Prostate cancer disproportionately impacts African American men, who experience significantly higher mortality rates and earlier disease onset than other populations. Current diagnostic approaches, including prostate-specific antigen testing and biopsy, lack sufficient specificity and sensitivity, underscoring the need for accurate, molecular-level classification tools. This paper presents a machine learning framework for binary classification of genomic DNA sequences as cancerous or healthy. A dataset of 1684 FASTA-formatted sequences obtained from the National Library of Medicine - GenBank was analyzed, with 1662 sequences retained after quality control filtering. Feature engineering yielded 67 attributes, including GC content, Shannon entropy, sequence length, and trinucleotide k-mer frequencies. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique to the training data. Seven classification algorithms were evaluated using stratified train-test splits, cross-validation, and hyperparameter optimization. Among the models, the optimized Random Forest classifier achieved superior performance, with a cross-validation accuracy of 97.2% (±0.006), a weighted F1-score of 0.95, a cancer-class recall of 0.96, and an ROC-AUC of 0.974. Feature importance analysis identified sequence length and Shannon entropy as the most discriminative predictors, followed by specific trinucleotide motifs (TTC, AAC, ACC, and GGG). These results demonstrate the potential of interpretable machine learning approaches for genomic sequence-based PCa classification, offering a promising pathway toward improved, equitable diagnostic tools for high-risk populations.
Cancer is a public health crisis affecting approximately 19.3 million globally annually. Lung cancer has the highest percentage of incidence and is responsible for 27% of cancer-related deaths. The lung cancer of prevalence in smokers is small-cell lung carcinoma. Lung cancer cells often exhibit altered gene expression patterns, compared to their normal counterparts. Yet this differential gene expression is not well understood. Herein, we investigated the differential expression of genes in small cell cancer cell lines compared to a healthy lung cell line that can lead to further insights into the proliferation, metastasis, and drug resistance of these lung cancer cells. Whole transcriptomic and small non-coding RNA sequencing was done by Next-generation sequencing (NGS) method, coupled with the Ingenuity Pathway Analysis (IPA) to decipher the core canonical pathways involved in these lung cancer cell-signaling mechanisms in disease pathophysiology. The findings of this investigation identified multiple genes and small non-coding tRNA/tRNA fragments that were differentially expressed, which thereby showed the possibility of potential diagnostic/prognostic and therapeutic targets. This important transcriptomic analysis can be a valuable tool for developing more effective treatment strategies for smokers lung cancer.
