Computational oncology advances multi-targeted therapies for Colon Cancer (CC) by leveraging molecular data and identifying potential drug candidates. However, challenges persist in understanding CC molecular pathways and identifying essential genes. This research integrates biomarker signatures from high-dimensional gene expression, mutation data, and protein interaction networks. The research study employs Adaptive Bacterial Foraging (ABF) optimization to refine search parameters, maximizing the predictive accuracy of therapeutic outcomes. The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. The ABF-CatBoost integration facilitates a multi-targeted therapeutic approach, addressing drug resistance by analyzing mutation patterns, adaptive resistance mechanisms, and conserved binding sites. External validation datasets assess predictive accuracy and generalizability. The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). The model predicts toxicity risks, metabolism pathways, and drug efficacy profiles, ensuring safer and more effective treatments. The artificial intelligence model personalizes therapy by leveraging patient-specific molecular profiles, optimizing drug selection and dosage while minimizing side effects. By altering the biomarker selection and pathway analysis components, this computational framework is modified for other cancers, expanding its application and impact in personalized cancer treatment. It also improves precision medicine in CC therapy, speeding up drug discovery and improving therapeutic outcomes.
One of the most often diagnosed diseases and the second leading cause of cancer-related deaths globally is colon cancer. An estimated 1.93 million new CC patients were discovered in 2020, making up 10% of all cancer cases globally. The increasing number of CC reports worldwide is linked to efficient screening and monitoring programs that are widely and quickly implemented. However, the rate of CC morbidity remained high, with 0.94 million deaths from the disease recorded in 2020 -- or accounting for 9.4% of all cancer-related fatalities worldwide; although effective screenings and CC reduction initiatives have led to a general increase in detection, a rise in CC prevalence diagnosis has been observed in developing or emerging nations, as well as a younger population (below 50 years old) starting CC in industrialized nations. The Guaiac FOBT and intestinal inflammatory examination are combined in the Fecal Occult Blood Test (FOBT), a crucial CC screening tool. Colonoscopy is regarded as the pattern procedure for CC selection, with benefits such as high specificity, sensitivity, and absolute accessibility, and it gives an essential function in Tumor and Malignant injuries. This comprises endorectal ultrasonography, Computed Tomography (CT), abdominal ultrasonography, and Nuclear Magnetic Resonance (NMR). Nevertheless, these approaches are only successful for severe localized lesions. Cancer indicators have become increasingly utilized in the analysis and cure of cancer. The marker must have high accuracy for tumor selection, analysis, efficiency and prediction evaluation, reappearance recognition, and other applications, as well as the ability to perceive tiny lesions and quantitatively reproduce them. Cancer performance denotes the grade of cancer that has gone across the body. It aids in determining the severity of cancer and the finest treatment options, and doctors also utilize it in survival statistics. Cancer is characterized into five grades: 0, I, II, III, and IV. The cancer grade determines the position and dimension of cancer, how much it has developed in neighboring matter, and if it has reached nearby humor nodules of the body, as well as the occurrence of cancer-reaching indicators. Among individuals with the lowest status of CC, if discovered at phase I, the 5-year endurance value for individuals aged 18 to 65 is 91% and is feasible with adequate therapy. Integrating biomarkers next to imaging modalities considerably improves the precision of identifying CC liver metastasis. Despite improvements in prognostic assessment like Carcinoembryonic Antigen (CEA) and Cancer Antigen 125 (CA125), and screening techniques like colonoscopy, tumor heterogeneity, and low biomarker sensitivity still hinder early identification and prognosis. While serum indicators like CEA are useful in diagnosing CC, their restrictions reduce their