The SLC Family Are Candidate Diagnostic and Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Cancer Therapeutic Potential and Prognostic Value of the SLC25 Mitochondrial Carrier Family: A Review

Transporters of the solute carrier family 25 (SLC25) regulate the intracellular distribution and concentration of nucleotides, amino acids, dicarboxylates, and vitamins within the mitochondrial and cytoplasmic matrices. This mechanism involves changes in mitochondrial function, regulation of cellular metabolism, and the ability to provide energy. In this review, important members of the SLC25 family and their pathways affecting tumorigenesis and progression are elucidated, highlighting the diversity and complexity of these pathways. Furthermore, the significant potential of the members of SLC25 as both cancer therapeutic targets and biomarkers will be emphasized.

Lipid metabolism-related gene expression in the immune microenvironment predicts prognostic outcomes in renal cell carcinoma

Background

Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases.

Methods

The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays.

Results

Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration.

Conclusion

In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.

Reprogramming of glucose metabolism via PFKFB4 is critical in FGF16-driven invasion of breast cancer cells

Fibroblast growth factors (FGFs) are expressed in both developing and adult tissues and play important roles in embryogenesis, tissue homeostasis, angiogenesis, and neoplastic transformation. Here, we report the elevated expression of FGF16 in human breast tumor and investigate its potential involvement in breast cancer progression. The onset of epithelial-mesenchymal transition (EMT), a prerequisite for cancer metastasis, was observed in human mammary epithelial cell-line MCF10A by FGF16. Further study unveiled that FGF16 alters mRNA expression of a set of extracellular matrix genes to promote cellular invasion. Cancer cells undergoing EMT often show metabolic alteration to sustain their continuous proliferation and energy-intensive migration. Similarly, FGF16 induced a significant metabolic shift toward aerobic glycolysis. At the molecular level, FGF16 enhanced GLUT3 expression to facilitate glucose transport into cells, which through aerobic glycolysis generates lactate. The bi-functional protein, 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase 4 (PFKFB4) was found to be a mediator in FGF16-driven glycolysis and subsequent invasion. Furthermore, PFKFB4 was found to play a critical role in promoting lactate-induced cell invasion since silencing PFKFB4 decreased lactate level and rendered the cells less invasive. These findings support potential clinical intervention of any of the members of FGF16-GLUT3-PFKFB4 axis to control the invasion of breast cancer cells.

Treatment strategies for clear cell renal cell carcinoma: Past, present and future

Clear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of kidney cancer, which is prone to metastasis, recurrence, and resistance to radiotherapy and chemotherapy. The burden it places on human health due to its refractory nature and rising incidence rate is substantial. Researchers have recently determined the ccRCC risk factors and optimized the clinical therapy based on the disease's underlying molecular mechanisms. In this paper, we review the established clinical therapies and novel potential therapeutic approaches for ccRCC, and we support the importance of investigating novel therapeutic options in the context of combining established therapies as a research hotspot, with the goal of providing diversified therapeutic options that promise to address the issue of drug resistance, with a view to the early realization of precision medicine and individualized treatment.

Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis

Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What’s more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.

Renal oncometabolite L-2-hydroxyglutarate imposes a block in kidney tubulogenesis: Evidence for an epigenetic basis for the L-2HG-induced impairment of differentiation

