis well, the receiver operating characteristic (ROC) prediction results were unsatisfactory. The reason for this may be the heteromorphism of BRCA, so we divided the cases into four subtypes according to the PAM50 (PAM50Call_RNAseq) in clinical information. The same method was used to construct the model in the four subtypes and verify the effect of each subtype prognostic model.ConclusionsThe results showed that the submodels constructed in this study can be used to evaluate the prognosis of each subtype.
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Project description:BackgroundCostimulatory molecules play significant roles in mounting anti-tumor immune responses, and antibodies targeting these molecules are recognized as promising adjunctive cancer immunotherapies. Here, we aim to conduct a first full-scale exploration of costimulatory molecules from the B7-CD28 and TNF families in patients with lung adenocarcinoma (LUAD) and generated a costimulatory molecule-based signature (CMS) to predict survival and response to immunotherapy.MethodsWe enrolled 1549 LUAD cases across 10 different cohorts and included 502 samples from TCGA for discovery. The validation set included 970 cases from eight different Gene Expression Omnibus (GEO) datasets and 77 frozen tumor tissues with qPCR data. The underlying mechanisms and predictive immunotherapy capabilities of the CMS were also explored.ResultsA five gene-based CMS (CD40LG, TNFRSF6B, TNFSF13, TNFRSF13C, and TNFRSF19) was initially constructed using the bioinformatics method from TCGA that classifies cases as high- vs. low-risk groups per OS. Multivariable Cox regression analysis confirmed that the CMS was an independent prognostic factor. As expected, CMS exhibited prognostic significance in the stratified cohorts and different validation cohorts. Additionally, the prognostic meta-analysis revealed that CMS was superior to the previous signature. Samples in high- and low-risk groups exhibited significantly different tumor-infiltrating leukocytes and inflammatory activities. Importantly, we found that the CMS scores were closely related to multiple immunotherapy biomarkers.ConclusionWe conducted the first and most comprehensive costimulatory molecule landscape analysis of patients with LUAD and built a clinically feasible CMS for prognosis and immunotherapy response prediction, which will be helpful for further optimize immunotherapies for cancer.
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Project description:Background: Ubiquitin and ubiquitin-like (UB/UBL) conjugations are one of the most important post-translational modifications and involve in the occurrence of cancers. However, the biological function and clinical significance of ubiquitin related genes (URGs) in prostate cancer (PCa) are still unclear. Methods: The transcriptome data and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), which was served as training cohort. The GSE21034 dataset was used to validate. The two datasets were removed batch effects and normalized using the "sva" R package. Univariate Cox, LASSO Cox, and multivariate Cox regression were performed to identify a URGs prognostic signature. Then Kaplan-Meier curve and receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of the URGs signature. Thereafter, a nomogram was constructed and evaluated. Results: A six-URGs signature was established to predict biochemical recurrence (BCR) of PCa, which included ARIH2, FBXO6, GNB4, HECW2, LZTR1 and RNF185. Kaplan-Meier curve and ROC curve analyses revealed good performance of the prognostic signature in both training cohort and validation cohort. Univariate and multivariate Cox analyses showed the signature was an independent prognostic factor for BCR of PCa in training cohort. Then a nomogram based on the URGs signature and clinicopathological factors was established and showed an accurate prediction for prognosis in PCa. Conclusion: Our study established a URGs prognostic signature and constructed a nomogram to predict the BCR of PCa. This study could help with individualized treatment and identify PCa patients with high BCR risks.
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Project description: Not available
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Project description:Background: Systemic metastasis is the main cause of death in patients with prostate cancer. It is necessary to establish a more accurate model to distinguish and predict patients with a high risk of metastasis to optimize individualized treatment. Methods: In this study, it was determined that hypoxia could affect the metastasis-free survival of patients with prostate cancer, and a hypoxia-related gene signature composed of seven genes for predicting metastasis was established and verified in different cohorts. The study further evaluated the effects of ALDOB expression on the proliferation and invasion of the LNCaP and DU145 cell lines under hypoxia and finally constructed a nomogram containing specific clinical characteristics of prostate cancer combined with the hypoxia gene signature to quantify the metastasis risk of individual patients. Results: The hypoxia-related gene signature was identified as an independent risk factor for metastasis-free survival in patients with prostate cancer. The expression of ALDOB increased under hypoxia and promoted the proliferation and invasion of LNCaP and DU145 cells. In addition, patients with a high risk score showed therapeutic resistance and immunosuppression. Compared with other parameters, the nomogram had the strongest predictive power and net clinical benefit. Conclusion: The study established a hypoxia-related gene signature and a nomogram to distinguish and predict patients with a high risk of prostate cancer metastasis, which may help to optimize individualized treatment and explore possible therapeutic targets.
