A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer

Abstract

There is an unmet clinical need to identify patients with early-stage non-small cell lung cancer (NSCLC) who are likely to develop recurrence and to predict their therapeutic responses. Our previous study developed a qRT-PCR-based seven-gene microfluidic assay to predict the recurrence risk and the clinical benefits of chemotherapy. This study showed it was feasible to apply this seven-gene panel in RNA sequencing profiles of The Cancer Genome Atlas (TCGA) NSCLC patients (n = 923) in randomly partitioned feasibility-training and validation sets (p < 0.05, Kaplan–Meier analysis). Using Boolean implication networks, DNA copy number variation-mediated transcriptional regulatory network of the seven-gene signature was identified in multiple NSCLC cohorts (n = 371). The multi-omics network genes, including PD-L1, were significantly correlated with immune infiltration and drug response to 10 commonly used drugs for treating NSCLC. ZNF71 protein expression was positively correlated with epithelial markers and was negatively correlated with mesenchymal markers in NSCLC cell lines in Western blots. PI3K was identified as a relevant pathway of proliferation networks involving ZNF71 and its isoforms formulated with CRISPR-Cas9 and RNA interference (RNAi) profiles. Based on the gene expression of the multi-omics network, repositioning drugs were identified for NSCLC treatment.

Single B Cell Gene Co-Expression Networks Implicated in Prognosis, Proliferation, and Therapeutic Responses in Non-Small Cell Lung Cancer Bulk Tumors

“…The Boolean implication network algorithm used in this study was developed by Guo et al [42,60,61]. A detailed description of the algorithm and its applications in multiomics network modeling was provided in our previous publications [43,44]. This algorithm was used to construct single-cell gene co-expression networks in the present study.…”

“…IC 50 and ln(IC 50 ) were the main metrics used in this analysis. The available CCLE NSCLC cell lines were divided into three groups based on the drug activity measurements in the GDSC1/2 datasets, namely sensitive, resistant, and intermediate, as described previously [43,44].…”

“…CD27 is a new ICI [75] that is being investigated in phase I/II clinical studies for a variety of cancer types, with encouraging results [76,77]. CD27 was also included in a seven-gene prognostic and chemopredictive assay we identified previously, showing concordant prognostic indications at the mRNA and protein expression levels in NSCLC tumors [44,78]. Here, we sought to examine the expression of these ICIs in NSCLC tumor B cells, T cells, and PBL T cells in single-cell RNA sequencing profiles.…”

“…The genes in blue font are drug-sensitive genes that expressed higher in sensitive cell lines; the genes in red font are drug-resistant genes that expressed higher in resistant cell lines. We previously developed a seven-gene prognostic and predictive assay for early-stage NSCLC (including ABCC4, CCL19, SLC39A8, CD27, FUT7, DAG1, and ZNF71) [44,78]. ZNF71 protein and its KRAB isoform gene expression are associated with epithelial to mesenchymal transition (EMT) [44,79].…”

“…Rigorous pathway and network approaches are crucial for the discovery of innovative targeted therapies and repurposing drugs to prolong NSCLC survival [41]. This study utilized Boolean implication networks [42][43][44] to identify tumor-specific B cell gene co-expression networks in NSCLC using public single-cell RNA sequencing data. Within the identified B cell networks, proliferation genes were identified from CRISPR-Case9/RNA interference (RNAi) screening assays in human NSCLC cell lines.…”