2-Hydroxyglutarate (2HG) overproducing tumors arise in a number of tissues, including the kidney. The tumorigenesis resulting from overproduced 2HG has been attributed to the ability of 2HG alter gene expression by inhibiting α-ketoglutarate (αKG)-dependent dioxygenases, including Ten-eleven-Translocation (TET) enzymes. Genes that regulate cellular differentiation are reportedly repressed, blocking differentiation of mesenchymal cells into myocytes, and adipocytes. In this report, the expression of the enzyme responsible for L2HG degradation, L-2HG dehydrogenase (L2HGDH), is knocked down, using lentiviral shRNA, as well as siRNA, in primary cultures of normal Renal Proximal Tubule (RPT) cells. The knockdown (KD) results in increased L-2HG levels, decreased demethylation of 5mC in genomic DNA, and increased methylation of H3 Histones. Consequences include reduced tubulogenesis by RPT cells in matrigel, and reduced expression of molecular markers of differentiation, including membrane transporters as well as HNF1α and HNF1β, which regulate their transcription. These results are consistent with the hypothesis that oncometabolite 2HG blocks RPT differentiation by altering the methylation status of chromatin in a manner that impedes the transcriptional events required for normal differentiation. Presumably, similar alterations are responsible for promoting the expansion of renal cancer stem-cells, increasing their propensity for malignant transformation.

An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a frequent malignancy of the digestive system with a poor prognosis and high mortality rate worldwide. Intratumor heterogeneity (ITH) is associated with tumor progression, poor prognosis, immunosuppression, and therapy resistance. However, the relationship between ITH and prognosis, the immune microenvironment, and the chemotherapy response in COAD patients remains unknown, and this knowledge is urgently needed.

Methods

We obtained clinical information and gene expression data for COAD patients from The Cancer Genome Atlas (TCGA) database. The DEPTH2 algorithm was utilized to evaluate the ITH score. X-tile software was used to determine the optimal cutoff value of the ITH score. The COAD patients were divided into high- and low-ITH groups based on the cutoff value. We analyzed prognosis, tumor mutation burden (TMB), gene mutations, and immune checkpoint expression between the high- and low-ITH groups. Differentially expressed genes (DEGs) in the high- and low-ITH groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to screen the prognosis-related genes for the construction of an ITH-related prognostic signature. The nomogram was used to predict the overall survival (OS) of COAD patients. The protein–protein interaction (PPI) network was constructed by using the GeneMANIA database. Principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were employed to explore the differences in biological pathway activation status between the high- and low-risk groups. The proportion and type of tumor-infiltrating immune cells were evaluated by the CIBERSORT and ESTIMATE algorithms. Additionally, we assessed the chemotherapy response and predicted small-molecule drugs for treatment. Finally, the expression of the prognosis-related genes was validated by using the UALCAN database and Human Protein Atlas (HPA) database.

Results

The OS of the high-ITH group was worse than that of the low-ITH group. A positive correlation between ITH and TMB was identified. In subgroups stratified by age, gender, and tumor stage, the OS of the low-ITH group remained better than that of the high-ITH group. There were dramatic differences in the mutated genes, single nucleotide variant classes, variant types, immune checkpoints and cooccurring and mutually exclusive mutations of the DEGs between the high- and low-ITH groups. Based on the DEGs between the high- and low-ITH groups, we constructed a five-gene signature consisting of CEACAM5, ENO2, GABBR1, MC1R, and SLC44A4. The COAD patients were divided into high- and low-risk groups according to the median risk score. The OS of the high-risk group was worse than that of the low-risk group. The nomogram was used to accurately predict the 1-, 3- and 5-year OS of COAD patients and showed good calibration and moderate discrimination ability. The stromal score, immune score, and ESTIMATE score of the high-risk group were significantly higher than those of the low-risk group, whereas tumor purity showed the opposite trend. The patients classified by the risk score had distinguishable sensitivity to chemotherapeutic drugs. Finally, two public databases confirmed that CEACAM5 and SLC44A4 were upregulated in normal tissues compared with COAD tissues, and ENO2, GABBR1, and MC1R were upregulated in COAD tissues compared with normal tissues.

Conclusion

Overall, we identified an ITH-related prognostic signature for COAD that was closely related to the tumor microenvironment and chemotherapy response. This signature may help clinicians make more personalized and precise treatment decisions for COAD patients.

