Down-regulated Solute Carrier Family 4 Member 4 Predicts Poor Progression in Colorectal Cancer

Targeting SLC4A4: A Novel Approach in Colorectal Cancer Drug Repurposing

BACKGROUND

Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, particularly in relation to tumor acidity and immune response.

METHODS

The study utilized computational approaches, including receptor-based virtual screening and high-throughput docking, to identify potential SLC4A4 inhibitors. A model of the human SLC4A4 structure was generated based on CryoEM data (PDB ID 6CAA), and drug candidates from the DrugBank database were evaluated using two computational tools (DrugRep and CB-DOCK2).

RESULTS

The study identified the compound (5R)-N-[(1r)-3-(4-hydroxyphenyl)butanoyl]-2-decanamide (DB07991) as the best ligand, demonstrating favorable binding affinity and stability. Molecular dynamics simulations revealed strong protein-ligand interactions with consistent RMSD (~0.25 nm), RMSF (~0.5 nm), compact Rg (4.0-3.9 nm), and stable SASA profiles, indicating that the SLC4A4 structure remains stable upon ligand binding.

CONCLUSIONS

The findings suggest that DB07991 is a promising drug candidate for further investigation as a therapeutic agent against CRC, particularly for targeting SLC4A4. This study highlights the potential of computational drug repositioning in identifying effective treatments for colorectal cancer.

Analysis of SLC genes alternative splicing identifies the SLC7A6 RI isoform as a therapeutic target for colorectal cancer

Alternative splicing (AS), a crucial mechanism in post-transcriptional regulation, has been implicated in diverse cancer processes. Several splicing variants of solute carrier (SLC) transporters reportedly play pivotal roles in tumorigenesis and tumor development. However, an in-depth analysis of AS landscapes of SLCs in colon adenocarcinoma (COAD) is lacking. Herein, we analyzed data from The Cancer Genome Atlas and identified 1215 AS events across 243 SLC genes, including 109 differentially expressed AS (DEAS) events involving 62 SLC genes in COAD. Differentially spliced SLCs were enriched in biological processes, including transmembrane transporter activity, transporter activity, ferroptosis, and choline metabolism. In patients with COAD, tumor tissues exhibited higher expression of longer mitochondrial carrier SLC25A16 isoforms than adjacent normal tissues, consistent with bioinformatics analysis. Protein-coding sequences and transmembrane helices of survival-related DEAS were predicted, revealing that shifts in splicing sites altered the number and structure of their transmembrane proteins. We developed a prognostic risk model based on the screened 6-SLC-AS (SLC7A6_RI_37208 (SLC7A6-RI), SLC11A2_AP_21724, SLC2A8_ES_87631, SLC35B1_AA_42317, SLC39A11_AD_43204, and SLC7A8_AP_26712). Knockdown of the intronic region of SLC7A6-RI isoform enhanced colon cancer cell proliferation. In vivo, knockdown of the intronic region of SLC7A6-RI isoform enhanced tumor growth in colon cancer. Mechanistically, si-SLC7A6-RI isoform exerted oncogenic effects by activating the PI3K-Akt-mTOR signaling pathway and promoting cell proliferation, evidenced by increased expression of key regulators Phosphorylated Mammalian Target of Rapamycin (p-mTOR) and a cell proliferation marker Proliferating Cell Nuclear Antigen (PCNA) using western blotting. Our study elucidated SLC-AS in COAD, highlighting its potential as a prognostic and therapeutic target and emphasizing the suppressive influence of SLC7A6-RI in colon cancer progression.

