LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer.

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Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium-calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.", "claims": [{"id": "claim-method", "statement": "To select candidate cancer-related gene combinations that were reliably associated with gynecologic cancers (cervix and endometrium), the R package glmnet (Version 4.1.2) [26] was used to fit a logistic LASSO regression model on the 78 DEGs. In our study, 10-fold cross-validation was performed for tuning parameter selection, and the partial likelihood deviance met the minimum criteria.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34834529", "locator": "sections[7] \"2.5. Feature Selection Using the LASSO Regression Model\" paragraph 2", "quote": "To select candidate cancer-related gene combinations that were reliably associated with gynecologic cancers (cervix and endometrium), the R package glmnet (Version 4.1.2) [26] was used to fit a logistic LASSO regression model on the 78 DEGs. In our study, 10-fold cross-validation was performed for tuning parameter selection, and the partial likelihood deviance met the minimum criteria."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and analyzed in this study were available from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ accessed on 3 July 2021) and The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ accessed on 3 July 2021).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34834529", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "The datasets used and analyzed in this study were available from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ accessed on 3 July 2021) and The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ accessed on 3 July 2021)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified PAMR1 and SLC24A3 as potential prognostic biomarkers for cervical and endometrial cancers.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05)."}, "evidence": [{"asset": "source:pmid:34834529", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05)."}, {"asset": "source:pmid:34834529", "locator": "sections[14] \"3.4. Verification of Prognostic Value for 20 Significant Genes\" paragraph 4", "quote": "Finally, the results suggested that PAMR1 and SLC24A3 may act as prognostic biomarkers for gynecologic cancer in our study."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified PAMR1 and SLC24A3 as potential prognostic biomarkers for cervical and endometrial cancers.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34834529"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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