Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

Dietary supplementation with Neolamarckia cadamba leaf extract improves broiler meat quality by enhancing antioxidant capacity and regulating metabolites

This study was to evaluate the effect of supplementing the diet of broilers with Neolamarckia cadamba leaf extract (NCLE) on meat quality by evaluating antioxidant parameters and the expression of genes in the p38 mitogen-activated protein kinase/nuclear factor-erythroid 2-related factor 2/antioxidant responsive element (p38 MAPK/Nrf2/ARE) signaling pathway, coupled with LC-MS-based metabolomic analysis. A total of 480 one-day-old male broilers were randomly allocated to four treatment groups-a control (CON) group, which was fed a basal diet, and three NCLE treatment groups, which were fed the basal diet supplemented with 100, 200, or 400 mg/kg NCLE (N1, N2, and N3 groups, respectively) for 42 d. Compared with the CON group, meat quality was improved in the N2 and N3 groups, as evidenced by the higher pH45min (P < 0.05) and lower shear force (P < 0.05) in breast muscle (BM) and lower drip loss at 48 h (P < 0.05) in leg muscle (LM). Moreover, BM antioxidant capacity was significantly enhanced in the N3 group, characterized by an increase in the total antioxidant capacity (T-AOC), the concentrations of glutathione peroxidase (GSH-Px) and catalase (CAT), and the relative mRNA expression of p38 MAPK, extracellular-signal regulated kinase (ERK1/2), c-Jun N-terminal kinase (JNK), Nrf2, CAT, and GSH-Px (P < 0.05). Similarly, LM in the N3 group displayed higher T-AOC, increased GSH-Px and CAT concentrations, reduced malonaldehyde contents (P < 0.05), and upregulation of the relative mRNA levels of JNK, Nrf2, heme oxygenase, CAT, and superoxide dismutase (SOD) (P < 0.05). Metabolomics analysis revealed that D-arabinono-1,4-lactone and lyso-PAF C-16-d4 were negatively correlated with shear force and cooking loss (P < 0.05) and displayed increased abundance in BM of the N3 group. L-Serine levels were upregulated while D-fructose 1,6-diphosphate contents were downregulated in the three NCLE groups. Finally, the differential metabolites in both BM and LM were involved in amino acid metabolism pathways. Our results indicated that NCLE supplementation improved meat quality by enhancing antioxidant enzyme activities, promoting the expression of genes in the p38 MAPK/Nrf2/ARE signaling pathway, and regulating amino acid metabolism. The optimal NCLE concentration was found to be 400 mg/kg.

PSAT1 mediated EMT of colorectal cancer cells by regulating Pl3K/AKT signaling pathway

Background: The metastasis of colorectal cancer (CRC) is one of the significant barriers impeding its treated consequence and bring about high mortality, less surgical resection rate and poor prognosis of CRC patients. PSAT1 is an enzyme involved in serine biosynthesis. The studies showed that PSAT1 plays the part of a crucial character in the regulation of tumor metastasis. And Epithelial-Mesenchymal Transition (EMT) is a process of cell reprogramming in which epithelialcells obtain mesenchymal phenotypes. It is a crucial course in promoting cell metastasis and the progression of malignant tumors. The relationship between PSAT1 and EMT in colorectal cancer, as well as the underlying molecular mechanisms, remains enigmatic and warrants thorough exploration. These findings suggest that PSAT1 may serve as a promising therapeutic target for mitigating colorectal cancer metastasis and holds the potential to emerge as a valuable prognostic biomarker in forthcoming research endeavors. Materials and Methods: Utilizing TCGA dataset in conjunction with clinical CRC specimens, our initial focus was directed towards an in-depth examination of PSAT1 expression within CRC, specifically exploring its potential correlation with the adverse prognostic outcomes experienced by patients. Furthermore, we conducted a comprehensive investigation into the regulatory influence exerted by PSAT1 on CRC through the utilization of siRNA knockdown techniques. In the realm of in vitro experimentation, we meticulously evaluated the impact of PSAT1 on various facets of CRC progression, including cell migration, invasion, proliferation, and colony formation. In order to elucidate the intricate effects in question, we adopted a multifaceted methodology that encompassed a range of assays and analyses. These included wound healing assays, transwell assays, utilization of the Cell Counting Kit-8 (CCK-8) assay, and colony formation assays. By employing this diverse array of investigative techniques, we were able to achieve a comprehensive comprehension of the multifaceted role that PSAT1 plays in the pathogenesis of colorectal cancer. This multifarious analysis greatly contributed to our in-depth understanding of the complex mechanisms at play in colorectal cancer pathogenesis. Using WB and PCR experiments, we found that PSAT1 has a role in regulating EMT development in CRC.In terms of mechanism, we found that PSAT1 affected EMT by Regulating Pl3K/AKT Signaling Pathway. Results: Our investigation revealed a noteworthy down-regulation of PSAT1 expression in CRC specimens. Importantly, this down-regulation exhibited a significant positive correlation with the unfavorable prognosis of patients afflicted with CRC. Functionally, our study showcased that the siRNA-mediated knockdown of PSAT1 markedly enhanced various key aspects of CRC pathogenesis in an in vitro setting. Specifically, this included a substantial promotion of CRC cell migration, invasion, proliferation, and colony formation. Moreover, the silencing of PSAT1 also demonstrated a substantial promotion of the EMT process. Intriguingly, our research unveiled a hitherto unexplored mechanism underlying the regulatory role of PSAT1 in CRC and EMT. We have established, for the first time, that PSAT1 exerts its influence by modulating the activation of the PI3K/AKT Signaling Pathway. This mechanistic insight provides a valuable contribution to the understanding of the molecular underpinnings of CRC progression and EMT induction mediated by PSAT1. Conclusions: In unison, our research findings shed light on the previously uncharted and significant role of the PSAT1/PI3K/AKT axis in the initiation of the EMT process in CRC. Furthermore, our discoveries introduce a novel biomarker with potential implications for the clinical diagnosis and treatment of CRC.

