A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome–Interactome Signature for Predicting Non-Small Cell Lung Cancer

Abstract

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for Testing Dataset 1 (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was accessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.999, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred (https://hifzuransari.shinyapps.io/NSCLCpred/) was developed to predict NSCLC.

Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients

“…As such it is commonly used in studies in fields with large numbers of explanatory variables to reduce the variable space. The LASSO-based model has been used to diagnose and predict diseases widely [ 14 ]. The potential utilization of LASSO regression outside its usual application in the area of variable selection has been proved, especially for real-time forecasting [ 15 ].…”

Development of a new hazard scoring system in primary neuronal cell cultures for drug-induced acute neuronal toxicity identification in early drug discovery

“…To be unbiased of the type of parameters used and to match our type of neuronal culture (strain of rats, duration of culture, media, and supplement used) we used a statistical model, LASSO regression ( Tibshirani and Suo, 2016 ; Ahmed et al, 2022 ), to analyze all the parameters extracted from the MEA recordings pre- and post-compound additions. This model was used to select the most important change parameters among 43 parameters of the MEA recordings pre- and post-positive and negative controls.…”

Unravelling the Triad of Lung Cancer, Drug Resistance, and Metabolic Pathways

“…For developing effective new markers which help to detect NSCLC in early stage, a study generated a very accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting non-small cell lung cancer (NSCLC) using gene expression data and interaction networks. Using TCGA and GTEx data (The Cancer Genome Atlas Program and Genotype-Tissue Expression), the differentially expressed genes (DEGs) in NSCLC in comparison with normal tissues were discovered [98]. The above-mentioned studies show the wide application of system biology and other integrated approaches to deal with lung cancer.…”