BackgroundNovel diagnostic predictors and drug targets are needed for LUAD (lung adenocarcinoma). We aimed to build a specific SVM (support vector machine) classifier for diagnosis of LUAD and identify molecular markers with prognostic value for LUAD.MethodsThe expression differences of miRNAs, lncRNAs and mRNAs between LUAD and normal samples were compared using data from TCGA (The Cancer Genome Atlas) database. A LUAD related miRNA-lncRNA-mRNA network was constructed, based on which feature genes were selected for the construction of LUAD specific SVM classifier. The robustness and transferability of SVM classifier were validated using gene expression profile datasets GSE43458 and GSE10072. Prognostic markers were identified from the network. A set of LUAD-related differentially expressed miRNAs, lncRNAs and miRNAs were identified and a LUAD related miRNA-lncRNA-mRNA network was obtained. The LUAD specific SVM classifier constructed on the basis of the network was robust and efficient for classification of samples from TCGA dataset and two independent validation datasets.ResultsEight RNAs with prognostic value were identified, including hsa-miR-96, hsa-miR-204, PGM5P2 (phosphoglucomutase 5 pseudogene 2), SFTA1P (surfactant associated 1), RGS20 (regulator of G protein signaling 20), RGS9BP (RGS9-binding protein), FGB (fibrinogen beta chain) and INA (alpha-internexin). Among them, RGS20 and INA were regulated by hsa-miR-96. RGS20 was also regulated by hsa-miR-204, which was a potential target of SFTA1P.ConclusionThe LUAD specific SVM classifier may serve as a novel diagnostic predictor. hsa-miR-96, hsa-miR-204, PGM5P2, SFTA1P, RGS20, RGS9BP, FGB and INA may serve as prognostic markers in clinical practice.

Gastric adenocarcinoma is the most common histologic type of gastric cancer; however, the pathogenic mechanisms remain unclear. To improve mechanistic understanding and identify new treatment targets or diagnostic biomarkers, we used bioinformatic tools to predict the hub genes related to the process of gastric adenocarcinoma development from public datasets, and explored their prognostic significance. We screened differentially expressed genes between gastric adenocarcinoma and normal gastric tissues in Gene Expression Omnibus datasets (GSE79973, GSE118916, and GSE29998) using the GEO2R tool, and their functions were annotated with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses in the DAVID database. Hub genes were identified based on the protein-protein network constructed in the STRING database with Cytoscape software. A total of 10 hub genes were selected for further analysis, and their expression patterns in gastric adenocarcinoma patients were investigated using the Oncomine GEPIA database. The expression levels of ATP4A, CA9, FGA, ALDH1A1, and GHRL were reduced, whereas those of TIMP1, SPP1, CXCL8, THY1, and COL1A1 were increased in gastric adenocarcinoma. The Kaplan–Meier online plotter tool showed associations of all hub genes except for CA9 with prognosis in gastric adenocarcinoma patients; CXCL8 and ALDH1A1 were positively correlated with survival, and the other genes were negatively correlated with survival. These 10 hub genes may be involved in important processes in gastric adenocarcinoma development, providing new directions for research to clarify the role of these genes and offer insight for improved treatment.

Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors. MethodsWe enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically con rmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (Dphase), and combined radiomics algorithms were generated from the training set based on contrastenhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classi er. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% con dence interval (CI), 0.640-0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397-0.790), 0.715 (95% CI, 0.506-0.873), and 0.679 (95% CI, 0.468-0.847), respectively, with the validation set. ConclusionThe radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

Highlights Summarise the established knowledge on this subject. Middle-aged and older patients are at high risk of rectal adenocarcinoma; however, studies comprehensively analysing its predictors and the construction of visual nomogram models are limited. Most studies that reported on the prediction of colorectal cancer-related survival models had limited samples and included data from a single centre. The included predictors were limited, or the evaluation indicators were not easy to obtain, greatly limiting clinical application. With the advancement of medical care, the clinical outcomes of patients with rectal adenocarcinoma have changed. Therefore, new, more comprehensive, and practical indicators are required for constructing clinical prediction models to effectively determine the prognosis of patients. What are the significant and/or new findings of this study? We included demographic and clinicopathological data from thousands of middle-aged and elderly patients with rectal adenocarcinoma to find relevant prognostic factors. New cut-offs were developed and used for the construction of nomograms. The nomogram constructed this time has excellent predictive ability and clinical decision-making ability, and has good clinical practicability. The nomogram survival prediction model constructed this time can effectively help evaluate the prognosis of middle-aged and elderly patients with rectal adenocarcinoma and guide the selection of clinical treatment measures.

No abstract