Multi-omics and immunogenomics analysis revealed PFKFB3 as a targetable hallmark and mediates sunitinib resistance in papillary renal cell carcinoma: in silico study with laboratory verification

Glycolysis-related metabolic reprogramming is a central hallmark of human cancers, especially in renal cell carcinoma. However, the regulatory function of glycolytic signature in papillary RCC has not been well elucidated. In the present study, the glycolysis-immune predictive signature was constructed and validated using WGCNA, glycolysis-immune clustering analysis. PPI network of DEGs was constructed and visualized. Functional enrichments and patients' overall survival were analyzed. QRT-PCR experiments were performed to detect hub genes' expression and distribution, siRNA technology was used to silence targeted genes; cell proliferation and migration assays were applied to evaluate the biological function. Glucose concentration, lactate secretion, and ATP production were measured. Glycolysis-Immune Related Prognostic Index (GIRPI) was constructed and combined analyzed with single-cell RNA-seq. High-GIRPI signature predicted significantly poorer outcomes and relevant clinical features of pRCC patients. Moreover, GIRPI also participated in several pathways, which affected tumor immune microenvironment and provided potential therapeutic strategy. As a key glycolysis regulator, PFKFB3 could promote renal cancer cell proliferation and migration in vitro. Blocking of PFKFB3 by selective inhibitor PFK-015 or glycolytic inhibitor 2-DG significantly restrained renal cancer cells' neoplastic potential. PFK-015 and sunitinib could synergistically inhibit pRCC cells proliferation. Glycolysis-Immune Risk Signature is closely associated with pRCC prognosis, progression, immune infiltration, and therapeutic response. PFKFB3 may serve as a pivotal glycolysis regulator and mediates Sunitinib resistance in pRCC patients.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage.

METHODS

We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis.

RESULTS

The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation.

CONCLUSION

The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Lipotoxicity and immunometabolism in ischemic acute kidney injury: current perspectives and future directions

Dysregulated lipid metabolism is implicated in the pathophysiology of a range of kidney diseases. The specific mechanisms through which lipotoxicity contributes to acute kidney injury (AKI) remain poorly understood. Herein we review the cardinal features of lipotoxic injury in ischemic kidney injury; lipid accumulation and mitochondrial lipotoxicity. We then explore a new mechanism of lipotoxicity, what we define as "immunometabolic" lipotoxicity, and discuss the potential therapeutic implications of targeting this lipotoxicity using lipid lowering medications.

Construction of EMT related prognostic signature for kidney renal clear cell carcinoma, through integrating bulk and single-cell gene expression profiles

Introduction: Kidney renal clear cell carcinoma (KIRC), as a main type of malignant kidney cancers, has a poor prognosis. Epithelial-mesenchymal transformation (EMT) exerts indispensable role in tumor progression and metastasis, including in KIRC. This study aimed to mine more EMT related details and build prognostic signature for KIRC. Methods: The KIRC scRNA-seq data and bulk data were downloaded from GEO and TCGA databases, respectively. The cell composition in KIRC was calculated using CIBERSORT. Univariate Cox regression analysis and LASSO Cox regression analysis were combined to determine the prognostic genes. Gene set variation analysis and cell-cell communication analysis were conducted to obtain more functional information. Additionally, functional analyses were conducted to determine the biological roles of si-LGALS1 in vitro. Results: We totally identified 2,249 significant differentially expressed genes (DEGs) in KIRC samples, meanwhile a significant distinct expression pattern was found in KIRC, involving Epithelial Mesenchymal Transition pathway. Among all cell types, significantly higher proportion of epithelial cells were observed in KIRC, and 289 DEGs were identified in epithelial cells. After cross analysis of all DEGs and 970 EMT related genes, SPARC, TMSB10, LGALS1, and VEGFA were optimal to build prognostic model. Our EMT related showed good predictive performance in KIRC. Remarkably, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process. Conclusion: A novel powerful EMT related prognostic signature was built for KIRC patients, based on SPARC, TMSB10, LGALS1, and VEGFA. Of which, si-LGALS1 could inhibit migration and invasion ability of KIRC cells, which might be involved in suppressing EMT process.