Proteomic profiling of tumour tissue-derived extracellular vesicles in colon cancer

Colon cancer is one of the most commonly occurring tumours among both women and men, and over the past decades the incidence has been on the rise. As such, the need for biomarker identification as well as an understanding of the underlying disease mechanism has never been greater. Extracellular vesicles are integral mediators of cell-to-cell communication and offer a unique opportunity to study the machinery that drives disease progression, and they also function as vectors for potential biomarkers. Tumour tissue and healthy mucosal tissue from the colons of ten patients were used to isolate tissue-resident EVs that were subsequently subjected to global quantitative proteomic analysis through LC-MS/MS. In total, more than 2000 proteins were identified, with most of the common EV markers being among them. Bioinformatics revealed a clear underrepresentation of proteins involved in energy production and cellular adhesion in tumour EVs, while proteins involved in protein biosynthesis were overrepresented. Additionally, 53 membrane proteins were found to be significantly upregulated in tumour EVs. Among them were several proteins with enzymatic functions that degrade the extracellular matrix, and three of these, Fibroblast activating factor (FAP), Cell surface hyaluronidase (CEMIP2), as well as Ephrin receptor B3 (EPHB3), were validated and found to be consistent with the global quantitative results. These stark differences in the proteomes between healthy and cancerous tissue emphasise the importance of the interstitial vesicle secretome as a major player of disease development.

In silico analysis of the solute carrier (SLC) family in cancer indicates a link among DNA methylation, metabolic adaptation, drug response, and immune reactivity

Introduction: The oncogenic transformation is driven by genetic and epigenetic alterations influencing cancer cell fate. These alterations also result in metabolic reprogramming by modulating the expression of membrane Solute Carrier (SLC) transporters involved in biomolecules trafficking. SLCs act as tumor suppressors or promoters influencing cancer methylome, tumor growth, immune-escape, and chemoresistance. Methods: This in silico study aimed to identify the deregulated SLCs in various tumor types compared to normal tissues by analyzing the TCGA Target GTEx dataset. Furthermore, the relationship between SLCs expression and the most relevant tumor features was tackled along with their genetic regulation mediated by DNA methylation. Results: We identified 62 differentially expressed SLCs, including the downregulated SLC25A27 and SLC17A7, as well as the upregulated SLC27A2 and SLC12A8. Notably, SLC4A4 and SLC7A11 expression was associated with favorable and unfavorable outcome, respectively. Moreover, SLC6A14, SLC34A2, and SLC1A2 were linked to tumor immune responsiveness. Interestingly, SLC24A5 and SLC45A2 positively correlated with anti-MEK and anti-RAF sensitivity. The expression of relevant SLCs was correlated with hypo- and hyper-methylation of promoter and body region, showing an established DNA methylation pattern. Noteworthy, the positive association of cg06690548 (SLC7A11) methylation with cancer outcome suggests the independent predictive role of DNA methylation at a single nucleotide resolution. Discussion: Although our in silico overview revealed a wide heterogeneity depending on different SLCs functions and tumor types, we identified key SLCs and pointed out the role of DNA methylation as regulatory mechanism of their expression. Overall, these findings deserve further studies to identify novel cancer biomarkers and promising therapeutic targets.

A novel inflammation-related signature for predicting prognosis and characterizing the tumor microenvironment in colorectal cancer

Inflammation is a critical component of tumor progression, and it modifies the tumor microenvironment by various mechanisms. Here, we explore the effect of the inflammatory response on the tumor microenvironment in colorectal cancer (CRC). A prognostic signature consisting of inflammation-related genes (IRGs) was constructed and verified based on the inflammatory response by bioinformatics analysis. IRG risk model was identified as an independent prognostic factor in CRC, and was related to biological processes of extracellular matrix, cell adhesion and angiogenesis. The IRG risk score predicted the clinical benefit of ipilimumab. Weighted correlation network analysis identified TIMP1 as the hub gene of the inflammatory response in the IRG risk model. Coculture experiments with macrophages and CRC cells revealed that TIMP1 promoted macrophage migration, inhibited the expression of M1 markers (CD11C and CD80), and promoted the expression of M2 markers (ARG1 and CD163). TIMP1 promoted the expression of ICAM1 and CCL2 by activating the ERK1/2 signaling pathway to promote macrophage migration and M2-like polarization. These IRGs in the risk model regulated stromal and immune components in the tumor microenvironment and could serve as potential therapeutic targets in CRC. TIMP1 promoted macrophage migration and meditated macrophage M2 polarization by activating ERK1/2/CLAM1 and CCL2.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Distinguishing among HCO 3- , CO 3= , and H + as Substrates of Proteins That Appear To Be "Bicarbonate" Transporters

