Altered gene expression pattern due to different tumor percentage affects functions

High-throughput data are produced on a big scale and at several levels in order to comprehend complex diseases including cancer, diabetes, and kidney disorders. A significant obstacle still exists, nonetheless, in extracting useful information from huge datasets for a thorough comprehension of cell phenotypes and disease pathogenesis. Big data is created to address biological concerns as a result of technological advancements, and it is always easy for biologists and computer scientists to collaborate to streamline the massive datasets and extract the information that is relevant and physiologically important. In order to achieve this, we have classified using expression datasets and inferred the corresponding functional annotation using a computational technique. In order to analyze changes in gene expression and functional annotation, we employed a dataset of prostate cancer patients with normal and variable tumor percentages. We have chosen a gene expression omnibus (GEO) dataset that includes human samples with a range of tumor percentages (0–85%). We arranged the samples according to tumor proportion in ascending order and compared them to the control group (samples with no tumor) to look for changes in gene expression and developed functions as the tumor percentage increased. When the tumor percentage is less than 50%, we see some fluctuation in the number of differentially expressed genes (DEGs), but after that, it increases exponentially. In terms of the pathways, there is a lot of variation in the number of enriched pathways, with tumor < 50% not increasing, while several cancer-associated pathways seem to be enriched for nearly all the different tumor percentages. Our analysis leads us to the conclusion that whereas a number of cancer-associated pathways are consistently enriched for all tumor percentages, the number of differentially expressed genes (DEGs) grows as the tumor percentage rises while the enriched pathways do not. Insulin resistance, acute myeloid leukemia, basal TFs, HIF1-a, neurotrophin, base excision repair, ErbB, VEGF, and mTOR signaling pathways are among the top-ranked enriched pathways. RPP30, SRP14, CCNE1, PRKAR1A, ABCF2, PCMT1, TUBA1C, STOML2, PPP2R4, and TPI1 are possible pathway components.