Prognostic significance of CDC25C in lung adenocarcinoma: An analysis of TCGA data

Liu

et al. 2021

Front. Cell Dev. Biol.

Aberrant regulation of angiogenesis involves in the growth and metastasis of tumors, but angiogenesis inhibitors fail to improve overall survival of pancreatic cancer patients in previous phase III clinical trials. A comprehensive knowledge of the mechanism of angiogenesis inhibitors against pancreatic cancer is helpful for clinical purpose and for the selection of patients who might benefit from the inhibitors. In this work, multi-omics analyses (transcriptomics, proteomics, and phosphoproteomics profiling) were carried out to delineate the mechanism of anlotinib, a novel angiogenesis inhibitor, against pancreatic cancer cells. The results showed that anlotinib exerted noteworthy cytotoxicity on pancreatic cancer cells. Multi-omics analyses revealed that anlotinib had a profound inhibitory effect on ribosome, and regulated cell cycle, RNA metabolism and lysosome. Based on the multi-omics results and available data deposited in public databases, an anlotinib-related gene signature was further constructed to identify a subgroup of pancreatic cancer patients who had a dismal prognosis and might be responsive to anlotinib.

Section: Discussionmentioning

confidence: 99%

Multi-Omics Analysis of Anlotinib in Pancreatic Cancer and Development of an Anlotinib-Related Prognostic Signature

Liu

et al. 2021

Front. Cell Dev. Biol.

“…There were 445 LUAD patients, including their clinical follow-up information, who were taken into consideration. We chose patients whose survival data >30 days in order to improve the accuracy of our study ( 17 ). Patients whose survival data <30 days may die of other diseases rather than LUAD.…”

Section: Methodsmentioning

confidence: 99%

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Liu

et al. 2020

Front. Oncol.

Background Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD). Methods RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method. Results A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance ( P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis. Conclusions We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

“…2A. For instance, the most signi cant GO term, cell division, has been reported in multiple articles related to the progression and metastasis of cancer [15][16][17]. We also found that DEGs were signi cantly enriched in angiogenesis, which is a core hallmark of advanced cancers, especially in LUAD [18][19][20].…”

Section: Functional Enrichment Of Dmgsmentioning

confidence: 54%

A Methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

Wang

Zhou

Xia

et al. 2020

Preprint

Background DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients. Method: The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated expression correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves. Result We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47–3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19–3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively. Conclusions The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.