Construction and Validation of a Novel Eight-Gene Risk Signature to Predict the Progression and Prognosis of Bladder Cancer.
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ABSTRACT:
SUBMITTER: Wang R
PROVIDER: S-EPMC8276675 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.
| S-EPMC7052873 | biostudies-literature
Project description:Background: Colorectal cancer (CRC) ranks as the third most common malignancy worldwide but a reliable prognostic biomarker of CRC is still lack. Thus, the purpose of our study was to explore whether ferroptosis - related lncRNAs could predict the prognosis of CRC. Methods: The mRNA expression profiling of colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) patients in The Cancer Genome Atlas (TCGA) database were downloaded. Univariate Cox and multivariate Cox regression analyses was used to obtain prognostic differently expressed ferroptosis-related lncRNAs (DE-FLs) and a risk signature was developed. Quantitative polymerase chain reaction (q-PCR) was used to validated the different expressions of DE-FLs. The calibration curves, C-index and the receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of nomogram. Gene set enrichment analyses (GSEA) were carried out to explore the biological mechanism between high- and low-risk group and the potential regulated pathway of prognostic DE-FLs in CRC. Results: Forty-nine DE-FLs were identified between CRC and normal tissue. Then, a 4-DE-FLs (AC016027.1, AC099850.3, ELFN1-AS1, and VPS9D1-AS1) prognostic signature model was generated. AC016027.1 was downregulated in CRC tissue; VPS9D1-AS1 and ELFN1-AS1 were upregulated by q-PCR. The model had a better accuracy presenting by 1-, 3-, and 5-years ROC curve (AUC ≥0.6), and identified survival probability (p < 0.05) well. Moreover, the risk signature could play as an independent factor of CRC (p < 0.05). Further, a nomogram including age, pathologic stage, T stage, and risk score with good prognostic capability (C-index = 0.789) was constructed. In addition, we found biological pathways mainly related to metabolism and apoptosis were down-regulated in high-risk group who with poor outcome. Finally, the functional enrichment showed prognostic DE-FLs may significantly impact bile secretion in CRC. Conclusion: A risk model and nomogram based on ferroptosis-related lncRNAs were created to predict 1-, 3-, and 5-years survival probability of CRC patients. Our data suggested that the prognostic lncRNAs could serve as valuable prognostic marker.
| S-EPMC8581609 | biostudies-literature
Project description:Uveal melanoma is the most common primary intraocular tumor with a poor prognosis. Currently, treatment for UVM is limited, and the development of drug resistance and tumor recurrence are common. Therefore, it is important to identify new prognostic biomarkers of UVM and explore their role in the tumor microenvironment. Pyroptosis is a way of cell programmed death, and related research is in full throttle. However, the role of pyroptosis in UVM is unclear. In this study, we constructed the prognosis model of pyroptosis-related genes of UVM. This model can accurately guide the prognosis of UVM, and different groups differ in immune infiltration. We further verified our results in cell experiments. To some extent, our study can provide new ideas for the diagnosis and treatment of UVM.
| S-EPMC8662541 | biostudies-literature
Project description:BackgroundIGFBP3 plays a pivotal role in carcinogenesis by being anomalously expressed in some malignancies. However, the clinical value of IGFBP3 and the role of IGFBP3-related signature in HCC remain unclear.MethodsMultiple bioinformatics methods were used to determine the expression and diagnostic values of IGFBP3. The expression level of IGFBP3 was validated by RT-qPCR and IHC. A IGFBP3-related risk score (IGRS) was built via correlation analysis and LASSO Cox regression analysis. Further analyses, including functional enrichment, immune status of risk groups were analyzed, and the role of IGRS in guiding clinical treatment was also evaluated.ResultsIGFBP3 expression was significantly downregulated in HCC. IGFBP3 expression correlated with multiple clinicopathological characteristics and demonstrated a powerful diagnostic capability for HCC. In addition, a novel IGRS signature was developed in TCGA, which exhibited good performance for prognosis prediction and its role was further validated in GSE14520. In TCGA and GSE14520, Cox analysis also confirmed that the IGRS could serve as an independent prognostic factor for HCC. Moreover, a nomogram with good accuracy for predicting the survival of HCC was further formulated. Additionally, enrichment analysis showed that the high-IGRS group was enriched in cancer-related pathways and immune-related pathways. Additionally, patients with high IGRS exhibited an immunosuppressive phenotype. Therefore, patients with low IGRS scores may benefit from immunotherapy.ConclusionsIGFBP3 can act as a new diagnostic factor for HCC. IGRS signature represents a valuable predictive tool in the prognosis prediction and therapeutic decision making for Hepatocellular Carcinoma.