Insights Into Long Non-Coding RNA and mRNA Expression in the Jejunum of Lambs Challenged With Escherichia coli F17

It has long been recognized that enterotoxigenic Escherichia coli (ETEC) is the major pathogen responsible for vomiting and diarrhea. E. coli F17, a main subtype of ETEC, is characterized by high morbidity and mortality in young livestock. However, the transcriptomic basis underlying E. coli F17 infection has not been fully understood. In the present study, RNA sequencing was conducted to explore the expression profiles of mRNAs and long non-coding RNAs (lncRNAs) in the jejunum of lambs who were identified as resistant or sensitive to E. coli F17 that was obtained in a challenge experiment. A total of 772 differentially expressed (DE) mRNAs and 190 DE lncRNAs were detected between the E. coli F17—resistance and E. coli F17-sensitive lambs (i.e., TFF2, LOC105606142, OLFM4, LYPD8, REG4, APOA4, TCONS_00223467, and TCONS_00241897). Then, a two-step machine learning approach (RX) combination Random Forest and Extreme Gradient Boosting were performed, which identified 16 mRNAs and 17 lncRNAs as potential biomarkers, within which PPP2R3A and TCONS_00182693 were prioritized as key biomarkers involved in E. coli F17 infection. Furthermore, functional enrichment analysis showed that peroxisome proliferator-activated receptor (PPAR) pathway was significantly enriched in response to E. coli F17 infection. Our finding will help to improve the knowledge of the mechanisms underlying E. coli F17 infection and may provide novel targets for future treatment of E. coli F17 infection.

An integrated bioinformatic investigation of mitochondrial solute carrier family 25 (SLC25) in colon cancer followed by preliminary validation of member 5 (SLC25A5) in tumorigenesis

Solute carrier family 25 (SLC25) encodes transport proteins at the inner mitochondrial membrane and functions as carriers for metabolites. Although SLC25 genetic variants correlate with human metabolic diseases, their roles in colon cancer remain unknown. Cases of colon cancer were retrieved from The Cancer Genome Atlas, and the transcriptionally differentially expressed members (DEMs) of SLC25 were identified. DNA level alterations, clinicopathological characteristics, and clinical survival were also investigated. A risk score model based on the DEMs was constructed to further evaluate their prognostic values in a clinical setting. The results were preliminarily validated using bioinformatic analysis of datasets from the Gene Expression Omnibus, immunohistochemical evaluations in clinical specimens, and functional experiments in colon cancer-derived cell lines. Thirty-seven DEMs were identified among 53 members of SLC25. Eight of 37 DEMs were introduced into a risk score model using integrated LASSO regression and multivariate Cox regression. Validated by GSE395282 and GSE175356, DEMs with high-risk scores were associated with the phenotypes of increasing tumor immune infiltration and decreasing glycolysis and apoptosis contents. SLC25A5 was downregulated in cancer, and its upregulation was related to better overall survival in patients from public datasets and in clinical cases. High SLC25A5 expression was an independent prognostic factor for 79 patients after surgical treatment. A negative correlation between CD8 and SLC25A5 was determined in specimens from 106 patients with advanced colon cancer. SLC25A5 attenuated cell proliferation, upregulated the expression of programmed cell death-related signatures, and exerted its biological function by inhibiting the MAPK signaling pathway. Our study reveals that mitochondrial SLC25 has prognostic value in patients with colon cancer. The bioinformatic analyses by following verification in situ and in vitro provide direction for further functional and mechanistic studies on the identified member of SLC25.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.

