Comprehensive analysis of transient receptor potential channels-related signature for prognosis, tumor immune microenvironment, and treatment response of colorectal cancer

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning

Background

Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs and TiME.

Methods

Predictive models for preoperative radiotherapy (preRT) response were developed using machine learning, identifying EIF5A as a key gene associated with radioresistance. EIF5A expression was analyzed via bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Functional assays and in vivo experiments validated EIF5A's role in radioresistance and TiME modulation.

Results

EIF5A was significantly upregulated in radioresistant colorectal cancer (CRC) tissues. EIF5A knockdown in CRC cell lines reduced cell viability, migration, and invasion after radiation, and increased radiation-induced apoptosis. Mechanistically, EIF5A promoted cancer stem cell (CSC) characteristics through the Hedgehog signaling pathway. Analysis of the TiME revealed that the radiation-resistant group had an immune-desert phenotype, characterized by low immune cell infiltration. In vivo experiments showed that EIF5A knockdown led to increased infiltration of CD8+ T cells and M1 macrophages, and decreased M2 macrophages and Tregs following radiation therapy, thereby enhancing the radiotherapy response.

Conclusion

EIF5A contributes to CRC radioresistance by promoting CSC traits via the Hedgehog pathway and modulating the TiME to an immune-suppressive state. Targeting EIF5A could enhance radiation sensitivity and improve immune responses, offering a potential therapeutic strategy to optimize radiotherapy outcomes in CRC patients.

A comprehensive analysis of TRP-related gene signature, and immune infiltration in patients with colorectal cancer

BACKGROUND

Transient receptor potential (TRP) channels are involved in the development and progression of tumors. However, their role in colorectal cancer (CRC) remains unclear, and this study aims to investigate the role of TRP-related genes in CRC.

METHODS

Data was obtained from The Cancer Genome Atlas (TCGA) database, and analyses were conducted on the GSE14333 and GSE38832 datasets to assess the prognosis and mark TRP-related genes (TRGs). Subsequently, clustering analysis and immune infiltration analysis were performed to explore the relevant TRGs. In vitro validation of key TRGs' gene and protein expression was conducted using human colon cancer cells.

RESULTS

Compared to normal tissues, 8 TRGs were significantly upregulated in CRC, while 11 were downregulated. TRPA1 was identified as a protective prognostic factor, whereas TRPM5 (HR = 1.349), TRPV4 (HR = 1.289), and TRPV3 (HR = 1.442) were identified as prognostic risk factors. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analyses yielded similar results. Additionally, lower expression of TRPA1 and higher expression of TRPV4 and TRPM5 were negatively correlated with patient prognosis, and experimental validation confirmed the underexpression of TRPA1 and overexpression of TRPV4 and TRPM5 in CRC cell lines.

CONCLUSION

This study identifies a TRP channel-related prognosis in CRC, providing a novel approach to stratifying CRC prognosis.

Development and therapeutic implications of small molecular inhibitors that target calcium-related channels in tumor treatment

Calcium ion dysregulation exerts profound effects on various physiological activities such as tumor proliferation, migration, and drug resistance. Calcium-related channels play a regulatory role in maintaining calcium ion homeostasis, with most channels being highly expressed in tumor cells. Additionally, these channels serve as potential drug targets for the development of antitumor medications. In this review, we first discuss the current research status of these pathways, examining how they modulate various tumor functions such as epithelial-mesenchymal transition (EMT), metabolism, and drug resistance. Simultaneously, we summarize the recent progress in the study of novel small-molecule drugs over the past 5 years and their current status.

A Treg-related riskscore model may improve the prognosis evaluation of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)-related gene signature in CRC.

METHODS

We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co-expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg-related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored.

RESULTS

Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg-related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples.

CONCLUSION

We established a useful riskscore model in light of seven Treg-related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

Analysis of prognostic factors in patients with emergency sepsis

BACKGROUND

Emergency sepsis is a common and serious infectious disease, and its prognosis is influenced by a number of factors.

AIM

To analyse the factors influencing the prognosis of patients with emergency sepsis in order to provide a basis for individualised patient treatment and care. By retrospectively analysing the clinical data collected, we conducted a comprehensive analysis of factors such as age, gender, underlying disease, etiology and site of infection, inflammatory indicators, multi-organ failure, cardiovascular function, therapeutic measures, immune status and severity of infection.

METHODS

Data collection: Clinical data were collected from patients diagnosed with acute sepsis, including basic information, laboratory findings, medical history and treatment options. Variable selection: Variables associated with prognosis were selected, including age, gender, underlying disease, etiology and site of infection, inflammatory indicators, multi-organ failure, cardiovascular function, treatment measures, immune status and severity of infection. Data analysis: The data collected are analysed using appropriate statistical methods such as multiple regression analysis and survival analysis. The impact of each factor on prognosis was assessed according to prognostic indicators, such as survival, length of stay and complication rates.

RESULTS

Descriptive statistics: Descriptive statistics were performed on the data collected from the patients, including their basic characteristics and clinical presentation.

CONCLUSION

Type 2 diabetes mellitus were independent factors affecting the prognosis of patients with sepsis.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.