Identification of downstream signaling cascades of ACK1 and prognostic classifiers in non-small cell lung cancer

Abstract

Activated Cdc42-associated kinase 1 ( ACK1 ) is an oncogene in multiple cancers, but the underlying mechanisms of its oncogenic role remain unclear in non-small cell lung cancer (NSCLC). Herein, we comprehensively investigated the ACK1-regulated cell processes and downstream signaling pathways, as well as its prognostic value in NSCLC. We found that ACK1 gene amplification was associated with mRNA levels in The Cancer Genome Atlas (TCGA) lung cancer cohort. The Oncomine databases showed significantly elevated ACK1 levels in lung cancer. In vitro , an ACK1 inhibitor (dasatinib) increased the sensitivity of NSCLC cell lines to AKT or MEK inhibitors. RNA-sequencing results demonstrated that an ACK1 deficiency in A549 cells affected the MAPK, PI3K/AKT, and Wnt pathways. These results were validated by gene set enrichment analysis (GSEA) of data from 188 lung cancer cell lines. Using Cytoscape, we dissected 14 critical ACK1-regulated genes. The signature with the 14 genes and ACK1 could significantly dichotomize the TCGA lung cohort regarding overall survival. The prognostic accuracy of this signature was confirmed in five independent lung cancer cohorts and was further validated by a prognostic nomogram. Our study unveiled several downstream signaling pathways for ACK1, and the proposed signature may be a promising prognostic predictor for NSCLC.

Small Molecules Targeting Activated Cdc42-Associated Kinase 1 (ACK1/TNK2) for the Treatment of Cancers

“…In NSCLC, compound 29 could significantly inhibit A549 cell proliferation and enhance the sensitivity of cancer cells to MK-2206, an AKT inhibitor, and selumetinib, a mitogen-activated protein kinase (MEK) inhibitor, and the combination of compound 29 and each of them had much better effects on cancer cell proliferation inhibition than either drug treatment alone . These findings provide scientific evidence for the combination treatment of ACK1 inhibitors and AKT or MEK inhibitors and a novel prospective treatment method for NSCLC treatment.…”

LncRNA HAR1A Suppresses the Development of Non-Small Cell Lung Cancer by Inactivating the STAT3 Pathway

“…In the end, LASSO regression yielded the best predictive signature composed of 23 genes with non-zero regression coefficients ( Figure 2 a,b; Table 1 ). A risk score was calculated for each patient based on the gene signature [ 16 , 17 , 18 ]. As shown in the receiver operating characteristic (ROC) curves, the risk score achieved a decent prediction accuracy with an area under the curve (AUC) of 0.700 in comparison to AUCs of 0.553 and 0.655 for the HAR1A and TNM stages, respectively.…”

“…We also included a GSE40419 dataset consisting of RNA-Seq data for 87 lung adenocarcinomas and 77 adjacent normal tissues. The data were processed as described before [ 16 , 17 , 18 ]. We identified 65 downregulated and 95 upregulated intergenic lncRNAs (lincRNAs) in LUAD relative to normal tissues (log2FC > 0.4 or <−0.4, adjusted p -value < 0.05).…”

“…The remaining survival genes were entered into a LASSO regression model, and genes with non-zero coefficients were used to establish a multi-gene survival model. The resulting LASSO coefficients and corresponding patient expression levels of each included gene were incorporated into a risk score algorithm to quantitatively evaluate patient risk as published previously [ 16 , 19 , 20 ]. To assess the predictive ability of the prognostic multiple gene models, time-dependent receiver operating characteristic (ROC) curves were plotted for the risk scores with the utilization of the R package survivalROC.…”

Development of immune gene pair-based signature predictive of prognosis and immunotherapy in esophageal cancer

“…Least absolute shrinkage and selection operator (LASSO) Cox regression analysis (glmnet package) was adopted to select prognostic IRGPs and build prognostic signatures (28,29). After the establishment of the IRGP signature, a risk score was assigned for each patient by linearly integrating the patient score (0 or 1) and risk contribution of each IRGP included in the prognostic signature, using a previously described formula (30)(31)(32).…”