CFHR3 (Complement Factor H Related 3)

In addition, the prognostic signature retained independent predictive value for HCC RFS, and it was validated successfully by another publicly available dataset and RT-qPCR experiments using patient tissues. In conclusion, the present study constructed a prognostic model for predicting RFS in patients with HCC via integrated analysis of scRNA-seq and bulk RNA-seq data that could serve as a valuable reference tool for clinicians.

This study also revealed that the risk model effectively predicted sensitivity to multiple anticancer drugs. This study developed a robust ICD-related six-gene prognostic model for HCC that can accurately stratify patient risk, reflect the tumor immune landscape, and provide guidance for immunotherapy and personalized treatment strategies.

It also underscores the role of genetic predisposition and the therapeutic benefit of complement inhibition with eculizumab. Early recognition, drug discontinuation, and consideration of both genetic screening and complement inhibition may improve outcomes in at-risk patients.

Treatment with SP94 peptide-modified dual-mRNA LNPs markedly suppressed HCC tumor growth and exhibited excellent biocompatibility and safety. Our study proposes a dual-targeted therapeutic strategy for HCC, which may represent a promising treatment approach.

Our study provides new insights into germline variants that can be associated with the development of GISTs, namely, CFHR1 and CFHR3 deep deletions. Further functional validation is warranted to elucidate the precise contributions of identified germline mutations in GIST development.

The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets.

Finally, specific mutations have been associated with the onset of CRS (PFKFB4, CX3CR1) and ICANS (PPM1D, DNMT3A, TE2, ASXL1). More research is essential in this field to achieve better outcomes for our patients.

We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines. The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

We developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.

Ultimately, this multilayered serum analysis led to a potential marker signature with 10 key markers significantly altered in at least two independent serum cohorts: CRP, LYVE1, SAA2, C1RL, CFHR3, LBP, LDHB, S100A8, S100A9, and SAA1, which will serve as the basis for further investigation. In addition to patient serum, we analyzed primary melanoma tumor cells from NR and found a potential marker signature with 4 key markers: LAMC1, PXDN, SERPINE1, and VCAN.

Moreover, the weighted gene co-expression network analysis and gene enrichment analyses found that the integrated signature were associated with metabolic-related biological process and lipid metabolism pathway, which has been implicated in the pathogenesis of CCA. Taken together, we developed an integrated mRNA-lncRNA signature that had an independent prognostic value in the risk stratification of patients with CCA.