Construction of the metabolic reprogramming-associated gene signature for clear cell renal cell carcinoma prognosis prediction

BACKGROUND

Metabolism reprogramming is a hallmark that associates tumor growth, metastasis, progressive, and poor prognosis. However, the metabolism-related molecular patterns and mechanism in clear cell renal cell carcinoma (ccRCC) remain unclear. Herein, the purpose of this study was to identify metabolism-related molecular pattern and to investigate the characteristics and prognostic values of the metabolism-related clustering.

METHODS

We comprehensively analyzed the differentially expressed genes (DEGs), and metabolism-related genes (MAGs) in ccRCC based on the TCGA database. Consensus clustering was used to construct a metabolism-related molecular pattern. Then, the biological function, molecular characteristics, Estimate/immune/stomal scores, immune cell infiltration, response to immunotherapy, and chemotherapy were analyzed. We also identified the DEGs between subclusters and constructed a poor signature and risk model based on LASSO regression cox analysis and univariable and multivariable cox regression analyses. Then, a predictive nomogram was constructed and validated by calibration curves.

RESULTS

A total of 1942 DEGs (1004 upregulated and 838 downregulated) between ccRCC tumor and normal samples were identified, and 254 MRGs were screened out from those DEGs. Then, 526 ccRCC patients were divided into two subclusters. The 7 metabolism-related pathways enriched in cluster 2. And cluster 2 with high Estimate/immune/stomal scores and poor survival. While, cluster 1 with higher immune cell infiltrating, expression of the immune checkpoint, IFN, HLA, immune activation-related genes, response to anti-CTLA4 treatment, and chemotherapy. Moreover, we identified 295 DEGs between two metabolism-related subclusters and constructed a 15-gene signature and 9 risk factors. Then, a risk score was calculated and the patients into high- and low-risk groups in TCGA-KIRC and E-MTAB-1980 datasets. And the prediction viability of the risk score was validated by ROC curves. Finally, the clinicopathological characteristics (age and stage), risk score, and molecular clustering, were identified as independent prognostic variables, and were used to construct a nomogram for 1-, 3-, 5-year overall survival predicting. The calibration curves were used to verify the performance of the predicted ability of the nomogram.

CONCLUSION

Our finding identified two metabolism-related molecular subclusters for ccRCC, which facilitates the estimation of response to immunotherapy and chemotherapy, and prognosis after treatment.

CircZBTB44 promotes renal carcinoma progression by stabilizing HK3 mRNA structure

CircZBTB44 (hsa_circ_0002484) has been identified to be upregulated in renal cell carcinoma (RCC) tissues, while its role and contribution in RCC remain elusive. We confirmed the overexpression of circZBTB44 in RCC cells compared to normal kidney cell HK-2. CircZBTB44 knockdown suppressed the viability, proliferation, and migration of RCC cells and inhibited tumorigenesis in xenograft mouse models. Heterogeneous Nuclear Ribonucleoprotein C (HNRNPC) and Insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) are two RNA binding proteins of circZBTB44. HNRNPC facilitated the translocation of circZBTB44 from nuclei to cytoplasm via m6A modification, facilitating the interaction of IGF2BP3 and circZBTB44 in the cytoplasm of RCC cells. Furthermore, circZBTB44 upregulated Hexokinase 3 (HK3) expression by binding to IGF2BP3 in RCC cells. HK3 exerted oncogenic effects on RCC cell malignant behaviors and tumor growth. In the co-culture of RCC cells with macrophages, circZBTB44 promoted M2 polarization of macrophages by up-regulating HK3. In summary, HNRNPC mediated circZBTB44 interaction with IGF2BP3 to up-regulate HK3, promoting the proliferation and migration of RCC cells in vitro and tumorigenesis in vivo. The results of the study shed new light on the targeted therapy of RCC.

Downregulation of PSAT1 inhibits cell proliferation and migration in uterine corpus endometrial carcinoma

Phosphoserine aminotransferase 1 (PSAT1) has been associated with the occurrence and development of various carcinomas; however, its function in uterine corpus endometrial carcinoma (UCEC) is unknown. We aimed to explore the relationship between PSAT1 and UCEC using The Cancer Genome Atlas database and functional experiments. PSAT1 expression levels in UCEC were employed using the paired sample t-test, Wilcoxon rank-sum test, the Clinical Proteomic Tumor Analysis Consortium database, and the Human Protein Atlas database, while survival curves were constructed using the Kaplan-Meier plotter. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the possible functions and related pathways of PSAT1. Furthermore, single-sample gene set enrichment analysis was performed to detect the relationship between PSAT1 and tumor immune infiltration. StarBase and quantitative PCR were used to predict and verify the interactions between miRNAs and PSAT1. The Cell Counting Kit-8, EdU assay, clone formation assay, western blotting and flow cytometry were used to evaluate cell proliferation. Finally, Transwell and Wound healing assays were used to assess cell invasion and migration. Our study found that PSAT1 was significantly overexpressed in UCEC, and this high expression was associated with a worse prognosis. A high level of PSAT1 expression was associated with a late clinical stage and, histological type. In addition, the results of GO and KEGG enrichment analysis showed that PSAT1 was mainly involved in the regulation of cell growth, immune system and cell cycle in UCEC. In addition, PSAT1 expression was positively correlated with Th2 cells and negatively correlated with Th17 cells. Furthermore, we also found that miR-195-5P negatively regulated the expression of PSAT1 in UCEC. Finally, the knockdown of PSAT1 resulted in the inhibition of cell proliferation, migration, and invasion in vitro. Overall, PSAT1 was identified as a potential target for the diagnosis and immunotherapy of UCEC.