BACKGROUND

Differentiating among HCO 3- , CO 3= , and H + movements across membranes has long seemed impossible. We now seek to discriminate unambiguously among three alternate mechanisms: the inward flux of 2 HCO 3- (mechanism 1), the inward flux of 1 CO 3= (mechanism 2), and the CO 2 /HCO 3- -stimulated outward flux of 2 H + (mechanism 3).

METHODS

As a test case, we use electrophysiology and heterologous expression in Xenopus oocytes to examine SLC4 family members that appear to transport "bicarbonate" ("HCO 3- ").

RESULTS

First, we note that cell-surface carbonic anhydrase should catalyze the forward reaction CO 2 +OH - →HCO 3- if HCO 3- is the substrate; if it is not, the reverse reaction should occur. Monitoring changes in cell-surface pH ( Δ pH S ) with or without cell-surface carbonic anhydrase, we find that the presumed Cl-"HCO 3 " exchanger AE1 (SLC4A1) does indeed transport HCO 3- (mechanism 1) as long supposed, whereas the electrogenic Na/"HCO 3 " cotransporter NBCe1 (SLC4A4) and the electroneutral Na + -driven Cl-"HCO 3 " exchanger NDCBE (SLC4A8) do not. Second, we use mathematical simulations to show that each of the three mechanisms generates unique quantities of H + at the cell surface (measured as Δ pH S ) per charge transported (measured as change in membrane current, ΔIm ). Calibrating ΔpH S /Δ Im in oocytes expressing the H + channel H V 1, we find that our NBCe1 data align closely with predictions of CO 3= transport (mechanism 2), while ruling out HCO 3- (mechanism 1) and CO 2 /HCO 3- -stimulated H + transport (mechanism 3).

CONCLUSIONS

Our surface chemistry approach makes it possible for the first time to distinguish among HCO 3- , CO 3= , and H + fluxes, thereby providing insight into molecular actions of clinically relevant acid-base transporters and carbonic-anhydrase inhibitors.

Experimental validation of in silico analysis estimated the reverse effect of upregulated hsa‐miR ‐106a‐5p and hsa‐miR ‐223‐3p on SLC4A4 gene expression in Iranian patients with colorectal adenocarcinoma by RT‐qPCR

BACKGROUND AND METHODS

Colorectal cancer (CRC) is considered one of the most common malignancies worldwide. The diagnosis and prognosis of the patients are very poor. In this study, we used in-silico analysis and experimental techniques to investigate novel co-expression genes and their associated miRNA networks in CRC. For this purpose, we conducted a comprehensive transcriptome analysis using online bulk and single-cell RNA-seq datasets. We then validated the results on tissue samples from cancerous and adjacent normal tissues from CRC patients by RT-qPCR.

RESULTS

Using a weighted gene co-expression network algorithm, we identified SLC4A4 as a significantly downregulated hub gene in the CRC. The single-cell analysis indicated that the expression level of SLC4A4 in Paneth cells is higher than in other cell populations. Further computational analysis suggested hsa-miR-223-3p and hsa-miR-106a-5p as two specific hub-miRNAs for the SLC4A4 gene. RT-qPCR analysis showed a 2.60-fold downregulation of SLC4A4. Moreover, hsa-miR-223-3p and hsa-miR-106a-5p showed an increased expression level of 5.58-fold and 9.66-fold in CRC samples, respectively. Based on the marginal model analysis, by increasing the expression of hsa-miR-106a-5p, the average expression of the SLC4A4 gene significantly decreased by 103 units. Furthermore, ROC curves analysis indicated statistically significant for diagnostic ability of SLC4A4 (AUC: 0.94, Sensitivity: 95.5%, Specificity: 95.5%) and hsa-miR-106a-5p (AUC: 0.72, Sensitivity: 72.7%, Specificity: 100%).

