COSMIC-based mutation database enhances identification efficiency of HLA-I immunopeptidome.

BACKGROUND: Neoantigens have emerged as a promising area of focus in tumor immunotherapy, with several established strategies aiming to enhance their identification. Human leukocyte antigen class I molecules (HLA-I), which present intracellular immunopeptides to T cells, provide an ideal source for identifying neoantigens. However, solely relying on a mutation database generated through commonly used whole exome sequencing (WES) for the identification of HLA-I immunopeptides, may result in potential neoantigens being missed due to limitations in sequencing depth and sample quality. METHOD: In this study, we constructed and evaluated an extended database for neoantigen identification, based on COSMIC mutation database. This study utilized mass spectrometry-based proteogenomic profiling to identify the HLA-I immunopeptidome enriched from HepG2 cell. HepG2 WES-based and the COSMIC-based mutation database were generated and utilized to identify HepG2-specific mutant immunopeptides. RESULT: The results demonstrated that COSMIC-based database identified 5 immunopeptides compared to only 1 mutant peptide identified by HepG2 WES-based database, indicating its effectiveness in identifying mutant immunopeptides. Furthermore, HLA-I affinity of the mutant immunopeptides was evaluated through NetMHCpan and peptide-docking modeling to validate their binding to HLA-I molecules, demonstrating the potential of mutant peptides identified by the COSMIC-based database as neoantigens. CONCLUSION: Utilizing the COSMIC-based mutation database is a more efficient strategy for identifying mutant peptides from HLA-I immunopeptidome without significantly increasing the false positive rate. HepG2 specific WES-based database may exclude certain mutant peptides due to WES sequencing depth or sample heterogeneity. The COSMIC-based database can effectively uncover potential neoantigens within the HLA-I immunopeptidomes.

Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma.

Background: Despite encouraging results from immunotherapy combined with targeted therapy for hepatocellular carcinoma (HCC), the prognosis remains poor. Chemokines and their receptors are an essential component in the development of HCC, but their significance in HCC have not yet been fully elucidated. We aimed to establish chemokine-related prognostic signature and investigate the association between the genes and tumor immune microenvironment (TIME). Methods: 342 HCC patients have screened from the TCGA cohort. A prognostic signature was developed using least absolute shrinkage and selection operator regression and Cox proportional risk regression analysis. External validation was performed using the LIHC-JP cohort deployed from the ICGC database. Single-cell RNA sequencing (scRNA-seq) data from the GEO database. Two nomograms were developed to estimate the outcome of HCC patients. RT-qPCR was used to validate the differences in the expression of genes contained in the signature. Results: The prognostic signature containing two chemokines-(CCL14, CCL20) and one chemokine receptor-(CCR3) was successfully established. The HCC patients were stratified into high- and low-risk groups according to their median risk scores. We found that patients in the low-risk group had better outcomes than those in the high-risk group. The results of univariate and multivariate Cox regression analyses suggested that this prognostic signature could be considered an independent risk factor for the outcome of HCC patients. We discovered significant differences in the infiltration of various immune cell subtypes, tumor mutation burden, biological pathways, the expression of immune activation or suppression genes, and the sensitivity of different groups to chemotherapy agents and small molecule-targeted drugs in the high- and low-risk groups. Subsequently, single-cell analysis results showed that the higher expression of CCL20 was associated with HCC metastasis. The RT-qPCR results demonstrated remarkable discrepancies in the expression of CCL14, CCL20, and CCR3 between HCC and its paired adjacent non-tumor tissues. Conclusion: In this study, a novel prognostic biomarker explored in depth the association between the prognostic model and TIME was developed and verified. These results may be applied in the future to improve the efficacy of immunotherapy or targeted therapy for HCC.

Multirelational Social Recommendations via Multigraph Ranking.

Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.