Exploring the prognostic value of HK3 and its association with immune infiltration in glioblastoma multiforme

Background: Hexokinase 3 (HK3) is one of the key enzymes involved in glucose phosphorylation (the first step in most glucose metabolic pathways). Many studies have demonstrated the vital role of dysregulation of HK3 in several tumors. However, there is a need for in-depth characterization of the role of HK3 in glioblastoma multiforme (GBM). Methods: All data were sourced from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Kaplan-Meier analysis and univariate regression were applied for survival analysis. Gene set enrichment analysis (GSEA) was used for enrichment analysis. Tumor Immune Single Cell Hub (TISCH) database was applied for single-cell analysis. Tumor Immune Dysfunction and Exclusion (TIDE) analysis was applied to evaluate the immune response. Results: HK3 expression was upregulated in GBM and correlated with poor prognosis. The high HK3 expression group was primarily enriched in adaptive immune response, chemokine signaling pathway, and cytokine-cytokine receptor interaction. The high HK3 expression group showed significantly greater enrichment of the majority of immune cells and immune-related pathways. HK3 showed significant correlation with most immune cells, especially macrophages (p < .001, R = .81). TISCH analysis showed that HK3 was predominantly expressed in macrophages in most cancers. HK3 showed significant correlation with most immune-related genes, such as PD-1 (p < .001, R = .41), PDL-1 (p < .001, R = .27), and CTLA-4 (p < .001, R = .29). TIDE analysis revealed that the low HK3 expression group has a lower TIDE score and may benefit from immunotherapy. Drug sensitivity analysis showed that patients with high HK3 expression frequently showed drug resistance. Conclusion: HK3 was associated with poor prognosis and may serve as a biomarker of macrophages in GBM. HK3 was also associated with immune response and drug resistance. Our findings may provide novel insights for GBM immunotherapy.

Meta-analysis of the effect of PGM on survival prognosis of tumor patients

Objective

A systematic evaluation of the impact of phosphoglucose translocase PGM on the survival prognosis of tumor patients was conducted to understand its impact on tumors so as to improve the quality of survival and to find effective therapeutic targets for tumor patients.

Methods

The following were searched in the databases China National Knowledge Infrastructure (CNKI), Wanfang, Wipu, PubMed, EMBASE, ScienceDirect, Web of Science, and Cochrane Library: "PGM1", "PGM2", "PGM3", "PGM4", and "PGM5" as Chinese keywords and "PGM1", "PGM2", "PGM3", "PGM4", "PGM5", "PGM1 cancer", "PGM2 cancer", "PGM3 cancer", "PGM4 cancer", "PGM5 cancer", and "phosphoglucomutase". Relevant studies published from the database establishment to April 2022 were collected. Studies that met the inclusion criteria were extracted and evaluated for quality with reference to the Cochrane 5.1.0 systematic evaluation method, and quality assessment was performed using RevMan 5.3 software.

Results

The final results of nine articles and 10 studies with a total of 3,806 patients were included, including 272 patients in the PGM1 group, 541 patients in the PGM2 group, 1,775 patients in the PGM3 group, and 1,585 patients in the PGM5 group. Results of the meta-analysis: after determining the results of the nine articles, it was found that the difference was statistically significant with a p-value <0.05 (hazard ratio (HR) = 0.89, 95% CI 0.69-1.09, p = 0.000). To find the sources of heterogeneity, the remaining eight papers were tested after removing the highly sensitive literature, and the results showed I² = 26.5%, p < 0.001, a statistically significant difference. The HR for high expression of PGM1 and PGM2 and PGM5 was <1, while the HR for high expression of PGM3 was >1.

Conclusion

Although PGM1, PGM2, PGM3, and PGM5 are enzymes of the same family, their effects on tumors are different. High expression of PGM1, PGM2, and PGM5 can effectively prolong the overall survival of patients. In contrast, high expression of PGM3 reduced the overall survival of patients. This study of PGM family enzymes can assist in subsequent tumor diagnosis, treatment, and prognostic assessment.