CONCLUSION

This study provides a framework of co-expression gene modules and miRNAs of CRC, which identifies some important biomarkers for CRC pathogenicity and diagnosis. Further experimental evidence will be required to support this study and validate the precise molecular pathways.

Downregulation of LRRC19 Is Associated with Poor Prognosis in Colorectal Cancer

OBJECTIVE

Colorectal cancer (CRC) is globally one of the most often diagnosed cancers with high mortality rates. This study aimed to explore novel biomarkers for the diagnosis and prognosis of CRC.

METHODS

We collected 4 datasets about CRC in GEO and sought differentially expressed genes (DEGs) with GEO2R. Leucine-rich repeat-containing protein 19 (LRRC19) expression was assessed through the Oncomine and TIMER database analyses, which was further confirmed by qRT-PCR of CRC samples. We used online survival analysis tools (GEPIA, PrognoScan, and Kaplan-Meier plotter) to examine the prognostic value of LRRC19 in CRC and other malignancies. GO and KEGG enrichment analyses were employed to explore the biological functions of LRRC19. Finally, we conducted network prediction by STRING and further validation on the GEPIA to discover other molecules that might interact with LRRC19.

RESULTS

A total of 21 upregulated and 46 downregulated DEGs were identified from the 4 datasets. The TIMER and Oncomine online analyses showed lower mRNA of LRRC19 in CRC tissues compared with adjacent normal tissues, which was validated by qRT-PCR in CRC patient samples. The survival analysis through the GEPIA and PrognoScan websites revealed that low LRRC19 expression was significantly correlated with poor prognosis in CRC patients. The Kaplan-Meier plotter survival analysis indicated that low LRRC19 expression was significantly associated with the disease progression of patients with ovarian cancer, gastric cancer, breast cancer, and lung cancer. The enrichment analysis suggested that low expression of LRRC19 could be involved in the retinol metabolism and the zymogen granule membrane. Through STRING and GEPIA, it was found that LRRC19 is clearly associated with ZCCHC10, MOB3B, IMMP2L, and TRMT11.

CONCLUSION

LRRC19 mRNA was prominently decreased in human CRC tissues and was significantly associated with shorter survival in CRC patients. LRRC19 might serve as a possible target for early diagnosis and prognosis assessment in CRC.

Solute carrier family 16 member 5 downregulation and its methylation might serve as a prognostic indicator of prostate cancer

Prostate cancer (PCa), characterized by high invasion, metastasis, and recurrence, is the most prevalent malignant tumor in men worldwide. A clear understanding of the underlying molecular mechanisms and their role during PCa tumorigenesis can help develop prognostic and targeted therapies. We analyzed datasets from public databases, including the Cancer Genome Atlas (TCGA) and Oncomine and Gene Expression Profiling Interactive Analysis for differential expression of solute carrier family 16 member 5 (SLC16A5). We further investigated its relationship with clinical stage, pathological grade, and prognosis of PCa. The promoter methylation level of SLC16A5 in PCa was also investigated by UALCAN. We also utilized datasets from UCSC Xena to explore the prognostic role of SLC16A5 methylation levels and CpG site. Correlations between SLC16A5 and immune infiltration were discovered through TIMER. We observed significantly lower levels of SLC16A5 mRNA in PCa relative to normal tissues across six datasets from Oncomine database (p < .001) and 498 cases from TCGA database (p < .0001). SLC16A5 is strongly negatively regulated by its DNA methylation, with a Spearman of -0.81 and Pearson of -0.80 (p < .001). The aberrant SLC16A5 expression resulted in a significant relationship with clinical stage, pathological grade, and lower SLC16A5 mRNA expression, and its hypermethylation was related to a poorer PCa prognosis. SLC16A5 acted as an important factor for PCa diagnosis, with an AUC of 0.9038 (95% CI: 0.8597-0.9479; p < .0001). Besides, the aberrant SLC16A5 expression revealed close correlations with multiple immune cells. Overall, these results indicate that decreased SLC16A5 expression might be a potential biomarker for determining prognosis and immune infiltration in PCa. The positive SLC16A5 modulation might be a promising therapeutic target for PCa.

