Raman spectroscopy and bioinformatics-based identification of key genes and pathways capable of distinguishing...

Diffuse large B-cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) are subtypes of non-Hogkin lymphoma (NHL) that are generally distinct form one cases, but the transformation of one of these diseases into the other is possible. Some patients with CLL, for instance, have the potential to develop Richter transformation such that they are diagnosed with a rare, invasive DLBCL subtype. In this study, bioinformatics analyses of these two NHL subtypes were conducted, identifying key patterns of gene expression and then experimentally validating the results. Disease-related gene expression datasets from the GEO database were used to identify differentially expressed genes (DEGs) and DEG functions were examined using GO analysis and protein-protein interaction network construction. This strategy revealed many up- and down-regulated DEGs, with functional enrichment analyses identifying these genes as being closely associated with inflammatory and immune response activity. PPI network analyses and the evaluation of clustered network modules indicated the top 10 up- and down-regulated genes involved in disease onset and development. Serological analyses revealed significantly higher ALB, TT, and WBC levels in CLL patients relative to DLBCL patients, whereas the opposite was true with respect to TG, HDL, GGT, ALP, ALT, and NEUT% levels. In comparison to the CLL and DLBCL groups, the healthy control samples demonstrated higher signals of protein peak positions (621, 643, 848, 853, 869, 935, 1003, 1031, 1221, 1230, 1260, 1344, 1443, 1446, 1548, 1579, 1603, 1647 cm^-1), nucleic acid peak positions (726, 781, 786, 1078, 1190, 1415, 1573, 1579 cm^-1), beta carotene peak positions (957, 1155, 1162 cm^-1), carbohydrate peak positions (842 cm^-1), collagen peak positions (1345 cm^-1), and lipid peak positions (957, 1078, 1119, 1285, 1299, 1437, 1443, 1446 cm^-1) compared to the CLL and DLBCL groups. Verification of these key genes in patient samples yielded results consistent with findings derived from bioinformatics analyses, highlighting their relevance to diagnosing and treating these forms of NHL. Together, these analyses identified genes and pathways involved in both DLBCL and CLL. The set of molecular markers established herein can aid in patient diagnosis and prognostic evaluation, providing a valuable foundation for their therapeutic application.