A Potential Fatty Acid Metabolism-Related Gene Signature for Prognosis in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common and aggressive subtype of renal cancer. Abnormal fatty acid metabolism (FAM) is reported to be strongly associated with multiple malignancies, yet there is limited research in ccRCC. In this manuscript, we reported the significant role of abnormal FAM in predicting the prognosis of ccRCC. Three independent clinical cohorts (TCGA, EMTAB and our clinical cohorts with prognostic profiles and gene expression data, including RNA-seq, microarray and RT-qPCR) were applied as training and two external validation cohorts. As a result, we successfully constructed and validated a novel FAM-related gene signature (FAMGS) and nomogram for the overall survival of patients with ccRCC. Additionally, our study further elucidated the potential immune relevance and molecular mechanisms of abnormal FAM and the signature. In conclusion, the novel FAMGS constructed in this study offered a promising prognostic tool in clinic and potential therapeutic targets for ccRCC patients.

Abstract

This study aims to explore the role of abnormal fatty acid metabolism (FAM) in ccRCC and construct a novel fatty acid metabolism-related gene signature (FAMGS) for prognosis. Three independent ccRCC cohorts, including The Cancer Genome Atlas, E-MTAB-1980 and our clinical cohort (including RNA-seq, microarray and RT-qPCR data), were applied as training and two independent validation cohorts. Firstly, FAM levels were found to be significantly decreased in ccRCC and correlated with degrees of malignancy, confirming the pivotal role of FAM in ccRCC. Applying the least absolute shrinkage and selection operator cox regression, we established a novel FAMGS for overall survival (OS). The FAMGS divided patients into low or high-risk groups in the training cohort and were successfully validated in both the EMTAB and our clinical validation cohorts. Additionally, the FAMGS serves as an independent risk factor for OS of ccRCC. Results of the immune cell abundance identifier (ImmuCellAI) algorithm and gene set variation analysis (GSVA) revealed that patients in the high-risk group have comprehensively impaired metabolism, including lipids, amino acids and tricarboxylic acid cycle-related pathways and a more immunosuppressive tumor microenvironment. In conclusion, our study constructed and validated a novel FAMGS, which may improve the risk stratification optimization and personalized management of ccRCC.

Comprehensive characterisation of immunogenic cell death in melanoma revealing the association with prognosis and tumor immune microenvironment

Increasing evidence has highlighted the critical functions of immunogenic cell death (ICD) within many tumors. However, the therapeutic possibilities and mechanism of utilizing ICD in melanoma are still not well investigated. Melanoma samples involved in our study were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. First, pan-cancer analysis of ICD systematically revealed its expression characteristics, prognostic values, mutation information, methylation level, pathway regulation relationship in multiple human cancers. The non-negative matrix factorization clustering was utilized to separate the TCGA-melanoma samples into two subtypes (i.e. C1 and C2) with different prognosis and immune microenvironment based on the expression traits of ICD. Then, LASSO-Cox regression analysis was utilized to determine an ICD-dependent risk signature (ICDRS) based on the differentially expressed genes (DEGs) between the two subtypes. Principal component analysis and t-distributed stochastic neighbor embedding analysis of ICDRS showed that high- and low-risk subpopulations could be clearly distinguished. Survival analysis and ROC curves in the training, internal validation, and external validation cohorts highlighted the accurate prognosis evaluation of ICDRS. The obvious discrepancies of immune microenvironment between the different risk populations might be responsible for the different prognoses of patients with melanoma. These findings revealed the close association of ICD with prognosis and tumor immune microenvironment. More importantly, ICDRS-based immunotherapy response and targeted drug prediction might be beneficial to different risk subpopulations of patients with melanoma. The innotative ICDRS could function as a marker to determine the prognosis and tumor immune microenvironment in melanoma. This will aid in patient classification for individualized melanoma treatment.

Cuprotosis-Related Genes: Predicting Prognosis and Immunotherapy Sensitivity in Pancreatic Cancer Patients

Based on TCGA, GTEx, and TIMER databases and various bioinformatics analysis methods, the potential biological roles of cuprotosis-related genes in pancreatic cancer were deeply explored, and a predictive model for pancreatic cancer patients was constructed. We downloaded the RNA-Seq data and clinicopathological and predictive data of 179 pancreatic cancer tissues and 332 adjacent normal tissues from TCGA and GTEx databases. The differential expression of cuprotosis-related genes in pancreatic cancer tissue and adjacent normal tissue was analyzed, and the LASSO regression algorithm was used to construct a prediction model and verify the validity of the model prediction. Based on the LASSO regression algorithm, a predictive model composed of three genes LIPT1, LIAS, and DLAT was screened. The corresponding survival curves showed that the constructed prediction model could significantly distinguish the prognosis of pancreatic cancer patients, and the prognosis of patients in the high-risk group was worse (P = 0.00557). The ROC curve showed that the area under the curve of the predictive model for predicting the 4-, 5-, and 6-year survival rates in pancreatic cancer was 0.816, 0.836, and 0.956, respectively. The AUC value of this risk model was significantly higher than 0.7, which could more accurately predict the prognosis of pancreatic cancer patients. This study determined a risk-scoring model of cuprotosis-related genes, which can provide an essential basis for judging the prognosis of pancreatic cancer patients.