| S-EPMC10312159 | biostudies-literature
Project description:Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan-Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells' roles in the gene signature's prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature's prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.
| S-EPMC8786962 | biostudies-literature
Project description:BackgroundBladder cancer is one of the most common cancers, and its histopathological type is mainly bladder urothelial carcinoma, accounting for about 90%. The prognostic biomarkers of bladder cancer are classified into clinical features biomarkers and molecular biomarkers. Nevertheless, due to the existence of individual specificity, patients with similar pathological characteristics still have great differences in the risk of disease recurrence. Therefore, it is often inaccurate to predict the survival status of patients based on clinical characteristic biomarkers, and a prognostic molecular biomarker that can grade the risk of bladder cancer patients is needed.MethodsA total of three bladder urothelial carcinoma datasets were used in this study from the Cancer Genome Atlas database and Gene Expression Omnibus database. In order to avoid overfitting, all samples were randomly divided into one training set and three validation sets, which were used to construct and test the prognostic biomarker model of bladder urothelial carcinoma. Univariate and multivariate Cox regression were used to screen candidate mRNAs and construct prognostic biomarkers model. Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the model.ResultsA prognostic biomarker model of bladder urothelial carcinoma combining with eight mRNA was constructed. Kaplan-Meier analyses indicated that a significant difference in the survival time of patients between the high-risk and the low-risk group. The area under the ROC curve were 0.632 (95% confidence interval (CI) [0.541-0.723]), 0.693 (95% CI [0.601-0.784]) and 0.686 (95% CI [0.540-0.831]) when the model was used to predict the patient's survival time in three validation datasets. The model showed high accuracy and applicability.
| S-EPMC6814068 | biostudies-literature
Project description:Telomerase allows eukaryotic cells to proliferate indefinitely, an important characteristic of tumor cells. Telomerase-related long no coding RNAs (TERLs) are involved in prognosis and drug sensitivity prediction; however, their association with bladder cancer (BLCA) is still unreported. The objective of this research is to determine a predictive prognostic TERL signature for OS and to provide an efficient treatment option for BLCA. The RNA sequence, clinical information, and mutational data of BLCA patients were acquired from The Cancer Genome Atlas (TCGA) database. With the help of the data from least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a prognostic signature was established including 14 TERLs, which could divide BLCA patients into low-risk (L-R) and high-risk (H-R) cohorts. The time-dependent receiver operating characteristic (ROC) curve demonstrated the greater predictive power of the model. By combing the TERLs-based signature and clinical risk factors (age, sex, grade, and stage), a prognostic nomogram was constructed to forecast the survival rates of patients with BLCA at 1-, 3-, and 5-years, which was well matched by calibration plots C-index and Decision curve analysis (DCA). Furthermore, the L-R cohort showed higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) than the H-R cohort, as well as substantial variability in immune cell infiltration and immune function between the two cohorts was elucidated. As for external validation, LINC01711 and RAP2C-AS1 were identified as poor prognostic factors by survival analysis from the Kaplan-Meier Plotter database, which were validated in BLCA cell lines (EJ, 253J, T24, and 5637) and SV-HUC-1 cells as the control group using qRT-PCR. In addition, interference with the expression of RAP2C-AS1 suppresses the proliferation and migration of BLCA cells, and RAP2C-AS1 could affect the expression of CD274 and CTLA4, which could serve as prognostic markers and characterize the tumor microenvironment in BLCA. Overall, the model based on the 14-TERLs signature can efficiently predict the prognosis and drug treatment response in individuals with bladder cancer.
