Establishing a Prognostic Model Based on Ulceration and Immune Related Genes in Melanoma Patients and Identification of EIF3B as a Therapeutic Target

Unveiling Atherosclerotic Plaque Heterogeneity and SPP1+/VCAN+ Macrophage Subtype Prognostic Significance Through Integrative Single-Cell and Bulk-Seq Analysis

Background

Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.

Methods

This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.

Results

Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1+/VCAN+ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.

Conclusion

Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability.

A novel EIF3C-related CD8+ T-cell signature in predicting prognosis and immunotherapy response of nasopharyngeal carcinoma

PURPOSE

At present, dysfunctional CD8+ T-cells in the nasopharyngeal carcinoma (NPC) tumor immune microenvironment (TIME) have caused unsatisfactory immunotherapeutic effects, such as a low response rate of anti-PD-L1 therapy. Therefore, there is an urgent need to identify reliable markers capable of accurately predicting immunotherapy efficacy.

METHODS

Utilizing various algorithms for immune-infiltration evaluation, we explored the role of EIF3C in the TIME. We next found the influence of EIF3C expression on NPC based on functional analyses and RNA sequencing. By performing correlation and univariate Cox analyses of CD8+ Tcell markers from scRNA-seq data, we identified four signatures, which were then used in conjunction with the lasso algorithm to determine corresponding coefficients in the resulting EIF3C-related CD8+ T-cell signature (ETS). We subsequently evaluated the prognostic value of ETS using univariate and multivariate Cox regression analyses, Kaplan-Meier curves, and the area under the receiver operating characteristic curve (AUROC).

RESULTS

Our results demonstrate a significant relationship between low expression of EIF3C and high levels of CD8+ T-cell infiltration in the TIME, as well as a correlation between EIF3C expression and progression of NPC. Based on the expression levels of four EIF3C-related CD8+ T-cell marker genes, we constructed the ETS predictive model for NPC prognosis, which demonstrated success in validation. Notably, our model can also serve as an accurate indicator for detecting immunotherapy response.

CONCLUSION

Our findings suggest that EIF3C plays a significant role in NPC progression and immune modulation, particularly in CD8+ T-cell infiltration. Furthermore, the ETS model holds promise as both a prognostic predictor for NPC patients and a tool for adjusting individualized immunotherapy strategies.

CCNE1 is a predictive and immunotherapeutic indicator in various cancers including UCEC: a pan-cancer analysis

BACKGROUND

CCNE1 plays an important oncogenic role in several tumors, especially high-stage serous ovarian cancer and endometrial cancer. Nevertheless, the fundamental function of CCNE1 has not been explored in multiple cancers. Therefore, bioinformatics analyses of pan-cancer datasets were carried out to explore how CCNE1 regulates tumorigenesis.

METHODS

A variety of online tools and cancer databases, including GEPIA2, SangerBox, LinkedOmics and cBioPortal, were applied to investigate the expression of CCNE1 across cancers. The pan-cancer datasets were used to search for links between CCNE1 expression and prognosis, DNA methylation, m6A level, genetic alterations, CCNE1-related genes, and tumor immunity. We verified that CCNE1 has biological functions in UCEC cell lines using CCK-8, EdU, and Transwell assays.

RESULTS

In patients with different tumor types, a high mRNA expression level of CCNE1 was related to a poor prognosis. Genes related to CCNE1 were connected to the cell cycle, metabolism, and DNA damage repair, according to GO and KEGG enrichment analyses. Genetic alterations of CCNE1, including duplications and deep mutations, have been observed in various cancers. Immune analysis revealed that CCNE1 had a strong correlation with TMB, MSI, neoantigen, and ICP in a variety of tumor types, and this correlation may have an impact on the sensitivity of various cancers to immunotherapy. CCK-8, EdU and Transwell assays suggested that CCNE1 knockdown can suppress UCEC cell proliferation, migration and invasion.

CONCLUSION

Our study demonstrated that CCNE1 is upregulated in multiple cancers in the TCGA database and may be a promising predictive biomarker for the immunotherapy response in some types of cancers. Moreover, CCNE1 knockdown can suppress the proliferation, migration and invasion of UCEC cells.

Transcriptome-based network analysis related to M2-like tumor-associated macrophage infiltration identified VARS1 as a potential target for improving melanoma immunotherapy efficacy

RATIONALE

The M2-like tumor-associated macrophages (TAMs) are independent prognostic factors in melanoma.

METHODS

We performed weighted gene co-expression network analysis (WGCNA) to identify the module most correlated with M2-like TAMs. The Cancer Genome Atlas (TCGA) patients were classified into two clusters that differed based on prognosis and biological function, with consensus clustering. A prognostic model was established based on the differentially expressed genes (DEGs) of the two clusters. We investigated the difference in immune cell infiltration and immune response-related gene expression between the high and low risk score groups.

RESULTS

The risk score was defined as an independent prognostic value in melanoma. VARS1 was a hub gene in the M2-like macrophage-associated WGCNA module that the DepMap portal demonstrated was necessary for melanoma growth. Overexpressing VARS1 in vitro increased melanoma cell migration and invasion, while downregulating VARS1 had the opposite result. VARS1 overexpression promoted M2 macrophage polarization and increased TGF-β1 concentrations in tumor cell supernatant in vitro. VARS1 expression was inversely correlated with immune-related signaling pathways and the expression of several immune checkpoint genes. In addition, the VARS1 expression level helped predict the response to anti-PD-1 immunotherapy. Pan-cancer analysis demonstrated that VARS1 expression negatively correlated with CD8 T cell infiltration and the immune response-related pathways in most cancers.

CONCLUSION

We established an M2-like TAM-related prognostic model for melanoma and explored the role of VARS1 in melanoma progression, M2 macrophage polarization, and the development of immunotherapy resistance.

Identification of Three Genes Associated with Metastasis in Melanoma and Construction of a Predictive Model: A Multiracial Identification

The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2, $P\le 0.02$ ; SNRPD3, $P=0.12$ ; EIF4A3, $P=0.11$ ; GSE65904, SNRPD2, $P=0.04$ ; SNRPD3, $P=0.10$ ; EIF4A3, $P<0.01$ ; GSE19234, SNRPD2, $P<0.01$ ; SNRPD3, $P<0.01$ ; EIF4A3, $P<0.01$ ). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2, $988.5\pm 47.83$ vs. $738.4\pm 35.35$ , $P<0.01$ ; SNRPD3, $502.7\pm 25.7$ vs. $416.4\pm 23.88$ , $P=0.02$ ; EIF4A3, $567.6\pm 19.56$ vs. $495.2\pm 21.1$ , $P=0.01$ ). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients.