A Gene Signature Identifying CIN3 Regression and Cervical Cancer Survival.
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ABSTRACT:
SUBMITTER: Halle MK
PROVIDER: S-EPMC8616457 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cervical cancer mortality is the second highest in gynecological cancers. This study developed a new model based on copy number variation data and mRNA data for overall survival prediction of cervical cancer. Differentially expressed genes from The Cancer Genome Atlas dataset detected by univariate Cox regression analysis were further simplified to six by least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). The study developed a six-gene signature, which was further verified in independent dataset. Association between immune infiltration and risk score was investigated by immune score. The relation between the signature and functional pathways was examined by gene set enrichment analysis. Ninety-nine differentially expressed genes were detected, and C11orf80, FOXP3, GSN, HCCS, PGAM5, and RIBC2 were identified as key genes to construct a six-gene signature. The prognostic signature showed a significant correlation with overall survival (hazard ratio, HR = 3.45, 95% confidence interval (CI) = 2.08-5.72, p < 0.00001). Immune score showed a negative correlation with the risk score calculated by the signature (p < 0.05). Four immune-related pathways were closely associated with risk score (p < 0.0001). The six-gene prognostic signature was an effective tool to predict overall survival of cervical cancer. In conclusion, the newly identified six genes may be considered as new drug targets for cervical cancer treatment.
| S-EPMC8851535 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC.Materials and methodsIn our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups.ResultsWe identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients.ConclusionThe signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
| S-EPMC7982000 | biostudies-literature
Project description:Cervical cancer (CC) is one of the most common gynecological malignancies, ranking fourth in both incidence and mortality in women worldwide. Early screening and treatment are of great significance in reducing the incidence and mortality of CC. Due to the complex molecular mechanisms of tumor progression, the predictive power of traditional clinical information is limited. In this study, an effective molecular model is established to assess prognosis of patients with CC and guide clinical treatment so as to improve their survival rate. Three high quality datasets (GSE138080, GSE52904, GSE67522) of expression profiling were obtained from gene expression omnibus (GEO) database. Another mRNA expression and clinicopathological data of CC were obtained from The Cancer Genome Atlas (TCGA) dataset. The bioinformatic analyses such as univariate analysis, multivariate Cox proportional-hazards model (Cox) analysis and lasso regression analysis were conducted to select survival-related differentially expressed genes (DEGs) and further establish a prognostic gene signature. Moreover, the performance of prognostic gene signature was evaluated based on Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) and tumor immunity analysis were carried out to elucidate the molecular mechanisms and immune relevance. A 4-gene signature comprising procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 (PLOD2), spondin1 (SPON1), secreted phosphoprotein 1 (SPP1), ribonuclease H2 subunit A (RNASEH2A) was established to predict overall survival (OS) of CC. The ROC curve indicated good performance of the 4-gene signature in predicting OS of CC based on the TCGA dataset. The 4-gene signature classified the patients into high-risk and low-risk groups with distinct OS rates of CC. Univariate analysis and multivariate Cox regression analysis revealed that the 4-gene signature was an independent factor affecting the prognosis of patients with CC. Our study developed a 4-gene signature capable of predicting the OS of CC. The findings may be beneficial to individualized clinical treatment and timely follow-up for patients with CC.
| S-EPMC9592452 | biostudies-literature
Project description:BackgroundAlthough the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary.MethodsSamples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed.ResultsWe obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59).ConclusionOur study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.
| S-EPMC7873033 | biostudies-literature
Project description:Incomplete spontaneous regression of melanoma is common. However, complete melanoma regression is still a very rare phenomenon. Because melanoma is the most immunogenic human malignancy, the mechanisms leading to regression, based on accumulative evidence, are the host's immune responses. Unfortunately, therapies aiming to enhance the patient's natural immunity against melanoma have yet to meet their expectations. Reasons for failure include various immune escape mechanisms, induced by the tumor, that subsequently lead to tolerance. Here, we performed time-dependent gene expression profiling to unravel molecular changes involved in the transition of progressive melanoma to complete tumor regression using a porcine model. The melanoblastomabearing Libechov minipigs are highly suitable for this study because these animals exhibit naturally occurring and regressing melanomas. We were able to identify a molecular signature of the melanoma regression process. Genes regulated in this signature were associated with 1) cell cycle, 2) immune response, and 3) melanocyte differentiation. These genes may shed light on molecular mechanisms involved in complete melanoma regression and indicate what improvements are needed for successful antimelanoma therapy.
| S-EPMC2435007 | biostudies-literature
Project description:Background: 5-Methylcytidine (m5C) is the most common RNA modification and plays an important role in multiple tumors including cervical cancer (CC). We aimed to develop a novel gene signature by identifying m5C modification subtypes of CC to better predict the prognosis of patients. Methods: We obtained the expression of 13 m5C regulatory factors from The Cancer Genome Atlas (TCGA all set, 257 patients) to determine m5C modification subtypes by the "nonnegative matrix factorization" (NMF). Then the "limma" package was used to identify differentially expressed genes (DEGs) between different subtypes. According to these DEGs, we performed Cox regression and Kaplan-Meier (KM) survival analysis to establish a novel gene signature in TCGA training set (128 patients). We also verified the risk prediction effect of gene signature in TCGA test set (129 patients), TCGA all set (257 patients) and GSE44001 (300 patients). Furthermore, a nomogram including this gene signature and clinicopathological parameters was established to predict the individual survival rate. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, colony formation, migration and invasion assays. Results: Based on consistent clustering of 13 m5C-modified genes, CC was divided into two subtypes (C1 and C2) and the C1 subtype had a worse prognosis. The 4-gene signature comprising FNDC3A, VEGFA, OPN3 and CPE was constructed. In TCGA training set and three validation sets, we found the prognosis of patients in the low-risk group was much better than that in the high-risk group. A nomogram incorporating the gene signature and T stage was constructed, and the calibration plot suggested that it could accurately predict the survival rate. The expression levels of FNDC3A, VEGFA, OPN3 and CPE were all high in cervical cancer tissues. Downregulation of FNDC3A, VEGFA or CPE expression suppressed the proliferation, migration and invasion of SiHa cells. Conclusions: Two m5C modification subtypes of CC were identified and then a 4-gene signature was established, which provide new feasible methods for clinical risk assessment and targeted therapies for CC.