T and NK cell abundance defines two distinct subgroups of renal cell carcinoma

Renal cell carcinoma (RCC) is considered as an immunogenic cancer. Because not all patients respond to current immunotherapies, we aimed to investigate the immunological heterogeneity of RCC tumors. We analyzedthe immunophenotype of the circulating, tumor, and matching adjacent healthy kidney immune cells from 52 nephrectomy patients with multi-parameter flow cytometry. Additionally, we studied the transcriptomic and mutation profiles of 20 clear cell RCC (ccRCC) tumors with bulk RNA sequencing and a customized pan-cancer gene panel. The tumor samples clustered into two distinct subgroups defined by the abundance of intratumoral CD3+ T cells (CD3^high, 25/52) and NK cells (NK^high, 27/52). CD3^high tumors had an overall higher frequency of tumor infiltrating lymphocytes and PD-1 expression on the CD8+ T cells compared to NK^high tumors. The tumor infiltrating T and NK cells had significantly elevated expression levels of LAG-3, PD-1, and HLA-DR compared to the circulating immune cells. Transcriptomic analysis revealed increased immune signaling (IFN-γ, TNF-α via NF-κB, and T cell receptor signaling) and kidney metabolism pathways in the CD3^high subgroup. Genomic analysis confirmed the typical ccRCC mutation profile including VHL, PBRM1, and SETD2 mutations, and revealed PBRM1 as a uniquely mutated gene in the CD3^high subgroup. Approximately half of the RCC tumors have a high infiltration of NK cells associated with a lower number of tumor infiltrating lymphocytes, lower PD-1 expression, a distinct transcriptomic and mutation profile, providing insights to the immunological heterogeneity of RCC which may impact treatment responses to immunological therapies.

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.

METHODS

Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.

RESULTS

In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.

CONCLUSIONS

The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

The Heterogeneity Metabolism of Renal Cell Carcinomas

According to data from the American Cancer Society, cancer is one of the deadliest health problems globally. Annually, renal cell carcinoma (RCC) causes more than 100,000 deaths worldwide [1-4], posing an urgent need to develop effective treatments to increase patient survival outcomes. New therapies are expected to address a major factor contributing to cancer's resistance to standard therapies: oncogenic heterogeneity. Gene expression can vary tremendously among different types of cancers, different patients of the same tumor type, and even within individual tumors; various metabolic phenotypes can emerge, making singletherapy approaches insufficient. Novel strategies targeting the diverse metabolism of cancers aim to overcome this obstacle. Though some have yielded positive results, it remains a challenge to uncover all of the distinct metabolic profiles of RCC. In the quest to overcome this obstacle, the metabolic oriented research focusing on these cancers has offered freshly new perspectives, which are expected to contribute heavily to the development of new treatments.

The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes

ADME genes are a group of genes that are involved in drug absorption, distribution, metabolism, and excretion (ADME). The expression profiles of ADME genes within tumours is proposed to impact on cancer patient survival; however, this has not been systematically examined. In this study, our comprehensive analyses of pan-cancer datasets from the Cancer Genome Atlas (TCGA) revealed differential intratumoral expression profiles for ADME genes in 21 different cancer types. Most genes also showed high interindividual variability within cancer-specific patient cohorts. Using Kaplan-Meier plots and logrank tests, we showed that intratumoral expression levels of twenty of the thirty-two core ADME genes were associated with overall survival (OS) in these cancers. Of these genes, five showed significant association with unfavourable OS in three cancers, including SKCM (ABCC2, GSTP1), KIRC (CYP2D6, CYP2E1), PAAD (UGT2B7); sixteen showed significant associations with favourable OS in twelve cancers, including BLCA (UGT2B15), BRCA (CYP2D6), COAD (NAT1), HNSC (ABCB1), KIRC (ABCG2, CYP3A4, SLC22A2, SLC22A6), KIRP (SLC22A2), LIHC (CYP2C19, CYP2C8, CYP2C9, CYP3A5, SLC22A1), LUAD (SLC15A2), LUSC (UGT1A1), PAAD (ABCB1), SARC (ABCB1), and SKCM (ABCB1, DYPD). Overall, these data provide compelling evidence supporting ADME genes as prognostic biomarkers and potential therapeutic targets. We propose that intratumoral expression of ADME genes may impact cancer patient survival by multiple mechanisms that can include metabolizing/transporting anticancer drugs, activating anticancer drugs, and metabolizing/transporting a variety of endogenous molecules involved in metabolically fuelling cancer cells and/or controlling pro-growth signalling pathways.