Identification and clinical validation of key genes as the potential biomarkers in colorectal adenoma

Revealing the role of serum exosomal novel long non-coding RNA NAMPT-AS as a promising diagnostic/prognostic biomarker in colorectal cancer patients

AIMS

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Nicotinamide phosphoribosyl-transferase (NAMPT) was found to be over-expressed in several cancers including CRC. NAMPT-Antisense (NAMPT-AS) is a novel long non-coding RNA (lncRNA) recently reported to be associated with triple negative breast cancer. However, its role in CRC has not been investigated. This study was designed to explore the role of lncRNA NAMPT-AS in CRC, and to investigate its circulating serum exosomal levels in subjects with/without CRC.

MAIN METHODS

We analyzed CRC patients' data in The Cancer Genome Atlas (TCGA). LncRNA NAMPT-AS and NAMPT mRNA levels were measured in serum exosomes isolated from CRC patients and healthy control subjects and were also measured in CRC-tissues using qRT-PCR. Serum NAMPT protein levels were measured by ELISA, and immunohistochemical analyses were done for NAMPT and Ki67 in CRC tissues.

KEY FINDINGS

Serum exosomal NAMPT-AS levels were found to be significantly higher in CRC patients compared to control subjects and significantly positively correlated with serum exosomal NAMPT mRNA and circulating NAMPT protein. Tissue NAMPT-AS was found to be significantly positively associated with tissue and serum exosomal NAMPT levels. Higher serum exosomal NAMPT-AS levels were found to be associated with higher susceptibility for CRC. Gene-ontology results and survival analysis of TCGA-data showed a potential classification of CRC samples based on NAMPT-AS levels and association of NAMPT-AS upregulation with poor CRC prognosis and survival.

SIGNIFICANCE

These results portray NAMPT-AS as a novel potential diagnostic/prognostic biomarker and key molecular mediator in CRC.

Identification of mitochondria-related gene biomarkers associated with immune infiltration in acute myocardial infarction

Mitochondrial dysfunction has been known to contribute to the worsening of acute myocardial infarction (AMI). We screened differentially expressed genes (DEGs) between AMI and healthy individuals based on the GSE66360 dataset. We took the intersection of the obtained DEGs with 1,136 mitochondria-related genes. Finally, we screened out mitochondria-related DEGs (MitoDEGs). Eight MitoDEGs were identified as hub genes based on the random forest algorithm. Two mitochondria-related robust molecular clusters were identified by consensus clustering. Immune infiltration analysis showed that immune cell infiltration was significantly increased in the high-expression group of MitoDEGs. We obtained the potential drugs targeted at ALDH2, PMAIP1, and BCL2A1, such as disulfiram, obatoclax mesylate, and bortezomib. Quantitative reverse-transcription polymerase chain reaction further validated the expression of the MitoDEGs in the cell model of AMI. These findings reveal the potential role of MitoDEGs in AMI and provide new insights into risk stratification and individualized treatment of AMI patients.

The impact of SLC10A3 on prognosis and immune microenvironment in colorectal adenocarcinoma

BACKGROUND

SLC10A3, a gene upregulated in pan-cancer, lacks full understanding regarding its prognostic implications and association with immune infiltration in colorectal cancer (CRC). This study comprehensively analyzed SLC10A3 in CRC, evaluating its prognostic significance and influence on the tumor's immune microenvironment.

METHODS

Transcriptomic data from TCGA were obtained to compare SLC10A3 expression in both colorectal cancer (CRC) and normal tissues. Prognostic value was assessed for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). DNA methylation patterns of SLC10A3 and correlation with DNA mismatch repair (MMR) were explored. Genetic alterations in SLC10A3 were scrutinized. The study also delved into the influence of SLC10A3 on the immune microenvironment of CRC, including immune cell infiltration and chemokines. Involvement of cancer-associated fibroblasts (CAFs) was explored. Methylation status of specific CpG islands in the SLC10A3 gene correlated with CRC patient prognosis. CRC tissue microarray was performed to verify the expression of SLC10A3 and its relationship with prognosis.

RESULTS

The research revealed that SLC10A3 is significantly upregulated in CRC and holds promise as a potential diagnostic marker. Elevated SLC10A3 expression was linked to poorer OS, DSS, and PFI. Methylation patterns of SLC10A3 displayed prognostic relevance, and genetic alterations in the gene were identified. SLC10A3 was shown to impact the immune microenvironment, with significant correlations observed between its expression and various immune cell types, chemokines, and markers associated with CAFs. Furthermore, an inverse relationship between SLC10A3 and MMR molecules was established. Methylation status of specific CpG islands within the SLC10A3 gene was associated with CRC patient prognosis. Tissue microarray showed that SLC10A3 was highly expressed in CRC and significantly correlated with poor prognosis.

CONCLUSION

The study underscores the importance of elevated SLC10A3 in CRC, associating it with decreased survival and immune infiltration, proposing it as a diagnostic biomarker and appealing immunotherapy target, given its significant overexpression and influence on the immune microenvironment and prognosis through methylation patterns.

Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.