effectiveness in detecting hepatic metastases. Thus, it is critical to investigate new biomarkers to develop analytical exactness and medical effects in patients. Cancer screening employs a variety of biomarkers like Deoxyribo Nucleic Acid (DNA), protein, and Ribo Nucleic Acid (RNA) biomarkers. Transcriptional biomarkers are a hopeful kind of biomarker that determines changes in the quantities of RNA particles created from DNA in groups, such as mRNAs, micro RNAs, extensive hypervariable RNAs, and globular RNAs. They are non-intrusive and extremely receptive, creating an excellent apparatus for untimely identification and observing numerous malignancies. Cancer beginning, expansion, and metastasis are all impacted by intricate transcriptome changes. The beginning and development of CC are significantly influenced by transcriptomic and epigenomic changes, and large-scale molecular profiling has been made possible by high-performing equipment like microarrays and Next-Generation Sequencing (NGS). These datasets, which are kept in databases like TCGA and GEO, make it easier to find biomarkers and do computer modeling. Transcriptome data processing is multifaceted and prolonged, necessitating bioinformatics and statistical knowledge. Conventional approaches for interpreting transcriptome information rely on physical search as well as understanding and are costly and unsuitable for dealing with the massive quantities of information produced by current sequencing equipment. The research's goal is to generate a computational oncology framework that integrates high-dimensional molecular data to identify multi-targeted therapeutic strategies for CC. Scalable methods for obtaining predictive characteristics from high-dimensional data are provided by recent advancements in bioinformatics and Machine Learning (ML). Due to noise and data imbalance, problems with feature selection, parameter tweaking, and precise classification still exist. The research aims to enhance biomarker discovery, improve drug response prediction, and personalize treatment plans. The proposed method can deliver quick and precise evaluations that would enable doctors to rank patients by of importance, expedite triage procedures, and potentially cut down on diagnostic errors and delays. It is also a useful addition to concurrent hospital operations because the method design facilitates easy integration and requires little training for clinical staff.
Research on biomarkers and predictive models is being conducted to improve diagnosis and treatment results for CC, a critical health challenge. Recent research has used a variety of ML and bioinformatics tools to uncover possible biomarkers and create predictive models for CRC.
Liñares-Blanco et al. confirmed a meta-signature using ML methods and molecular docking to corroborate the relations of FABP6 with abemaciclib. However, their validation was restricted to in-silico approaches. Similarly, Kong et al. used 3D organoid data to establish biomarkers that accurately predicted treatment responses in colorectal and bladder tumors, but their findings were limited to preclinical validation. Shuwen et al. used CatBoost to obtain 99% accuracy and an Area Under Curve (AUC) of 1.0 on GSE131418 for diagnosing Colon Adenocarcinoma (COAD) liver metastases, despite limited external validation. Liu et al. found five transcription factors and validated their model across four GEO datasets, achieving good survival prediction accuracy; however, further clinical validation was required. Jin et al. used SVM-RFE and Cox regression to identify six lncRNAs for predicting COAD recurrence; still, their model was constrained by dataset reliance. Wang et al. used bioinformatics to identify eleven hub genes for colorectal cancer, which need to be validated experimentally. Sun et al. used Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analysis to identify four influential genes and two medicines, but did not conduct any experimental testing. Fang et al. identified twelve prognostic genes with a model AUC greater than 0.8 and created a nomogram, albeit only using retrospective data. Zhang et al. identified eighteen Differentially Expressed Genes (DEGs), six hub genes, and two predictive indicators for colorectal liver metastases, relying on public datasets. Pan et al. found IRF4 and TNFRSF17 and validated their model using GSE1433, but external clinical validation remained lacking.