Low expression of isocitrate dehydrogenase 1 (IDH1) R132H is associated with advanced pathological features in laryngeal squamous cell carcinoma

INTRODUCTION

Recent developments in genomic sequencing have led to the identification of somatic mutations in isocitrate dehydrogenase 1 (IDH1) in various malignancies. IDH1 R132H is the most common mutation of IDH1, which affects codon 132 and results in the conversion of amino acid residue arginine (R) to histidine (H). This study is designed to evaluate the association between the expression of IDH1 R132H and clinicopathological characteristics in laryngeal squamous cell carcinoma (LSCC).

METHODS

The expression pattern and clinical significance of IDH1 R132H were investigated in tissue microarrays (TMAs) of 50 LSCC tumors as well as adjacent normal tissues using immunohistochemistry. Then the exons of the 12 tumor samples with negative/weak positive staining were sequenced by applying polymerase chain reaction (PCR).

RESULTS

The results demonstrated that the cytoplasmic expression of IDH1 R132H was downregulated in tumor cells compared to adjacent normal tissues. A statistically significant association was found between a low level of cytoplasmic expression of IDH1 R132H protein and an increase in histological grade (p < 0.001), perineural invasion (p = 0.019), and lymph node involvement (p < 0.001). The exon4 sequencing results showed that only one sample was positive for IDH1 R132H mutation. IDH1 R132H expression was observed in 39 (78.0%) LSCC samples.

CONCLUSION

These findings indicate that low cytoplasmic expression of IDH1 R132H may have clinical significance in LSCC patients and is associated with more aggressive tumor behavior and progression of the disease, which can help improve potential treatment in patients with LSCC. Further investigations are needed to understand the biological function of IDH1 R132H and larger sample size to confirm our findings.

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes.

Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM‐related differentially expressed genes were screened via the “limma” package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the “survminer” package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m⁶A)-related genes, and sensitivity to chemotherapy was assessed.

Results: A novel consensus AMM‐related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m⁶A modification, and sensitivity to chemotherapy drugs.

Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

Long non-coding RNAs play an important regulatory role in tumorigenesis and tumor progression through aerobic glycolysis

Compared to normal cells, cancer cells generate ATP mainly through aerobic glycolysis, which promotes tumorigenesis and tumor progression. Long non-coding RNAs (LncRNAs) are a class of transcripts longer than 200 nucleotides with little or without evident protein-encoding function. LncRNAs are involved in the ten hallmarks of cancer, interestingly, they are also closely associated with aerobic glycolysis. However, the mechanism of this process is non-transparent to date. Demonstrating the mechanism of lncRNAs regulating tumorigenesis and tumor progression through aerobic glycolysis is particularly critical for cancer therapy, and may provide novel therapeutic targets or strategies in cancer treatment. In this review, we discuss the role of lncRNAs and aerobic glycolysis in tumorigenesis and tumor progression, and further explore their interaction, in hope to provide a novel therapeutic target for cancer treatment.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10–15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97–3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50–5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.

Adipogenic Transdifferentiation and Regulatory Factors Promote the Progression and the Immunotherapy Response of Renal Cell Carcinoma: Insights From Integrative Analysis

Background

Adipogenic transdifferentiation was an important carcinogenic factor in various tumors, while studies on its role in clear cell renal cell carcinoma (ccRCC) were still relatively few. This study aimed to investigate its prognostic value and mechanism of action in ccRCC.

Methods

Gene expression profiles and clinical data of ccRCC patients were obtained from The Cancer Genome Atlas database. Nonnegative matrix factorization was used for clustering. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to analyze the pathways and biological process activities. single-sample GSEA (ssGSEA) was utilized to quantify the relative abundance of each immune cell. Tumor Immune Estimation Resource (TIMER) was used to evaluate the proportion of various immune infiltrating cells across diverse cancer types. Real-Time PCR was performed to examine the gene expression. R software was utilized to analyze the expression and prognostic role of genes in ccRCC.

Results

A total of 49 adipose-related genes (ARGs) were screened for differential expression between normal and ccRCC tissues. Based on differentially expressed ARGs, patients with ccRCC were divided into two adipose subtypes with different clinical, molecular, and pathway characteristics. Patients in cluster A exhibited more advanced pathological stages, higher expressions of RARRES2 and immune checkpoint genes, higher immune infiltration scores, and less nutrient metabolism pathways. Adipose differentiation index (ADI) was constructed according to the above ARGs and survival data, and its robustness and accuracy was validated in different cohorts. In addition, it was found that the expression of ARGs was associated with immune cell infiltration and immune checkpoint in ccRCC, among which GBP2 was thought to be the most relevant gene to the tumor immune microenvironment and play a potential role in carcinogenesis and invasion of tumor cells.

Conclusion

Our analysis revealed the consistency of higher adipogenic transdifferentiation of tumor cells with worse clinical outcomes in ccRCC. The 16-mRNA signature could predict the prognosis of ccRCC patients with high accuracy. ARGs such as GBP2 might shed light on the development of novel biomarkers and immunotherapies of ccRCC.