| S-EPMC10710514 | biostudies-literature
Project description:Background Bladder cancer is the tenth most common cancer worldwide. Valuable biomarkers in the field of diagnostic bladder cancer are urgently required. Method Here, the gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA), GSE13507, GSE32894, and Mariathasan et al. Five prognostic genes were identified by the univariate, robust, and multivariate Cox's regression and were used to develop a prognosis-related model. The Kaplan–Meier survival curves and receiver operating characteristics were used to evaluate the model's effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. Subsequently, using the bladder cancer (BLCA) cell lines, the role of TNFRSF14 was determined by Western blotting, cell proliferation assay, and 5-ethynyl-20-deoxyuridine assay. Results GSDMB, CLEC2D, APOL2, TNFRSF14, and GBP2 were selected as prognostic genes in bladder cancer patients. The model's irreplaceable reliability was validated by the training and validation cohorts. CD8+ T cells were highly infiltrated in the high-TNFRSF14-expression group, and M2 macrophages were the opposite. Higher expression of TNFRSF14 was associated with higher expression levels of LCK, interferon, MHC-I, and MHC-II, while risk score was the opposite. Many compounds with higher sensitivity for treating bladder cancer patients in the low-TNFRSF14-expression group were identified, with obatoclax being a potential drug most likely to treat patients in the low-TNFRSF14-expression group. Finally, the proliferation of BLCA cell lines was increased in the TNFRSF14-reduced group, and the differential expression was identified. TNFRSF14 plays a role in bladder cancer progression through the Wnt/β-catenin-dependent pathway. TNFRSF14 is a potential protective biomarker involved in cell proliferation in BLCA. Conclusion We conducted a study to establish a 5-gene score model, providing reliable prediction for the outcome of bladder cancer patients and therapeutic drugs to individualize therapy. Our findings provide a signature that might help determine the optimal treatment for individual patients with bladder cancer.
| S-EPMC8545590 | biostudies-literature
Project description:BackgroundMetabolic abnormalities in patients with gastric adenocarcinoma lead to drug resistance and poor prognosis. Therefore, this study aimed to explore biomarkers that can predict the prognostic risk of gastric adenocarcinoma by analyzing drug metabolism-related genes.MethodsThe RNA-seq and clinical information on gastric adenocarcinoma were downloaded from the UCSC and gene expression omnibus databases. Univariate and least absolute shrinkage and selection operator regression analyses were used to identify the prognostic gene signature of gastric adenocarcinoma. The relationships between gastric adenocarcinoma prognostic risk and tumor microenvironment were assessed using CIBERSORT, EPIC, QUANTISEQ, MCPCounter, xCell, and TIMER algorithms. The potential drugs that could target the gene signatures were predicted in WebGestalt, and molecular docking analysis verified their binding stabilities.ResultsCombined with clinical information, an eight-gene signature, including GPX3, ABCA1, NNMT, NOS3, SLCO4A1, ADH4, DHRS7, and TAP1, was identified from the drug metabolism-related gene set. Based on their expressions, risk scores were calculated, and patients were divided into high- and low-risk groups, which had significant differences in survival status and immune infiltrations. Risk group was also identified as an independent prognostic factor of gastric adenocarcinoma, and the established prognostic and nomogram models exhibited excellent capacities for predicting prognosis. Finally, miconazole and niacin were predicted as potential therapeutic drugs for gastric adenocarcinoma that bond stably with NOS3 and NNMT through hydrogen interactions.ConclusionsThis study proposed a drug metabolism-related eight-gene signature as a potential biomarker to predict the gastric adenocarcinoma prognosis risks.
| S-EPMC8649372 | biostudies-literature
Project description:BackgroundBladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer.MethodsThe RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes.ResultsSORBS2, GPC2, SETBP1, FGF11, APOL1, and H1-2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer.ConclusionsIn this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.
| S-EPMC8314557 | biostudies-literature