Identification of Biomarker and Co-Regulatory Motifs in Lung Adenocarcinoma Based on Differential Interactions

Abstract

Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

“…Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) has redox homeostasis and can be used in the treatment of lung cancer (Nishimura et al, 2019). NR3C1 is reported to be involved in the pathways related to the biological process of lung cancer, and as a gene marker has a significant correlation with the survival of LUAD (Zhao et al, 2015;Luo et al, 2018). Cathepsin L1, as a protein was encoded by the CTSL1 gene, could reduce the cellular matrix and proteolytic cascades which resulting to promote invasion or metastatic activity (Duffy, 1996;Turk et al, 2012).…”

Gene networks in cancer are biased by aneuploidies and sample impurities

“…in the Dialogue of Reverse Engineering and Assessment of Methods (DREAM) competitions 11 and many more comparisons on smaller scale [12][13][14][15][16][17][18] . They have provided many biological insights, and have been particularly useful to elucidate mechanisms of pathogenicity in human diseases such as cancer 1,2,[19][20][21][22][23][24] . However, there is a disconnect between evaluation in often relatively simple systems (synthetic networks or GRNs in E. coli and yeast) and their application to much more complex mammalian systems.…”

Integrated analysis reveals candidate genes and transcription factors in lung adenocarcinoma

“…Lung adenocarcinoma is a malignant cancer and a primary subtype of non-small cell lung cancer (NSCLC), with the greatest incidence and the worst prognosis worldwide (1). In the majority of cases the development of lung adenocarcinoma is a multifactor and multistage process, which is associated with numerous genes (2).…”

“…A previous study on the synergistic regulation of microRNAs (miRNAs) and TFs have identified a variety of significant motifs (15). In addition, a miRNA-TF synergistic regulation network has been constructed (2).…”