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Project description:Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, ferroptosis related genes (FRGs) in prostate cancer (PCa) are not been well studied. In this study, we collected the mRNA expression profiles and clinical information of PCa patients from TCGA and MSKCC databases. The univariate, LASSO, and multivariate Cox regression analyses were performed to construct a prognostic signature. Seven FRGs, AKR1C3, ALOXE3, ATP5MC3, CARS1, MT1G, PTGS2, and TFRC, were included to establish a risk model, which was validated in the MSKCC dataset. The results showed that the high-risk group was apparently correlated with copy number alteration load, tumor burden mutation, immune cell infiltration, mRNAsi, immunotherapy, and bicalutamide response. Moreover, we found that TFRC overexpression induced the proliferation and invasion of PCa cell lines in vitro. These results demonstrate that this risk model can accurately predict prognosis, suggesting that FRGs are promising prognostic biomarkers and potential drug targets in PCa patients.
| S-EPMC8320699 | biostudies-literature
Project description:Breast cancer (BC) is the most common cancer among women worldwide and is the second leading cause of cancer-related deaths in women. Increasing evidence has validated the vital role of the immune system in BC development and recurrence. In this study, we identified an immune-related prognostic signature of BRCA that could help delineate risk scores of poor outcome for each patient. This prognostic signature comprised information on five danger genes-TSLP, BIRC5, S100B, MDK, and S100P-and three protect genes RARRES3, BLNK, and ACO1. Kaplan-Meier survival curve showed that patients classified as low-risk according to optimum cut-off risk score had better prognosis than those identified within the high-risk group. ROC analysis indicated that the identified prognostic signature had excellent diagnostic efficiency for predicting 3- and 5-years relapse-free survival (RFS). Multivariate Cox regression analysis proved that the prognostic signature is independent of other clinical parameters. Stratification analysis demonstrated that the prognostic signature can be used to predict the RFS of BC patients within the same clinical subgroup. We also developed a nomogram to predict the RFS of patients. The calibration plots exhibited outstanding performance. The validation sets (GSE21653, GSE20711, and GSE88770) were used to external validation. More convincingly, the real time RT-PCR results of clinical samples demonstrated that danger genes were significantly upregulated in BC samples, whereas protect genes were downregulated. In conclusion, we developed and validated an immune-related prognostic signature, which exhibited excellent diagnostic efficiency in predicting the recurrence of BC, and will help to make personalized treatment decisions for patients at different risk score.
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Project description:BackgroundThe tumor microenvironment (TME) has recently been proven to play a crucial role in the development and prognosis of tumors. However, the current knowledge on the potential of the TME in prostate cancer (PCa) remains scarce.PurposeThis study aims to elucidate the value of TME-related genes for PCa prognosis by integrative bioinformatics analysis.Materials and methodsWe downloaded the immune and stromal scores of PCa samples via the ESTIMATE and correlated these scores to clinicopathological characteristics and recurrence-free survival (RFS) of patients. Based on these scores, the TME-related differentially expressed genes were identified for functional enrichment analysis. Cox regression analyses were performed to identify prognostic genes and establish a predictive risk model. Moreover, gene set enrichment analysis (GSEA) was performed to evaluate the relationship between risk score and immune pathway.ResultsThe stromal and immune scores were associated with clinicopathological characteristics and RFS in PCa patients. In total, 238 intersecting differentially expressed genes were identified. Functional enrichment analysis further revealed that these genes dramatically participated in the immune-related pathways. The immune-related risk model was built with C-type lectin domain containing 7A (CLEC7A) and collagen type XI alpha 1 chain (COL11A1) using Cox regression analyses. Kaplan-Meier survival analysis showed that the expression levels of CLEC7A and COL11A1 were significantly associated with the RFS. Further, the RFS time in high-risk group was significantly shorter than that in low-risk group. The areas under the curve for the risk model in predicting 3- and 5-year RFS rates were 0.694 and 0.731, respectively. GSEA suggested that immunosuppression existed in high-risk PCa patients.ConclusionCLEC7A and COL11A1 were selected to build a predictive risk model, which may help clinicians to assess the prognosis of PCa patients and select appropriate targets for immunotherapy.
| S-EPMC8254424 | biostudies-literature
Project description:Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet. Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively. Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4+/CD8+ T cell, activated dendritic cell, and effector memory CD8+ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC. Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.
| S-EPMC8596572 | biostudies-literature