Identification and Validation of a Novel Inflammatory Response-Related Gene Signature for the Prognosis of Colon Cancer

Purpose

The inflammatory response plays a crucial role in the occurrence and development of colon cancer. In this study, we aimed to explore a novel prognostic model for patients with colon cancer (COAD) based on inflammatory response-related genes.

Methods

Inflammatory response-related genes were obtained from Molecular Signatures database. Univariate and multivariate Cox regression analyses were used for model construction based on TCGA dataset. GSE39582 dataset and qRT-PCR dataset were used for validation. Gene set variation analysis and gene set enrichment analysis were performed to explore the potential regulatory pathways. The immune cell infiltration level was analyzed via CIBERSORT. Immunohistochemistry analysis and experiments were used to explore the function of genes in model.

Results

In this study, a novel prognostic signature was identified using stepwise Cox proportional hazards regression analysis based on TCGA dataset. The results were subsequently validated in 562 patients from GSE39582 and a qRT-PCR data set from 70 tumor samples. Functional analysis indicated that the tumor microenvironment and immune cell infiltrate were different between high- and low-risk groups. Additionally, IHC results showed that the protein levels of prognostic genes were significantly different between COAD tissues and adjacent non-tumorous tissues, and prognostic genes could regulate the malignant phenotype of COAD cells.

Conclusion

Overall, the inflammation-related gene signature can be used for prognostic prediction in patients with COAD.

Identification of gene biomarkers and immune cell infiltration characteristics in rectal cancer

BACKGROUND

Compared with colon cancer, the increase of morbidity is more significant for rectal cancer. The current study set out to identify novel and critical biomarkers or features that may be used as promising targets for early diagnosis and treatment monitoring of rectal cancer.

METHODS

Microarray datasets of rectal cancer with a minimum sample size of 30 and RNA-sequencing datasets of rectal adenocarcinoma (READ) were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The method of robust rank aggregation was utilized to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was structured using the STRING platform, and hub genes were identified using the Cytoscape plugin cytoHubba and an UpSet diagram. R software was employed to perform functional enrichment analysis. Receiver operating characteristic (ROC) curves based on the GEO data and Kaplan-Meier curves based on the TCGA data were drawn to assess the diagnostic and prognostic values of the hub genes. Immune cell infiltration analysis was conducted with CIBERSORT, and the diagnostic value and correlations between prognostic genes and infiltrated immune cells were analyzed by principal component analysis (PCA), ROC curves, and correlation scatter plots.

RESULTS

A total of 137 robust DEGs were obtained by integrating datasets in GEO. Twenty-four hub genes, including CHGA, TTR, SAA1, SPP1, MMP1, TGFBI, COL1A1, and PCK1, were identified as a diagnostic gene biomarker group for rectal cancer, and SAA1, SPP1, and SI were identified as potential novel prognostic biomarkers. Functionally, the hub genes were mainly involved in the rectal cancer related interleukin (IL)-17 and proximal tubule bicarbonate reclamation pathways. Twelve sensitive infiltrated immune cells were identified, and were correlated with prognostic genes.

CONCLUSIONS

The integrated gene biomarker group combined with immune cell infiltration can effectively indicate rectal cancer.