DLK2 Acts as a Potential Prognostic Biomarker for Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8⁺ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint.

Identification of a Three-Glycolysis-Related lncRNA Signature Correlated With Prognosis and Metastasis in Clear Cell Renal Cell Carcinoma

The clear cell renal cell carcinoma (ccRCC) is not only a malignant disease but also an energy metabolic disease, we aimed to identify a novel prognostic model based on glycolysis-related long non-coding RNA (lncRNAs) and explore its mechanisms. With the use of Pearson correlation analysis between the glycolysis-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas (TCGA) dataset, we identified three glycolysis-related lncRNAs and successfully constructed a prognostic model based on their expression. The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated by univariate and multivariate Cox analyses, Kaplan–Meier survival analysis, and principal component analysis (PCA). The glycolysis-related lncRNA signature was constructed based on the expressions of AC009084.1, AC156455.1, and LINC00342. Patients were grouped into high- or low-risk groups according to risk score demonstrated significant differences in overall survival (OS) period, which were validated by patients with ccRCC from the International Cancer Genome Consortium (ICGC) database. Univariate Cox analyses, multivariate Cox analyses, and constructed nomogram-confirmed risk score based on our signature were independent prognosis predictors. The CIBERSORT algorithms demonstrated significant correlations between three-glycolysis-related lncRNAs and the tumor microenvironment (TME) components. Functional enrichment analysis demonstrated potential pathways and processes correlated with the risk model. Clinical samples validated expression levels of three-glycolysis-related lncRNAs, and LINC00342 demonstrated the most significant aberrant expression. in vitro, the general overexpression of LINC00342 was detected in ccRCC cells. After silencing LINC00342, the aberrant glycolytic levels and migration abilities in 786-O cells were decreased significantly, which might be explained by suppressed Wnt/β-catenin signaling pathway and reversed Epithelial mesenchymal transformation (EMT) process. Collectively, our research identified a novel three-glycolysis-related lncRNA signature as a promising model for generating accurate prognoses for patients with ccRCC, and silencing lncRNA LINC00342 from the signature could partly inhibit the glycolysis level and migration of ccRCC cells.

Value of Immunohistochemical Expression of Apelin, Succinate Dehydrogenase B, Chromogranin B, Human Epidermal Growth Factor Receptor-2, Contactin 4, and Succinyl-CoA Synthetase Subunit Beta in Differentiating Metastatic From Non-Metastatic Pheochromocytoma and Paraganglioma

OBJECTIVE

We aimed to retrospectively collect pathologically identified pheochromocytoma and paraganglioma (PPGL) tumor tissues from our center and investigate the expression of apelin and succinyl-CoA synthetase subunit beta (SUCLG2), human epidermal growth factor receptor-2 (HER2 or ERBB-2), contactin 4 (CNTN4), chromogranin B (CHGB), and succinate dehydrogenase B (SDHB) in metastatic and non-metastatic PPGLs, for exploring their roles in the diagnosis of metastatic PPGLs.

METHODS

A total of 369 patients with pathologically and surgically confirmed PPGLs at Xiangya Hospital, Central South University, between June 2010 and June 2020 were retrospectively included. Sixty patients-12 patients with metastatic PPGLs and 48 patients with non-metastatic PPGLs-were selected through propensity score matching (1:4) to reduce the effect of PPGL type, sex, and age. We observed and quantified the expression of apelin, SDHB, CHGB, ERBB-2, CNTN4, and SUCLG2 in paraffin-embedded samples using immunohistochemical staining.

RESULTS

No significant differences were observed between the metastatic group and non-metastatic group with respect to the expression of CNTN4 and SUCLG2. The expression of apelin, SDHB, CHGB, and ERBB-2 was significantly different between the two groups. The expression of apelin, SDHB, and CHGB was significantly lower in the metastatic group than that in the non-metastatic group (P < 0.001). ERBB-2 expression was significantly higher in the metastatic group than in the non-metastatic group (P = 0.042). Kaplan-Meier analysis revealed that patients with negative expression of apelin, SDHB, and CHGB showed significantly lower metastasis-free survival than those with positive expression. Multivariate Cox analysis revealed that SDHB and CHGB levels were independently associated with metastasis-free survival.

CONCLUSION

The expression levels of apelin, CHGB, SDHB, and ERBB-2 may be predictive biomarkers for the diagnosis of metastatic PPGLs. Patients with negative expression of apelin, CHGB, and SDHB should be subjected to frequent postoperative follow-up procedures.

Are we still on the right path(way)?: the altered expression of the pentose phosphate pathway in solid tumors and the potential of its inhibition in combination therapy

INTRODUCTION

The pentose phosphate pathway (PPP) branches from glycolysis and is crucial for cell growth, since it provides necessary compounds for anabolic reactions, nucleotide synthesis, and detoxification of reactive-oxygen-species (ROS). Overexpression of PPP enzymes has been reported in multiple cancer types and linked to therapy resistance, making their inhibition interesting targets for anti-cancer therapies.

