Exploring the Genetic Orchestra of Cancer: The Interplay Between Oncogenes and Tumor-Suppressor Genes

Abstract

Cancer is complex because of the critical imbalance in genetic regulation as characterized by both the overexpression of oncogenes (OGs), mainly through mutations, amplifications, and translocations, and the inactivation of tumor-suppressor genes (TSGs), which entail the preservation of genomic integrity by inducing apoptosis to counter the malignant growth. Reviewing the intricate molecular interplay between OGs and TSGs draws attention to their cell cycle, apoptosis, and cancer metabolism regulation. In the present review, we discuss seminal discoveries, such as Knudson's two-hit hypothesis, which framed the field's understanding of cancer genetics, leading to the next breakthroughs with next-generation sequencing and epigenetic profiling, revealing novel insights into OG and TSG dysregulation with opportunities for targeted therapy. The key pathways, such as MAPK/ERK, PI3K/AKT/mTOR, and Wnt/β-catenin, are presented in the context of tumor progression. Importantly, we further highlighted the advances in therapeutic strategies, including inhibitors of KRAS and MYC and restoration of TSG function, despite which mechanisms of resistance and tumor heterogeneity pose daunting challenges. A high-level understanding of interactions between OG-TSGs forms the basis for effective, personalized cancer treatment-something to strive for in better clinical outcomes. This synthesis should integrate foundational biology with translation and, in this case, contribute to the ongoing effort against cancer.

Keywords: cancer genetics; carcinogenesis; cell cycle regulation; epigenetics in cancer; gene mutations; molecular pathways; oncogenes; proto-oncogene proteins; targeted cancer therapy; tumor-suppressor genes.

Figure 1

Comparison between normal cell division and malignant cell division. (a) With the support of typical proto-OGs and TSGs, a normal cell division preserves tissue homeostasis by self-renewing and supplying the differentiated cell types inside the tissue through a regulated sequence of cell cycles. (b) A malignant cell division that causes uncontrolled tumor growth when it undergoes excessive cell divisions due to dysregulated function of mutated proto-OGs and TSGs (created with www.biorender.com).

Figure 2

Major oncogenic pathways driving tumor cell proliferation. (a) The MAPK pathway is one of the signaling pathways that are triggered by activated KRAS. By switching between an active, GTP-bound state and an inactive, GDP-bound one, KRAS function is regulated. (b) When a Wnt ligand attaches to its frizzled receptor, it stops GSK3β from phosphorylating β-catenin and causing it to degrade. As β-catenin builds up in the nucleus, it binds to T-cell factor/lymphoid enhancer factor (TCF/LEF) and activates target genes of Wnt signaling, including MYC. The enhancement of MYC activation due to growth and survival factors, such as EGF, and its downstream mediators, such as MEK/MAPK/PI3K/AKT. (c) Activation of NF-κB can be achieved via several routes. A couple of these include PI3K/AKT and MAP3K. Activated MAP3K7 and PI3K/AKT both phosphorylate NF-κB, which, in turn, causes ubiquitination, proteasomal breakdown, and IκB phosphorylation. Released and translocated into the nucleus, an NF-κB dimer attaches to the κB-binding site to upregulate the expression of the Bcl-2 gene. The anti-apoptotic activity is carried out by Bcl-2. (d) Growth factor binding to EGFR results in activation of the phosphatidylinositol 3-kinase (PI3K) pathway (PI3K-AKT-mTOR) (created with www.biorender.com).

Figure 3

AI-based method for better cancer diagnosis by combining radiological imaging and histopathology examination. Initially, the medical images, such as MRI and CT scans and histology slides, are pre-processed through enhancement, noise removal, and segmentation to enhance data quality. Then, the features are extracted and analyzed by deep learning models to diagnose tumors and also determine subtypes (if any). The AI-derived insights help significantly in guiding clinical decision making and facilitating diagnosis and treatment planning. A feedback mechanism enables constant improvement of the model as per the actual clinical results, making it more accurate and responsive over time (created with www.biorender.com).