Unraveling the Relevance of ARL GTPases in Cutaneous Melanoma Prognosis through Integrated Bioinformatics Analysis

Quantitative Proteomic Analysis of Macrophages Infected with Trypanosoma cruzi Reveals Different Responses Dependent on the SLAMF1 Receptor and the Parasite Strain

Chagas disease is caused by the intracellular protozoan parasite Trypanosoma cruzi. This disease affects mainly rural areas in Central and South America, where the insect vector is endemic. However, this disease has become a world health problem since migration has spread it to other continents. It is a complex disease with many reservoirs and vectors and high genetic variability. One of the host proteins involved in the pathogenesis is SLAMF1. This immune receptor acts during the infection of macrophages controlling parasite replication and thus affecting survival in mice but in a parasite strain-dependent manner. Therefore, we studied the role of SLAMF1 by quantitative proteomics in a macrophage in vitro infection and the different responses between Y and VFRA strains of Trypanosoma cruzi. We detected different significant up- or downregulated proteins involved in immune regulation processes, which are SLAMF1 and/or strain-dependent. Furthermore, independently of SLAMF1, this parasite induces different responses in macrophages to counteract the infection and kill the parasite, such as type I and II IFN responses, NLRP3 inflammasome activation, IL-18 production, TLR7 and TLR9 activation specifically with the Y strain, and IL-11 signaling specifically with the VFRA strain. These results have opened new research fields to elucidate the concrete role of SLAMF1 and discover new potential therapeutic approaches for Chagas disease.

Molecular subtype identification and prognosis stratification based on golgi apparatus-related genes in head and neck squamous cell carcinoma

BACKGROUND

Abnormal dynamics of the Golgi apparatus reshape the tumor microenvironment and immune landscape, playing a crucial role in the prognosis and treatment response of cancer. This study aims to investigate the potential role of Golgi apparatus-related genes (GARGs) in the heterogeneity and prognosis of head and neck squamous cell carcinoma (HNSCC).

METHODS

Transcriptional data and corresponding clinical information of HNSCC were obtained from public databases for differential expression analysis, consensus clustering, survival analysis, immune infiltration analysis, immune therapy response assessment, gene set enrichment analysis, and drug sensitivity analysis. Multiple machine learning algorithms were employed to construct a prognostic model based on GARGs. A nomogram was used to integrate and visualize the multi-gene model with clinical pathological features.

RESULTS

A total of 321 GARGs that were differentially expressed were identified, out of which 69 were associated with the prognosis of HNSCC. Based on these prognostic genes, two molecular subtypes of HNSCC were identified, which showed significant differences in prognosis. Additionally, a risk signature consisting of 28 GARGs was constructed and demonstrated good performance for assessing the prognosis of HNSCC. This signature divided HNSCC into the high-risk and low-risk groups with significant differences in multiple clinicopathological characteristics, including survival outcome, grade, T stage, chemotherapy. Immune response-related pathways were significantly activated in the high-risk group with better prognosis. There were significant differences in chemotherapy drug sensitivity and immune therapy response between the high-risk and low-risk groups, with the low-risk group being more suitable for receiving immunotherapy. Riskscore, age, grade, and radiotherapy were independent prognostic factors for HNSCC and were used to construct a nomogram, which had good clinical applicability.

CONCLUSIONS

We successfully identified molecular subtypes and prognostic signature of HNSCC that are derived from GARGs, which can be used for the assessment of HNSCC prognosis and treatment responses.