Ma et al. created a thirteen-gene immune-related gene classifier with AUC ethics varying from 0.68 to 0.74, presenting modest accuracy. Liu et al. used ESTIMATE to identify six predictive DEGs associated with immunological and stromal scores; however, their findings were based on retrospective data. Wang et al. identified fifteen important genes, including IL1RN and PRRX1, which are immune-relevant; however, the clinical relationship is poor. Su et al. found that their random forest model outperformed Least Absolute Shrinkage and Selection Operator (LASSO) and Weighted Gene Co-expression Network Analysis (WGCNA), but accuracy decreased with each validation stage. Koppad et al. discovered that random forest outperformed five other ML classifiers, finding 34 genes, albeit dataset heterogeneity was a concern. Li et al. used SVM and LASSO to identify eleven diagnostic genes with substantial AUCs; however, their performance was only moderate. Johnson et al. created a seven-gene model for metastatic colorectal cancer that outperformed current techniques; nevertheless, clinical testing was required. Ye et al. developed a fifteen-gene profile that predicts survival risk, although their retroactive analysis is limited. Wang et al. created a four-gene model that was verified using SVM, quantitative Polymerase Chain Reaction (PCR), and ImmunoHistoChemistry (IHC), with little external testing. Sharma et al. discovered genes associated with immunological and cell cycle pathways that lacked in vivo validation. Leng et al. discovered 249 medicines and identified TIMP1 as a critical prognostic gene, still without experimental evidence. Liu et al. developed a seven-gene signature for recognizing Hepcidin Antimicrobial Peptide (HAMP); furthermore, dataset dependency was a drawback. Kang et al. created an immune-related model linked to survival, which requires prospective validation. Salimy et al. found that HPOAE outperformed other multi-omics models but lacked clinical testing. Maurya et al. obtained 100% accuracy with a random forest model based on Boruta feature selection; however, data imbalance was a concern. Li et al. employed WGCNA to identify thirteen gene modules, with the brown and blue modules being the most important, however the research lacked validation through clinical trials. Mortezapour et al. discovered seven increased genes, including miR-940, but did not ensure drug testing. Xiao et al. discovered three biomarkers that link Crohn's disease and COAD, albeit using a tiny dataset. Wang et al. developed a TLS-based survival prediction model using seven genes that required clinical confirmation.
Xu et al. created a six-URG signature that demonstrated good immunological and prognostic classification, albeit with dataset dependencies. Xu et al. used semi-supervised machine learning on 933 data to generate eighteen prognostic and ten predictive signatures for a 5-FU response; still, the validation was insufficient. Building on previous research, Radhakrishnan et al. used ML algorithms and PPI examination to uncover proteomic biomarkers for CRC. It uses classifiers such as LASSO, XGBoost, and LightGBM on proteome reports from both healthy persons and CRC patients. LASSO has the highest AUC, at 75%. Trefoil Factor 3 (TFF3), Lipocalin 2 (LCN2), and Carcinoembryonic Antigen-Related Cell Adhesion Molecule 5 (CEACAM5) were recognized as crucial markers for group devotion and irritation. Similarly, Zhang et al. created a system that uses matched CRC tumor and organoid gene appearance information to enhance chemotherapy reaction forecast. Using consensus WGCNA, researchers have identified important gene modules and proposed biomarkers or important genes for patients with CC. However, these were often based on PPI networks or expression analysis. Identifying essential genes and understanding their molecular pathways in the growth and development of CC in patients was a hard task. Additional research was necessary in this area.
The current research is significant in the field of computational oncology as it addresses the challenges of developing effective multi-targeted therapies for CC. The key contributions of this research are listed below.
The intention is to generate a computational technique that targets multiple signaling pathways in CC, overcoming the limitations of traditional single-target approaches and improving treatment precision. The data is gathered from high-dimensional genomic, Transcriptomics, and proteomic data, enabling robust biomarker discovery and deeper insights into drug resistance mechanisms.
The research introduces the Adaptive Bacterial Foraging - CatBooost algorithm (ABF-CatBoost) to enhance parameter tuning and predictive accuracy, enabling effective classification of patients and identification of key biomarkers and drug targets. The model personalizes therapy by predicting drug efficacy, toxicity risks, and metabolic pathways based on patient-specific molecular profiles, contributing to safer and more effective treatment strategies. The proposed framework is adaptable for other cancers by modifying biomarker selection and pathway analysis modules, thus expanding its impact in precision oncology and personalized medicine.
