A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers

Abstract

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein–protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

Characterization of the tumour microenvironment phenotypes in malignant tissues and pleural effusion from advanced osteoblastic osteosarcoma patients

“…CLEC11A (C‐type lectin domain family 11 member A), also known as osteolectin or stem cell growth factor‐β, can promote bone formation through stimulating the differentiation of mesenchymal progenitor cells into osteoblasts 60 and maintain the adult skeleton age‐related bone loss and fracture repair 61 . CLEC11A could recruit the endothelial cells in lung cancer 62 and neutralization of CLEC11A reduced metastasis and viability of cancer stem cells in hepatocellular carcinoma 63 . A recent study performed by Sun et al 64 .…”

A Strategy Utilizing Protein–Protein Interaction Hubs for the Treatment of Cancer Diseases

“…Although this discrepancy may seem strange, it is because the PCA algorithm identifies the genes that best explain the variance between the H and L classes, while the RFC algorithm maximizes the classification power of a given tumor as belonging to either the H or L class. Additionally, when we mapped the genes listed by both PCA and RFC onto the interactome, we found that they were all close neighbors [23].…”

“…It is caused by the disruption of various pathways, including those involved in growth factor signaling, developmental pathways, such as WNT, Hedgehog, Notch, and Hippo, and other mechanisms, such as mTOR and NF-κB signaling that affect cell growth and proliferation [22]. Additionally, the interplay among these pathways in cancer must be considered [23]. For example, some tumors display interactions between the WNT pathway and other important ones, such as EGF, FGF, Hedgehog, mTOR, Notch, TGF-β, and NF-κB [24].…”

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy

“…The method of evaluating the expression of genes by reference to the population of DEGs has been used to identify up-regulated hub targets in solid tumors [10,11,[34][35][36].…”