PURPOSE Prostate cancer is a significant global health issue. The tumor microenvironment significantly influences cancer progression, treatment response, and metastasis. There's a lack of studies directly comparing adjacent tissue behavior to both tumor and normal tissue. An analytical strategy was developed to identify genes altered in the adjacent tissue to the tumor without the need for comparison with tumor or normal tissue. METHODS Data sets containing information on both adjacent tumor and normal tissue were searched. Also, transcriptomic data from the prostate cancer project in TCGA for adjacent tumor samples were searched. Differentially expressed genes (DEGs) were identified from the dataset enabling direct comparison between adjacent tissue and normal tissue. The DEGs were utilized as probes to identify other genes of interest within the TCGA database. We hypothesized that genes showing correlated expression with the DEGs would also exhibit altered expression in adjacent tissue within TCGA.A co-expression network was constructed using all genes displaying a correlation coefficient greater than 0.6 with at least one of the DEGs. Finally, a network analysis was conducted. RESULTS GSE6919 was the only dataset that allowed direct comparison of adjacent tissue to prostate cancer with normal tissue. Six DEGs were identified TRIB1, C18orf54, ARF1, NFKBIZ, and LINC00662, and MAFB. The transcriptomes of the 54 adjacent tissues to prostate cancer from TCGA were interrogated, revealing that the expression profiles of 3316 genes had a Pearson correlation coefficient greater than 0.6 with at least one of the 6 DEGs. With these genes, a co-expression network was constructed, retaining 3254 genes. Two networks were identified, one with 2234 genes and another with 395 genes. The first network was enriched with genes related to protein synthesis and vesicle transport from the endoplasmic reticulum to the Golgi apparatus. The second network was enriched with genes from the IL17 signaling pathway and the TNF signaling pathway. CONCLUSION The proposed method proved effective in identifying genes potentially altered in adjacent tissue to the tumor, even in the absence of normal tissue for comparison. Some pathways identified have been previously associated with cancer progression, metastasis, and treatment response. Further studies are warranted to determine whether the behavior of these and novel discovered pathways in adjacent tissue has clinical implications for prostate cancer.