SLC11A1 associated with tumor microenvironment is a potential biomarker of prognosis and immunotherapy efficacy for colorectal cancer

SLC11A1 predicts the overall survival of patients with colorectal cancer

Colorectal cancer (CRC) remains a significant contributor to cancer-related mortality, emphasizing the critical need for identifying biomarkers that can improve clinical management and patient outcomes. In this retrospective study, we analyzed tumor samples from 25 patients with metastatic CRC, categorized based on long-term (> 50 months) or short-term (< 10 months) survival. Employing the PanCancer Immune Profile Panel, encompassing 770 genes, in the discovery dataset, we identified 54 differentially expressed genes (DEGs) within the tumor microenvironment of metastatic CRC. Validation of potential biomarkers was performed using two publicly available RNA-based sequencing datasets (TCGA 1 (n=371) and TCGA 2 (n=566)). Univariate COX regression unveiled that three significant biomarkers were associated with overall survival in CRC within the discovery dataset, which were SLC11A1 (hazard ratio (HR): 4.09, P=0.012), TNFSF11 (HR: 3.67, P=0.02), and MEF2C (HR: 0.34, P=0.037). Kaplan-Meier survival curve analyses confirmed the correlation between SLC11A1 expression and overall survival in CRC across the discovery set (P=0.0071) and the two independent datasets (TCGA 1 (P=0.0016) and TCGA 2 (P=0.025)). Receiver operating characteristic curve analysis demonstrated an area under the curve ranging from 0.64 to 0.76, with sensitivity of 59% to 87% and specificity of 60% to 73% for predicting CRC overall survival. Immunohistochemistry staining further validated the strong expression of SLC11A1 protein in CRC tumor cells, with high expression correlating with short-term survival. These findings suggest that SLC11A1 serves as a predictive biomarker for overall survival in CRC patients.

SLC11A1 promotes kidney renal clear cell carcinoma (KIRC) progression by remodeling the tumor microenvironment

Kidney renal clear cell carcinoma (KIRC) is a highly immune-infiltrated kidney cancer with the highest mortality rate and the greatest potential for invasion and metastasis. Solute carrier family 11 member1 (SLC11A1) is a phagosomal membrane protein located in monocytes and plays a role in innate immunity, autoimmune diseases, and infection, but its expression and biological role in KIRC is still unknown. In this study, we sought to investigate the potential value of SLC11A1 according to tumor growth and immune response in KIRC. TIMER and UALCAN database was used to analyze the expression feature and prognostic significance of SLC11A1 and its correlation with immune-related biomarkers in KIRC. Proliferation, migration, and invasion were measured using colony formation, EdU, and transwell assays. Role of SLC11A1 on KIRC tumor growth was examined by the xenograft tumor model in vivo. Effects of KIRC cells on macrophage polarization and the proliferation and apoptosis of CD8+ T cells were analyzed using flow cytometry assays. Herein, SLC11A1 was highly expressed in KIRC tissues and cell lines. SLC11A1 downregulation repressed KIRC cell proliferation, migration, invasion, macrophage, and lymphocyte immunity in vitro, as well as hindered tumor growth in vivo. SLC11A1 is significantly correlated with immune cell infiltration and immune-related biomarkers. In KIRC patients, SLC11A1 is highly expressed and positively correlated with the immune-related factors CCL2 and PD-L1. SLC11A1 induced CCL2 and PD-L1 expression, thereby activating the JAK/STAT3 pathway. SLC11A1 deficiency constrained KIRC cell malignant phenotypes and immune response via regulating CCL2 and PD-L1-mediated JAK/STAT3 pathway, providing a promising therapeutic target for KIRC treatment.

Using machine learning methods to study the tumour microenvironment and its biomarkers in osteosarcoma metastasis

Background

The long-term prognosis for patients with osteosarcoma (OS) metastasis remains unfavourable, highlighting the urgent need for research that explores potential biomarkers using innovative methodologies.

