TRGC1

Melanoma is one of the most sensitive tumors to immune modulation and the major challenge for melanoma patients' survival is immune checkpoint inhibitors therapy. γδ T lymphocytes play an antitumoral role in a broad variety of tumors including melanoma and they are optimal candidates for cellular immunotherapy. Thus, a comprehensive analysis of the correlation between γδ T cells and immune checkpoint receptors in the context of melanoma was conducted, with the aim of devising an innovative combined immunotherapeutic strategy. In this study, using the GEPIA2.0 database, a significant positive correlation was observed between the expression of γδ T cell-related genes (TRGC1, TRGC2, TCRD) and immune checkpoint genes (PDCD1, HAVCR2, LAG3), highlighting the potential role of γδ T cells in the immune response within melanoma. Moreover, flow cytometry analysis unveiled a significant augmentation in the population of γδ T cells within melanoma lesions, which exhibited the expression of immune checkpoint receptors including LAG3, TIM-3, and PD1. Analysis of scRNA seq data revealed a significant enrichment and functional reprogramming of γδ T cell clusters in response to immune checkpoint inhibitors (ICI). Interestingly, the effects of ICI therapy varied between Vδ1 and Vδ2 γδ T cell subsets, with distinct changes in gene expression patterns. Lastly, a correlation analysis between γδ T cell abundance, immune checkpoint gene expression, and clinical outcomes in melanoma patients showed that low expression of immune checkpoint genes, including LAG3, HAVCR2, and PDCD1, was associated with improved 1-year overall survival, emphasizing the significance of these genes in predicting patient outcomes, potentially outweighing the impact of γδ T cell abundance. This study offers critical insights into the dynamic interaction between γδ T cells, immune checkpoint receptors, and melanoma, providing valuable prospectives for potential therapeutic avenues and predictive markers in this intricate interplay.

Although the tumor mutation burden (TMB) was reported as a biomarker for immunotherapy of various cancers, whether it can effectively predict the survival prognosis in breast cancer patients remains unclear. In this study, the prognostic value of TMB and its correlation with immune infiltration were explored by using multigroup studies.

The somatic mutation data of 986 breast cancer patients were obtained from TCGA database. Breast cancer patients were divided into a low-TMB group and a high-TMB group according to the quartile of TMB scores. The differentially expressed genes (DEGs) were identified by the "limma" R program. The CIBERSORT algorithm was utilized to estimate the immune cell fraction of each sample. The TIMER database was utilized to evaluate the association between CNVs of immune genes and tumor immune cell infiltration and the prognostic value of the immune cells in breast cancer.

In breast cancer, TP53, PIK3CA, TTN, CDH1 and other genes were the most important mutated genes. Higher survival rate of patients was found in the low-TMB group. Among the top 10 DEGs, three of them belong to the KRT gene family. GSEA enrichment analysis showed that MAPK, Hedgehog, mTOR, TGF-bate and GnRH signaling pathways were enriched in the low-TMB group. The infiltration levels of the most of immune cells were higher in the low-TMB group (P < 0.01). Higher expression of CCL18 and TRGC1 was correlated with poor prognosis. Breast cancer patients with CCL18 copy number variations, especially arm-level gains, showed significantly decreased immune cell infiltration. In the low B cell infiltration group, the survival prognosis of breast cancer patients was poor.

TMB is a potential prognosis marker in breast cancer. Immune-related gene CCL18 and TRGC1 are biomarkers of poor prognosis while immune (B cell) infiltration is a biomarker of good prognosis.

THP-1 monocyte, which can be differentiated into macrophages by PMA, is widely used in researches on pathogen infection and host innate immunity, but reports on the induction methods of PMA are different and lack a unified standard, and the transcriptome characteristics of macrophage compared with THP-1 cells remains unclear. In this research, we examined the differentiation effect of three factors including induction time, cell seeding density and PMA concentration by detecting the positive rate of CD14 expression. The concentration of 80ng/ml of PMA, the induction time of 24h, and the cell seeding density of 5×105 cells/ml, could respectively facilitates a relatively higher CD14 positive rate in THP-1 cells. Under this optimized conditions, the CD14 positive rate of THP-1 cells can reach 66.52%. Transcriptome sequencing showed that after the above induction, the mRNA expression of 3113 genes which were closely related to cell communication, signal transduction, cell response to stimulus, signaling receptor binding and cytokine activity were up-regulated, and the top 10 genes were RGS1, SPP1, GDF15, IL-1B, HAVCR2, SGK1, EGR2, TRAC, IL-8 and EBI3. While the mRNA expression of 2772 genes which were associated with cell cycle process, DNA binding and replication and cell division, were down-regulated, and the top genes were SERPINB10, TRGC2, SERPINB2, TRGC1, MS4A3, MS4A4E, TRGJP1, MS4A6A, TRGJP2, MS4A4A. This research optimized the induction method on THP-1 cell differentiation from three aspects and delineated the transcriptomic profile of PMA-induced THP-1 cells, laying a foundation for the construction method of cell model and for the functional study of macrophage.