Tumor heterogeneity attributes substantial challenges in determining the treatment regimen. Along with the conventional treatment, such as chemotherapy and radiotherapy, targeted therapy has greater impact in cancer management. Owing to the recent advancements in proteomics, we aimed to mine and re-interrogate the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data sets which contain deep scale, mass spectrometry (MS)-based proteomic and phosphoproteomic data sets conducted on human tumor samples. Quantitative proteomic and phosphoproteomic data sets of tumor samples were explored and downloaded from the CPTAC database for six different cancers types (breast cancer, clear cell renal cell carcinoma (CCRCC), colon cancer, lung adenocarcinoma (LUAD), ovarian cancer, and uterine corpus endometrial carcinoma (UCEC)). We identified 880 phosphopeptide signatures for differentially regulated phosphorylation sites across five cancer types (breast cancer, colon cancer, LUAD, ovarian cancer, and UCEC). We identified the cell cycle to be aberrantly activated across these cancers. The correlation of proteomic and phosphoproteomic data sets identified changes in the phosphorylation of 12 kinases with unchanged expression levels. We further investigated phosphopeptide signature across five cancer types which led to the prediction of aurora kinase A (AURKA) and kinases-serine/threonine-protein kinase Nek2 (NEK2) as the most activated kinases targets. The drug designed for these kinases could be repurposed for treatment across cancer types.

Background: The architecture of the protein-protein interaction (PPI) network in any organism relies on their gene expression signature. microRNAs (miRNAs) have recently emerged as major post transcriptional regulators that control PPI by targeting mainly untranslated regions of the gene encoding proteins. Here, we aimed to unveil the role of miRNAs in the PPI network for identifying potential molecular targets for lung adenocarcinoma (LUAD). Materials and methods: The expression profiles of miRNAs and mRNAs were collected from the NCBI Gene Expression Omnibus (GEO) database (GSE74190 and GSE116959). Abnormally expressed mRNAs from the data were appointed to construct a PPI network and hence incorporated with the miRNA−mRNA regulatory network. The miRNAs and mRNAs in this network were subjected to functional enrichment. Through the network analysis, hubs were identified and their mutation rate and probability of cooccurrence were calculated. Results: We identified 17 miRNAs and 429 mRNAs signature for differentially altered transcriptome in LUAD. The combined miRNA−mRNA regulatory network exhibited scale−free characteristics. Network analysis showed 5 miRNA (including hsa−miR−486−5p, hsa−miR−200b−5p, and hsa−miR−130b−5p) and 10 mRNA (including ASPM, CCNB1, TTN, TPX2, and BIRC5) which expressively contribute in the LUAD. We further investigated the hub genes and noticed that ASPM and TTN had the maximum rate of mutation and possessed a high tendency of cooccurrence in LUAD. Conclusion: This study provides a unique network approach to the exploration of the underlying molecular mechanism in LUAD. Identified mRNAs and miRNAs may therefore, serve as significant prognostic predictors and therapeutic targets.