| S-EPMC8493221 | biostudies-literature
Project description:Aims. This study is aimed at identifying a prognostic signature for cervical cancer. Main Methods. The gene expression data and clinical information of cervical cancer and normal cervical tissues were acquired from The Cancer Genome Atlas and from three datasets of the Gene Expression Omnibus database. DESeq2 and Limma were employed to screen differentially expressed genes (DEGs). The overlapping DEGs among all datasets were considered the final DEGs. Then, the functional enrichment analysis was performed. Moreover, the Cox proportional hazards regression was performed to establish a prognostic signature of the DEGs. The Kaplan-Meier analysis was applied to test the model. Relationships between gene expression and clinicopathological parameters in cervical cancer, including age, HPV status, histology, stage, and lymph node metastasis, were analysed by the chi-square test. The somatic mutations of these prognostic genes were assessed through cBioPortal. The robustness of the model was verified in another two independent validation cohorts. Key Findings. In total, 169 overlapping upregulated genes and 29 overlapping downregulated genes were identified in cervical cancer compared with normal cervical tissues. Functional enrichment analysis indicated that the DEGs were mainly enriched in DNA replication, the cell cycle, and the p53 signalling pathway. Finally, a 5-gene- (ITM2A, DSG2, SPP1, EFNA1, and MMP1) based prognostic signature was built. According to this model, each patient was given a prognostic-related risk value. The Kaplan-Meier analysis showed that a higher risk was related to worse overall survival in cervical cancer, with an area under the receiver operating characteristic curve of 0.811 for 15 years. The validity of this model in the prediction of cervical cancer outcome was verified in another two independent datasets. In addition, our study also found that the low expression of ITM2A was associated with cervical adenocarcinoma. Interestingly, DSG2 was associated with the HPV status of cervical cancer. Significance. Our study constructed a prognostic model in cervical cancer and discovered two novel genes, ITM2A and DSG2, associated with cervical carcinogenesis and survival.
| S-EPMC7136791 | biostudies-literature
Project description:Growing evidences showed that a large number of miRNAs were abnormally expressed in cervical cancer tissues and played irreplaceable roles in tumorigenesis, progression and metastasis. The aim of the present study was to identify the differential miRNAs expression between cervical cancer and normal cervical tissues by analyzing the high-throughput miRNA data downloaded from TCGA database. Additionally, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a three-miRNA signature that could effectively predict patient survival. According to the cut-off criteria (P?<?0.05 and |log2FC|?>?2.0), a total of 78 differentially expressed miRNAs were identified between cervical cancer tissues and matched normal tissues, including 37 up-regulated miRNAs and 41 down-regulated miRNAs. The Kaplan-Meier survival method revealed the prognostic function of the three miRNAs (miRNA-145, miRNA-200c, and miRNA-218-1). Univariate and multivariate Cox regression analysis showed that the three-miRNA signature was an independent prognostic factor in cervical cancer. The functional enrichment analysis suggested that the target genes of three miRNAs may be involved in various pathways related to cancer, including MAPK, AMPK, focal adhesion, cGMP-PKG, wnt, and mTOR signaling pathway. Taken together, the present study suggested that three-miRNA signature could be used as a prognostic marker in cervical cancer.
| S-EPMC5514022 | biostudies-literature
Project description:Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling. The proposed method adopts tensor regression to accommodate high data dimensionality and the penalization technique for selection. It naturally accommodates the strong hierarchical structure without imposing additional constraints, making optimization much simpler and faster than in the existing studies. It outperforms multiple alternatives in simulation. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer and melanoma demonstrates that it can identify markers with important implications and better prediction performance.
| S-EPMC5771864 | biostudies-literature
Project description:Gene expression and DNA methylation levels affect the outcomes of patients with cancer. The present study aimed to establish a multigene risk model for predicting the outcomes of patients with cervical cancer (CerC) treated with or without radiotherapy. RNA sequencing training data with matched DNA methylation profiles were downloaded from The Cancer Genome Atlas database. Patients were divided into radiotherapy and non?radiotherapy groups according to the treatment strategy. Differently expressed and methylated genes between the two groups were identified, and 8 prognostic genes were identified using Cox regression analysis. The optimized risk model based on the 8?gene signature was defined using the Cox's proportional hazards model. Kaplan?Meier survival analysis indicated that patients with higher risk scores exhibited poorer survival compared with patients with lower risk scores (log?rank test, P=3.22x10?7). Validation using the GSE44001 gene set demonstrated that patients in the high?risk group exhibited a shorter survival time comprared with the low?risk group (log?rank test, P=3.01x10?3). The area under the receiver operating characteristic curve values for the training and validation sets were 0.951 and 0.929, respectively. Cox regression analyses indicated that recurrence and risk status were risk factors for poor outcomes in patients with CerC treated with or without radiotherapy. The present study defined that the 8?gene signature was an independent risk factor for the prognosis of patients with CerC. The 8?gene prognostic model had predictive power for CerC prognosis.
| S-EPMC6755236 | biostudies-literature