The Prognostic and Therapeutic Roles of ARL-6 Gene in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent human cancers. ARL-6, a member of the ADP ribosylation factor (like) (ARF) protein family, has gained attention as a potential therapeutic target in various malignancies and a prognostic biomarker. However, its specific roles in HCC, both prognostically and biochemically, remain largely unclear. Methods: To examine the functional relevance of ARL-6 in HCC, we acquired data from GEPIA, UALCAN, TIMER, TCGA, GeneMANIA, and Metascape databases. Then, we conducted immunohistochemistry on a replication sample comprising 26 HCC specimens to assess the efficacy of the ARL-6 gene. To unravel the mechanistic intricacies, we employed diverse assays such as the cell counting kit 8 (CCK8), flow cytometry, and transwell invasion assessment. Results: Our findings demonstrated the mRNA expression of ARL-6 was significantly upregulated in HCC compared to normal tissue, as evidenced by comprehensive database analysis. Immunohistochemistry further revealed that ARL-6 expression was remarkably higher in HCC than in para-carcinoma tissues. Moreover, ARL-6 expression exhibited noteworthy variations across diverse LIHC characteristics, including sample type, histological subtype, TP53 mutation status, nodal metastatic status, and cancer stage. In addition, high transcriptional levels of ARL-6 were correlated with diminished overall survival (OS) and disease-free survival (DFS) in HCC patients. Furthermore, our study indicated positive correlations between ARL-6 expression levels and the activities of tumor-infiltrating immune cells such as B cells, myeloid dendritic cells, macrophages, neutrophils, CD8+T cells, and CD4+T cells. Substantiating our findings, database analysis uncovered additional evidence of ARL-6 gene co-expression and its functional significance in HCC cases. Finally, we demonstrated the involvement of the ARL-6 gene in HCC cell invasion, proliferation, and apoptosis. Conclusions: In conclusion, our investigation sheds light on the pivotal role of ARL-6 in influencing HCC prognosis and treatment by modulating the biological activities of tumor cells. These discoveries hold promise for the development of predictive biomarkers and novel therapeutic avenues for affected patients.

Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma

In this study, we aimed to develop an invasion-related risk signature and prognostic model for personalized treatment and prognosis prediction in skin cutaneous melanoma (SKCM), as invasion plays a crucial role in this disease. We identified 124 differentially expressed invasion-associated genes (DE-IAGs) and selected 20 prognostic genes (TTYH3, NME1, ORC1, PLK1, MYO10, SPINT1, NUPR1, SERPINE2, HLA-DQB2, METTL7B, TIMP1, NOX4, DBI, ARL15, APOBEC3G, ARRB2, DRAM1, RNF213, C14orf28, and CPEB3) using Cox and LASSO regression to establish a risk score. Gene expression was validated through single-cell sequencing, protein expression, and transcriptome analysis. Negative correlations were discovered between risk score, immune score, and stromal score using ESTIMATE and CIBERSORT algorithms. High- and low-risk groups exhibited significant differences in immune cell infiltration and checkpoint molecule expression. The 20 prognostic genes effectively differentiated between SKCM and normal samples (AUCs >0.7). We identified 234 drugs targeting 6 genes from the DGIdb database. Our study provides potential biomarkers and a risk signature for personalized treatment and prognosis prediction in SKCM patients. We developed a nomogram and machine-learning prognostic model to predict 1-, 3-, and 5-year overall survival (OS) using risk signature and clinical factors. The best model, Extra Trees Classifier (AUC = 0.88), was derived from pycaret's comparison of 15 classifiers. The pipeline and app are accessible at https://github.com/EnyuY/IAGs-in-SKCM.

ZAP70 interaction with 13 mRNAs as a potential immunotherapeutic target for endometrial cancer

For advanced, refractory endometrial cancer (EC), it is advisable to find effective immunotherapeutic targets. In the present study, genes affecting the immune status of uterine corpus endometrial carcinoma (UCEC) samples within The Cancer Genome Atlas were explored by weighted correlation network analysis and differential gene expression analysis. The protein function and immune correlation of 14 key genes, including ζ-chain-associated protein kinase 70 (ZAP70), were analyzed. Based on the expression levels of key genes, the patients with UCEC were divided into two groups using consensus clustering, low expression (group 1) and high expression (group 2). Next, the functions of differentially expressed genes (DEGs) between the two groups were identified using Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes analysis and Gene Set Enrichment Analysis. The immune status of the patients in the two groups was evaluated using immune infiltration score and the expression levels of targets of immune checkpoint inhibitors. The role of ZAP70 in the prognosis of patients with UCEC and the differences in ZAP70 expression between EC tissues and healthy intimal tissues were determined by reverse transcription-quantitative PCR and immunohistochemistry. The present study found strong correlations between key genes, including ZAP70, LCK, FOXP3, TIGIT, CTLA4, ICOS, CD5, IL2RG, PDCD1, TNFRSF4, CD27, CCR7, GZMB, CXCL9. From the enrichment analyses, it was found that the functions of these DEGs were related to T cells. Patients in group 2 had stronger immune infiltration and higher immune checkpoints expression compared with those in group 1. ZAP70 was expressed at higher levels in EC tissues compared with in normal tissues, and may act as a protective factor in EC. In conclusion, ZAP70 interaction with 13 mRNAs may affect the immune status of patients with EC and may be a potential target for immunotherapy.