Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

Li, Shiyan; Han, Fengjuan; Qi, Na; Wen, Liyang; Li, Jia; Cong, Feng; Wang, Qingling

doi:10.1186/s12957-021-02384-2

Cited by 22 publications

(16 citation statements)

References 41 publications

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“…PPIA (Cyclophilin A) is a member of the immunophilin family and acts as an immune inflammatory mediator that secretes oxidative stress-induced, which promotes the formation of foam cells by increasing the levels of ROS and pro-inflammatory cytokines. Furthermore, PPIA is involved in biological processes such as intracellular signaling, transcription, and apoptosis, therefore playing critical roles in microorganismal infections, inflammatory diseases, and tumor proliferation [32][33][34]. An increase in PPIA levels may lead to macrophage apoptosis through activation of mitochondrial death signaling pathways and caspase 3 cascade [35].…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

et al. 2022

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Background Esophageal carcinoma (ESCA) is a common malignancy with a poor prognosis. Previous research has suggested that necroptosis is involved in anti-tumor immunity and promotes oncogenesis and cancer metastasis, which in turn affects tumor prognosis. However, the role of necroptosis in ESCA is unclear. This study aimed to investigate the relationships between necroptosis-related genes (NRGs) and ESCA. Methods and results The clinical data and gene expression profiles of ESCA patients were extracted from The Cancer Genome Atlas (TCGA), and 159 NRGs were screened from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We then identified 52 differentially expressed NRGs associated with ESCA and used them for further analysis. Gene ontology (GO) and KEGG functional enrichment analyses showed that these NRGs were mostly associated with the regulation of necroptosis, Influenza A, apoptosis, NOD-like receptor, and NF-Kappa B signaling pathway. Next, univariate and multivariate Cox regression and LASSO analysis were used to identify the correlation between NRGs and the prognosis of ESCA. We constructed a prognostic model to predict the prognosis of ESCA based on SLC25A5, PPIA, and TNFRSF10B; the model classified patients into high- and low-risk subgroups based on the patient’s risk score. Furthermore, the receiver operating characteristic (ROC) curve was plotted, and the model was affirmed to perform moderately well for prognostic predictions. In addition, Gene Expression Omnibus (GEO) datasets were selected to validate the applicability and prognostic value of our predictive model. Based on different clinical variables, we compared the risk scores between the subgroups of different clinical features. We also analyzed the predictive value of this model for drug sensitivity. Moreover, Immunohistochemical (IHC) validation experiments explored that these three NRGs were expressed significantly higher in ESCA tissues than in adjacent non-tumor tissues. In addition, a significant correlation was observed between the three NRGs and immune-cell infiltration and immune checkpoints in ESCA. Conclusions In summary, we successfully constructed and validated a novel necroptosis-related signature containing three genes (SLC25A5, PPIA, and TNFRSF10B) for predicting prognosis in patients with ESCA; these three genes might also play a crucial role in the progression and immune microenvironment of ESCA.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

et al. 2022

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“…Strategies to improve patient prognosis and quality of life deserve further study. It is reported that abnormal expression of important genes and alterations of signaling pathways are associated with cancer pathogenesis [ 23 , 24 ]. For instance, Qin F et al revealed a novel angiogenesis subtype classification in squamous cell carcinoma [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…It is reported that abnormal expression of important genes and alterations of signaling pathways are associated with cancer pathogenesis [ 23 , 24 ]. For instance, Qin F et al revealed a novel angiogenesis subtype classification in squamous cell carcinoma [ 23 ]. Li S et al screened six prognostic hub genes by weighted gene co-expression network analysis (WGCNA) combined with LASSO Cox-PH analysis, which provided a new strategy for targeted therapy of cervical cancer [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

KIF14 affects cell cycle arrest and cell viability in cervical cancer by regulating the p27Kip1 pathway

et al. 2022

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Background Cervical cancer is a kind of malignant gynecological tumor. The first choice for treating cervical cancer is still a combination of surgery and chemoradiotherapy, but the 5-year survival rate remains poor. Therefore, researchers are trying to find new ways to diagnose and treat cervical cancer early. Methods The expression level of KIF14 in cells and tissues was determined via qRT–PCR. The ability of the cells to proliferate, migrate, and invade was examined using CCK-8 assay kits, colony formation assays, and Transwell chambers. The expression levels of Cyclin D1, Cyclin B1, p21, and p27 were also detected using western blot assays. Results The results suggested that p27 is a key regulatory factor in the KIF14-mediated regulation of the cell cycle. In addition, KIF14 knockdown promotes malignancy in cervical cancer cells by inhibiting p27 degradation, resulting in cell cycle arrest. Conclusions KIF14 is an oncogene in cervical cancer, and knocking down KIF14 causes cell cycle arrest by inhibiting p27 degradation, thus affecting cell viability, proliferation, and migration. These results provide a potential therapeutic target for cervical cancer.

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“…The predictive power of multivariate Cox regression analysis is better than that of univariate Cox regression analysis because univariate Cox analysis is easily affected by the bias of univariate significant variables. The predictive power of multivariate regression to establish a prognostic model is better than that of univariate Cox regression [ 38 ]. Combining the use of eight bioinformatics tools to establish the BLCA prognostic model increases model accuracy, reduces model-independent variables, and improves predictive sensitivity and depth.…”

Section: Discussionmentioning

confidence: 99%

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses

Qiu

Yin

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan–Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan–Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.

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Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

Cited by 22 publications

References 41 publications

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

Identification and validation of necroptosis-related prognostic gene signature and tumor immune microenvironment infiltration characterization in esophageal carcinoma

KIF14 affects cell cycle arrest and cell viability in cervical cancer by regulating the p27Kip1 pathway

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses

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