BCAP31 is involved in modulating colorectal cancer cell proliferation via the Emerin/β-catenin axis

Pan-cancer analysis shows that BCAP31 is a potential prognostic and immunotherapeutic biomarker for multiple cancer types

Background

B-cell receptor-associated protein 31 (BCAP31) is a widely expressed transmembrane protein primarily located in the endoplasmic reticulum (ER), including the ER-mitochondria associated membranes. Emerging evidence suggests that BCAP31 may play a role in cancer development and progression, although its specific effects across different cancer types remain incompletely understood.

Methods

The raw data on BCAP31 expression in tumor and adjacent non-tumor (paracancerous) samples were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) and UCSC databases. We also examined the association between BCAP31 expression and clinicopathological factors. Using the Cox proportional hazards model, we found that high BCAP31 levels were linked to poor prognosis. To further explore BCAP31's role, we analyzed the relationship between copy number variations (CNV) and BCAP31 mRNA expression using data from The Cancer Genome Atlas (TCGA). Additionally, the association between BCAP31 expression and signature pathway scores from the MsigDB database provided insights into the tumor biology and immunological characteristics of BCAP31.We assessed the relationship between tumor immune infiltration and BCAP31 expression using the TIMER2 and ImmuCellAI databases. The ESTIMATE computational method was employed to estimate the proportion of immune cells infiltrating the tumors, as well as the stromal and immune components, based on TCGA data. To investigate drug sensitivity in relation to BCAP31 expression, we utilized GDSC2 data, which included responses to 198 medications. We explored the relationship between BCAP31 gene expression and response to immunotherapy. Additionally, the study involved culturing KYSE-150 cells under standard conditions and using siRNA-mediated knockdown of BCAP31 to assess its function. Key experiments included Western blotting (WB) to confirm BCAP31 knockdown, MTT assays for cell proliferation, colony formation assays for growth potential, Transwell assays for migration and invasion, and wound healing assays for motility. Additionally, immunohistochemistry (IHC) was performed on tumor and adjacent normal tissue samples to evaluate BCAP31 expression levels.

Results

BCAP31 was found to be significantly overexpressed in several prevalent malignancies and was associated with poor prognosis. Cox regression analysis across all cancer types revealed that higher BCAP31 levels were predominantly linked to worse overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). In most malignancies, increased BCAP31 expression was positively correlated with higher CNV. Additionally, BCAP31 expression was strongly associated with the tumor microenvironment (TME), influencing the levels of infiltrating immune cells, immune-related genes, and immune-related pathways. Drug sensitivity analysis identified six medications that showed a significant positive correlation with BCAP31 expression. Furthermore, BCAP31 expression impacted the outcomes and prognosis of cancer patients undergoing immune therapy. The functional assays demonstrated that BCAP31 knockdown in KYSE-150 cells significantly inhibited cell migration, invasion, and proliferation while enhancing colony formation ability. WB and immunohistochemistry analyses confirmed elevated BCAP31 expression in tumor tissues compared to adjacent normal tissues in esophageal cancer, lung adenocarcinoma, and gastric adenocarcinoma.

Conclusion

BCAP31 has the potential to serve as a biomarker for cancer immunology, particularly in relation to immune cell infiltration, and as an indicator of poor prognosis. These findings provide a new perspective that could inform the development of more targeted cancer therapy strategies.

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

OBJECTIVES

To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value.

METHODS

Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts.

RESULTS

Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion.

CONCLUSION

A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

Single-cell RNA-Seq and bulk RNA-Seq reveal reliable diagnostic and prognostic biomarkers for CRC

PURPOSE

The potential role of epithelium-specific genes through the adenoma-carcinoma sequence in the development of colorectal cancer (CRC) remains unknown. Therefore, we integrated single-cell RNA sequencing and bulk RNA sequencing data to select diagnosis and prognosis biomarkers for CRC.

METHODS

The CRC scRNA-seq dataset was used to describe the cellular landscape of normal intestinal mucosa, adenoma and CRC and to further select epithelium-specific clusters. Differentially expressed genes (DEGs) of epithelium-specific clusters were identified between intestinal lesion and normal mucosa in the scRNA-seq data throughout the adenoma-carcinoma sequence. Diagnostic biomarkers and prognostic biomarker (the risk score) for CRC were selected in the bulk RNA-seq dataset based on DEGs shared by the adenoma epithelium-specific cluster and the CRC epithelium-specific cluster (shared-DEGs).

RESULTS

Among the 1063 shared-DEGs, we selected 38 gene expression biomarkers and 3 methylation biomarkers that had promising diagnostic power in plasma. Multivariate Cox regression identified  174 shared-DEGs as prognostic genes for CRC. We combined 1000 times LASSO-Cox regression and two-way stepwise regression to select 10 prognostic shared-DEGs to construct the risk score in the CRC meta-dataset. In the external validation dataset, the 1- and 5-year AUCs of the risk score were higher than those of stage, the pyroptosis-related genes (PRG) score and the cuproptosis-related genes (CRG) score. In addition, the risk score was closely associated with the immune infiltration of CRC.

CONCLUSION

The combined analysis of the scRNA-seq dataset and the bulk RNA-seq dataset in this study provides reliable biomarkers for the diagnosis and prognosis of CRC.