Background: DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in hepatocellular carcinoma (HCC) prognosis has received little attention. In this study, we want to develop a prognostic signature that will open up novel therapy options for HCC. Methods: We acquired mRNA expression profiles and clinical data of HCC patients from the TCGA database. A polygenic prognostic model for HCC was constructed using LASSO Cox regression and was validated using the ICGC database. Correlations between risk signature and immune status, clinical characteristics and drug sensitivity were investigated. Protein expression levels of prognostic genes were verified using immunohistochemistry. Results: A DDRGs signature model was developed using LASSO Cox regression analysis. Patients in the high-risk group had worse overall survival compared to the low-risk group. Multivariate Cox analysis showed that the risk score is an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of prognostic genes were significantly correlated with increased sensitivity of cancer cells to anti-tumor drugs. Immunohistochemistry staining indicated that, except for NEIL3, the other 9 genes were highly expressed in HCC. Conclusion: Ten DDRGs were utilized to create a new signature that might influence the immunological state in HCC and be used for prognostic prediction. In addition, blocking these genes could be an alternate treatment.

Background: Type 2 diabetes mellitus (T2DM), which has a high incidence and several harmful consequences, poses a severe danger to human health. More research is being done on ferroptosis’ function in T2DM. This study uses a bioinformatics technique to look for new diagnostic T2DM biomarkers associated with ferroptosis. Methods: In order to identify ferroptosis-related genes (DEGs) that are differently expressed between T2DM patients and healthy individuals, we first obtained T2DM sequencing data and ferroptosis-related genes (FRGs) from the Gene Expression Omnibus (GEO) database and FerrDb database. Then, drug-gene interaction networks and ceRNA networks linked to the marker genes were built after marker genes were filtered by two machine learning algorithms (LASSO and SVM-RFE algorithms). Finally, to confirm the expression of marker genes, the GSE76895 dataset was utilized. The protein expression of some marker genes between T2DM and non-diabetic tissues was also examined by Western Blotting, Immunohistochemistry (IHC) and Immunofluorescence (IF), respectively. Results: We obtained 58 DEGs associated with ferroptosis. GO and KEGG enrichment analysis showed that these DGEs were significantly enriched in hypoxia and ferroptosis. Subsequently, eight marker genes (SCD, CD44, HIF1A, BCAT2, MTF1, HILPDA, NR1D2 and MYCN) were screened by LASSO and SVM- RFE machine learning algorithms, and a model was constructed based on these eight genes. These newly discovered marker genes may be linked to alterations in the immune microenvironment in T2DM patients. In addition, based on these 8 genes, we obtained 48 drugs and a complex ceRNA network map. Finally, Western Blotting, IHC and IF results of clinical samples further confirmed the results of public databases. Conclusions: The diagnosis and etiology of T2DM can be greatly aided by eight ferroptosis-related genes, opening up novel therapeutic avenues.