A novel biological function-based method for mining core genes in rare disease with limited cases.

Conventional high - throughput sequencing data analysis methods, relying on large cohorts, are unsuitable for rare diseases with few cases and poor at explaining gene biological functions. This study proposes a biological-function-based method for mining core genes under limited-sample conditions. Using colorectal cancer (CRC) and its rare subtype signet-ring cell carcinoma (SRCC) as examples, differential expression analysis was performed separately for each SRCC sample against CRC samples. Shared genes obtained through progressive intersections were defined as the initial core gene set (List 1). Non-core genes were subjected to functional enrichment analysis, and pathway-associated genes were integrated to construct the non-core gene set. Two independent SRCC samples were subsequently used for secondary mining to obtain the final biologically significant core gene set (List 2). In addition, RankProd (RP), Robust Rank Aggregation (RRA), and an external mucinous adenocarcinoma dataset (GSE281917) were used for validation. First-round mining identified 246 core genes in SRCC, while secondary mining generated 66 and 65 biologically significant core genes from two validation samples with a high degree of overlap. RP identified 1,850 candidate genes and RRA identified 601 candidate genes, among which 64 genes overlapped with the 65 genes identified by this method. In the external mucinous adenocarcinoma dataset, first-round mining identified 144 core genes, while secondary mining generated 122 and 119 core genes with high overlap between validation samples. This method enables the identification of biologically significant core genes in rare diseases under limited-sample conditions, improves the biological interpretability of gene-mining results, demonstrates stable performance in both SRCC and external mucinous adenocarcinoma datasets, and provides a reliable experimental basis for the advancement of precision medicine in rare disease research.