Methods

This study explored potential biomarkers for OS metastasis by analysing data from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases. The synthetic minority oversampling technique (SMOTE) was employed to tackle class imbalances, while genes were selected using four feature selection algorithms (Monte Carlo feature selection [MCFS], Borota, minimum-redundancy maximum-relevance [mRMR], and light gradient-boosting machine [LightGBM]) based on the gene expression matrix. Four machine learning (ML) algorithms (support vector machine [SVM], extreme gradient boosting [XGBoost], random forest [RF], and k-nearest neighbours [kNN]) were utilized to determine the optimal number of genes for building the model. Interpretable machine learning (IML) was applied to construct prediction networks, revealing potential relationships among the selected genes. Additionally, enrichment analysis, survival analysis, and immune infiltration were performed on the featured genes.

Results

In DS1, DS2, and DS3, the IML algorithm identified 53, 45, and 46 features, respectively. Using the merged gene set, we obtained a total of 79 interpretable prediction rules for OS metastasis. We subsequently conducted an in-depth investigation on 39 crucial molecules associated with predicting OS metastasis, elucidating their roles within the tumour microenvironment. Importantly, we found that certain genes act as both predictors and differentially expressed genes. Finally, our study unveiled statistically significant differences in survival between the high and low expression groups of TRIP4, S100A9, SELL and SLC11A1, and there was a certain correlation between these genes and 22 various immune cells.

Conclusions

The biomarkers discovered in this study hold significant implications for personalized therapies, potentially enhancing the clinical prognosis of patients with OS.

Constructing and validating a risk model based on neutrophil-related genes for evaluating prognosis and guiding immunotherapy in colon cancer

BACKGROUND

Colon cancer is one of the most common digestive tract malignancies. Although immunotherapy has brought new hope to colon cancer patients, there is still a large proportion of patients who do not benefit from immunotherapy. Studies have shown that neutrophils can interact with immune cells and immune factors to affect the prognosis of patients.

METHODS

We first determined the infiltration level of neutrophils in tumors using the CIBERSORT algorithm and identified key genes in the final risk model by Spearman correlation analysis and subsequent Cox analysis. The risk score of each patient was obtained by multiplying the Cox regression coefficient and the gene expression level, and patients were divided into two groups based on the median of risk score. Differences in overall survival (OS) and progression-free survival (PFS) were assessed by Kaplan-Meier survival analysis, and model accuracy was validated in independent dataset. Differences in immune infiltration and immunotherapy were evaluated by immunoassay. Finally, immunohistochemistry and western blotting were performed to verify the expression of the three genes in the colon normal and tumor tissues.

RESULTS

We established and validated a risk scoring model based on neutrophil-related genes in two independent datasets, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, with SLC11A1 and SLC2A3 as risk factors and MMP3 as a protective factor. A new nomogram was constructed and validated by combining clinical characteristics and the risk score model to better predict patients OS and PFS. Immune analysis showed that patients in the high-risk group had immune cell infiltration level, immune checkpoint level and tumor mutational burden, and were more likely to benefit from immunotherapy.

CONCLUSIONS

The low-risk group showed better OS and PFS than the high-risk group in the neutrophil-related gene-based risk model. Patients in the high-risk group presented higher immune infiltration levels and tumor mutational burden and thus may be more responsive to immunotherapy.

Immune-related gene-based model predicts the survival of colorectal carcinoma and reflected various biological statuses

Bakcground: Prognosis of colorectal cancer (CRC) varies due to complex genetic-microenviromental interactions, and multiple gene-based prognostic models have been highlighted. Material and Method: In this work, the immune-related genes' expression-based model was developed and the scores of each sample were calculated. The correlation between the model and clinical information, immune infiltration, drug response and biological pathways were analyzed. Results: The high-score samples have a significantly longer survival (overall survival and progression-free survival) period than those with a low score, which was validated across seven datasets containing 1,325 samples (GSE17536 (N = 115), GSE17537 (N = 55), GSE33113 (N = 90), GSE37892 (N = 130), GSE38832 (N = 74), GSE39582 (N = 481), and TCGA (N = 380)). The score is significantly associated with clinical indicators, including age and stage, and further associated with PD-1/PD-L1 gene expression. Furthermore, high-score samples have significantly higher APC and a lower MUC5B mutation rate. The high-score samples show more immune infiltration (including CD4+ and CD8+ T cells, M1/M2 macrophages, and NK cells). Enriched pathway analyses showed that cancer-related pathways, including immune-related pathways, were significantly activated in high-score samples and that some drugs have significantly lower IC50 values than those with low score. Conclusion: The model developed based on immune-related genes is robust and reflected various statuses of CRC and may be a potential clinical indicator.