Coactosin-Like Protein 1 (COTL1) Could Be an Immunological and Prognostic Biomarker: From Pan-Cancer Analysis to Low-Grade Glioma Validation

Background

Cancer represents a widespread global health challenge impacting millions of individuals worldwide. Identifying new targets for cancer treatment is a crucial step in developing more effective therapies. Among these potential targets, Coactosin-like protein 1 (COTL1), a cytoskeleton-associated protein with critical roles in cell migration, adhesion, and signaling, has shown involvement in tumor progression.

Methods

GSCA, TIMER, SangerBox database were used to explore the COTL1 expression across different tumor types. We employed the TCGA Pan-Atlas Cancer Genomics Dataset, which is available through the cBioportal platform, to explore genetic alterations in COTL1. We conduct a comprehensive analysis of COTL1, encompassing gene expression, clinical prognosis, RNA modification, immunotherapy, and cancer stemness through SangerBox database. Clinical samples were validated using immunohistochemistry.

Results

Our analysis revealed that COTL1 is highly expressed in most cancers and correlates with decreased survival in Glioma, Glioblastoma multiforme, and pan-kidney cohorts. Furthermore, COTL1 was found to be associated with DNA and RNA stemness in 20 and 22 different tumor types, respectively. Additionally, COTL1 showed positive correlations with immunological checkpoints and immune infiltration cells. It was also linked to tumor mutation burden (TMB), microsatellite instability (MSI), neoantigen (NEO), and programmed death ligand 1 (PD-L1), all of which are potential targets for immunotherapies. Moreover, a favorable relationship was demonstrated between genomic-instability markers such as heterozygosity (LOH), homologous recombination deficiency (HRD), and mutant allele tumor heterogeneity (MATH) with COTL1. Furthermore, our findings confirmed a positive correlation between COTL1 expression, CD8, and PD-L1 in LGG, as well as an association of high COTL1 expression with decreased patient survival in LGG.

Conclusion

Based on these compelling findings, COTL1 may hold significant clinical implications for the development of novel cancer therapies and serve as a potential target for tumors associated with immunotherapy in the future.

Investigating the Mechanism of Chufan Yishen Formula in Treating Depression through Network Pharmacology and Experimental Verification

Objective: To investigate the antidepressant effect and potential mechanism of the Chufan Yishen Formula (CFYS) through network pharmacology, molecular docking, and experimental verification. Methods: The active ingredients and their target genes of CFYS were identified through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and TCM-ID. We obtained the differentially expressed genes in patients with depression from the GEO database and screened out the genes intersecting with the target genes of CFYS to construct the PPI network. The key pathways were selected through STRING and KEGG. Then, molecular docking and experimental verification were performed. Results: A total of 113 effective components and 195 target genes were obtained. After intersecting the target genes with the differentially expressed genes in patients with depression, we obtained 37 differential target genes, among which HMOX1, VEGFA, etc., were the key genes. After enriching the differential target genes by KEGG, we found that the "chemical carcinogenesis-reactive oxygen species" pathway was the key pathway for the CFYS antidepressant effect. Besides, VEGFA might be a key marker for depression. Experimental verification found that CFYS could significantly improve the behavioral indicators of rats with depression models, including improving the antioxidant enzyme activity and increasing VEGFA levels. The results are consistent with the network pharmacology analysis. Conclusions: CFYS treatment for depression is a multicomponent, multitarget, and multipathway complex process, which may mainly exert an antidepressant effect by improving the neuron antioxidant stress response and regulating VEGFA levels.

Identifying immune-related prognostic biomarkers in osteosarcoma: Development and validation of the tumor immune microenvironment risk model based on analysis of TCGA

OBJECTIVE

Osteosarcoma is a rare and aggressive malignancy with limited effective therapeutic options. This study aimed to identify immune-related prognostic biomarkers and develop a prognostic model for osteosarcoma.

METHODS

We performed integrated analysis of transcriptomic data and immune cell infiltration profiles of 84 osteosarcoma samples from the Cancer Genome Atlas (TCGA) database. Time-dependent receiver operating characteristic (ROC) curve analysis was used to assess the prognostic value of the TIMErisk model. We also performed functional annotation and pathway enrichment analyses to explore the potential mechanisms underlying the TIMErisk model.

RESULTS

We identified a seven-gene TIMErisk model (C2, APBB1IP, BST2, TRPV2, CCL5, GBP1, and F13A1) that was independently associated with overall survival of osteosarcoma patients. The TIMErisk model showed significant associations with immune cell infiltration and immunosuppressive gene expression. In addition, the TIMErisk model was associated with drug sensitivity, and we found that several immune checkpoint genes were significantly differentially expressed between high- and low-TIMErisk groups. Functional annotation and pathway enrichment analyses revealed that the TIMErisk model was associated with multiple immune-related pathways, including antigen processing and presentation, cytokine-cytokine receptor interaction, and T cell receptor signaling pathway.

CONCLUSION

Our study identified a novel TIME-based prognostic model for osteosarcoma that incorporates immune-related genes and can be used to predict patient prognosis and response to immunotherapy. Our findings highlight the importance of the TIME microenvironment in osteosarcoma progression and suggest that immune-related biomarkers may have clinical significance in the management of osteosarcoma.

RNA binding protein RBM46 regulates mitotic-to-meiotic transition in spermatogenesis

Meiosis entry during spermatogenesis requires reprogramming from mitotic to meiotic gene expression profiles. Transcriptional regulation has been extensively studied in meiosis entry, but gain of function for master transcription factors is insufficient to down-regulate mitotic genes. RNA helicase YTHDC2 and its partner MEIOC emerge as essential posttranscriptional regulators of meiotic entry. However, it is unclear what governs the RNA binding specificity of YTHDC2/MEIOC. Here, we identified RNA binding protein RBM46 as a component of the YTHDC2/MEIOC complex. Testis-specific Rbm46 knockout in mice causes infertility with defective mitotic-to-meiotic transition, phenocopying global Ythdc2 or Meioc knockout. RBM46 binds to 3' UTR of mitotic transcripts within 100 nucleotides from YTHDC2 U-rich motifs and targets these transcripts for degradation. Dysregulated RBM46 expression is associated with human male fertility disorders. These findings establish the RBM46/YTHDC2/MEIOC complex as the major posttranscriptional regulator responsible for down-regulating mitotic transcripts during meiosis entry in mammalian spermatogenesis, with implications for understanding meiosis-related fertility disorders.