AREAS COVERED

This review summarizes the extent of PPP upregulation across different cancer types, and the non-metabolic functions that PPP-enzymes might contribute to cancer initiation and maintenance. The effects of PPP-inhibition and their combinations with chemotherapeutics are summarized. We searched the databases provided by the University of Amsterdam to characterize the altered expression of the PPP across different cancer types, and to identify the effects of PPP-inhibition.

EXPERT OPINION

It can be concluded that there are synergistic and additive effects of PPP-inhibition and various classes of chemotherapeutics. These effects may be attributed to the increased susceptibility to ROS. However, the toxicity, low efficacy, and off-target effects of PPP-inhibitors make application in clinical practice challenging. Novel inhibitors are currently being developed, which could make PPP-inhibition a potential therapeutic strategy in the future, especially in combination with conventional chemotherapeutics and the inhibition of other metabolic pathways.

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Background

Breast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC.

Methods

We analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control.

Results

We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception.

Conclusion

We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.

Involvement of Tricarboxylic Acid Cycle Metabolites in Kidney Diseases

Mitochondria are complex organelles that orchestrate several functions in the cell. The primary function recognized is energy production; however, other functions involve the communication with the rest of the cell through reactive oxygen species (ROS), calcium influx, mitochondrial DNA (mtDNA), adenosine triphosphate (ATP) levels, cytochrome c release, and also through tricarboxylic acid (TCA) metabolites. Kidney function highly depends on mitochondria; hence mitochondrial dysfunction is associated with kidney diseases. In addition to oxidative phosphorylation impairment, other mitochondrial abnormalities have been described in kidney diseases, such as induction of mitophagy, intrinsic pathway of apoptosis, and releasing molecules to communicate to the rest of the cell. The TCA cycle is a metabolic pathway whose primary function is to generate electrons to feed the electron transport system (ETS) to drives energy production. However, TCA cycle metabolites can also release from mitochondria or produced in the cytosol to exert different functions and modify cell behavior. Here we review the involvement of some of the functions of TCA metabolites in kidney diseases.

DNA Polymerase Iota Promotes Esophageal Squamous Cell Carcinoma Proliferation Through Erk-OGT-Induced G6PD Overactivation

Esophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers with rapid progression and a high mortality rate. Our previous study demonstrated that DNA polymerase iota (Pol ι) is overexpressed in ESCC tumors and correlates with poor prognosis. However, its role in ESCC proliferation remains obscure. We report here that Pol ι promotes ESCC proliferation and progression through Erk- O-GlcNAc transferase (OGT) regulated Glucose-6-phosphate dehydrogenase (G6PD) overactivation. Cell clonogenic ability was assessed by colony formation assay. Cell proliferation was assessed by EdU incorporation assay. Our transcriptome data was reanalyzed by GSEA and validated by analysis of cellular metabolism, G6PD activity, and cellular NADPH concentration. The level of Pol ι, OGT, G6PD and O-GlcNAcylation in ESCC cells and patient samples were analyzed. The MEK inhibitor PD98059 was applied to confirm OGT expression regulation by the Erk signaling. The G6PD inhibitor polydatin was used to examine the role of G6PD activation in Pol ι promoted proliferation. We found that Pol ι promotes ESCC proliferation. It shunted the glucose flux towards the pentose phosphate pathway (PPP) by activating G6PD through OGT-promoted O-GlcNAcylation. The expression of OGT was positively correlated with Pol ι expression and O-GlcNAcylation. Notably, elevated O-GlcNAcylation was correlated with poor prognosis in ESCC patients. Pol ι was shown to stimulate Erk signaling to enhance OGT expression, and the G6PD inhibitor polydatin attenuated Pol ι induced tumor growth in vitro and in vivo. In conclusion, Pol ι activates G6PD through Erk-OGT-induced O-GlcNAcylation to promote the proliferation and progression of ESCC, supporting the notion that Pol ι is a potential biomarker and therapeutic target of ESCC.

Comparison of the Fatty Acid Metabolism Pathway in Pan-Renal Cell Carcinoma: Evidence from Bioinformatics

This study analyzed and compared the potential role of fatty acid metabolism pathways in three subtypes of renal cell carcinoma. Biological pathways that were abnormally up- and downregulated were identified through gene set variation analysis in the subtypes. Abnormal downregulation of the fatty acid metabolism pathway occurred in all three renal cell carcinoma subtypes. Alteration of the fatty acid metabolism pathway was vital in the development of pan-renal cell carcinoma. Bioinformatics methods were used to obtain a panoramic view of copy number variation, single-nucleotide variation, mRNA expression, and the survival landscape of fatty acid metabolism pathway-related genes in pan-renal cell carcinoma. Most importantly, we used genes related to the fatty acid metabolism pathway to establish a prognostic-related risk model in the three subtypes of renal cell carcinoma. The data will be valuable for future clinical treatment and scientific research.