A glycolysis-related gene signature predicts prognosis of patients with esophageal adenocarcinoma

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

LINC00665 promotes glycolysis in lung adenocarcinoma cells via the let-7c-5p/HMMR axis

Lung adenocarcinoma (LUAD) is one of the most lethal and common malignancies. The energy metabolism of LUAD is a critical factor affecting its malignant progression, and research on this topic can aid in the development of novel cancer treatment targets. Bioinformatics analysis of the expression of long non-coding RNA (lncRNA) LINC00665 in LUAD was performed. Downstream regulatory molecules of LINC00665 were predicted using the StarBase database. We used quantitative reverse transcription polymerase chain reaction and western blot to measure the expression at mRNA and protein levels, respectively. The effects of the LINC00665/let-7c-5p/HMMR axis on cell viability in vitro were tested by CCK-8 assay. The regulatory effects on glycolysis were analyzed by extracellular acidification rate, oxygen consumption rate, glucose uptake, adenosine triphosphate production, and lactate production. The predicted competitive endogenous RNA mechanism between LINC00665 and let-7c-5p/HMMR was verified by a dual-luciferase reporter gene assay. LINC00665 was upregulated in LUAD. Silencing LINC00665 inhibited tumor proliferation and reduced the glycolytic activity of tumor cells. Additionally, the expression of LINC00665 had a negative correlation with that of let-7c-5p, while the expression of HMMR was remarkably inhibited by let-7c-5p. HMMR could affect the development of LUAD by influencing glycolytic capacity. Mechanistically, LINC00665 acted as a molecular sponge to absorb let-7c-5p and targeted HMMR. Transfection of let-7c-5p inhibitor or overexpression of HMMR plasmid could reverse the inhibition in proliferation and glycolysis of LUAD cells induced by silencing of LINC00665. In summary, this study demonstrated that the LINC00665/let-7c-5p/HMMR regulatory axis promoted the tumorigenesis of LUAD by enhancing aerobic glycolysis, suggesting that this regulatory axis was an effective target for inhibiting LUAD progression and providing theoretical support for the development of new drugs for LUAD.

Porphyromonas gingivalis msRNA P.G_45033 induces amyloid-β production by enhancing glycolysis and histone lactylation in macrophages

BACKGROUND

High expression of amyloid-β (Aβ) in periodontal tissue could contribute to exacerbating the development of both periodontitis and Alzheimer's disease (AD). Porphyromonas gingivalis (P. gingivalis) as a periodontal pathogen expresses msRNAs, which can regulate gene transcription in host cells.

OBJECTIVE

The aim of this study is to reveal the mechanism of msRNA P.G_45033, a high copy msRNA in P. gingivalis, inducing Aβ expression in macrophages, and provide a new insight to explain the development of periodontitis, and also to explain the role of periodontal infection on AD.

METHODS

The levels of glucose consumption, pyruvate and lactate productions in macrophages after transfection with msRNA P.G_45033 were detected. Miranda, TargetScan, and RNAhybrid databases were used to predict the target gene of msRNA P.G_45033, and GO analysis was conducted to describe the functions of the overlapping ones. RT2 glucose-metabolism PCR Array was used to verify the relationship between msRNA P.G_45033 and the expression of genes related to glucose metabolism. The levels of histone Kla were detected using western blotting. The levels of Aβ in the macrophages and the culture medium were detected by immunofluorescence and ELISA, respectively.

RESULTS

The levels of glucose consumption, pyruvate and lactate productions were increased after transfection of msRNA P.G_45033 in macrophages. GO analysis revealed that target genes were enriched in the metabolic process. RT2 glucose-metabolism PCR Array showed the expression of genes associated with glycolysis. The results of western blotting showed that the level of histone Kla was increased in macrophages. The results of immunofluorescence and ELISA showed that Aβ levels in macrophages and culture medium were increased after transfection.

CONCLUSION

The present study revealed that msRNA P.G_45033 can induce Aβ production by enhancing glycolysis and histone Kla in macrophages.

Differential Regulation of Genes by the Glucogenic Hormone Asprosin in Ovarian Cancer

Background: Ovarian cancer (OvCa) is one of the most lethal forms of gynaecological malignancy. Altered energy metabolism and increased aerobic glycolysis in OvCa are hallmarks that demand attention. The glucogenic hormone asprosin is often dysregulated in metabolic disorders such as insulin resistance, diabetes (type 2 and gestational), and preeclampsia. Despite association with metabolic disorders, its role in energy metabolism within the tumour microenvironment is yet to be explored. Here, we study the role of asprosin in OvCa using transcriptomics and expand on functional studies with clinical samples. Methods: RNA sequencing, functional gene enrichment analysis, Western blotting and ImageStream. Results: Following treatment with 100 nM of asprosin, the serous OvCa cell line, SKOV-3, displayed 160 and 173 gene regulatory changes, at 4 and 12 h respectively, when compared with control samples (p < 0.05 and Log2FC > 1). In addition to energy metabolism and glucose-related pathways, asprosin was shown to alter pathways associated with cell communication, TGF-β signalling, and cell proliferation. Moreover, asprosin was shown to induce phosphorylation of ERK1/2 in the same in vitro model. Using liquid biopsies, we also report for novel expression of asprosin’s predicted receptors OR4M1 and TLR4 in cancer-associated circulating cells; with significant reduction seen between pre-chemotherapy and end of first line chemotherapy, in addition to patients under maintenance with bevacizumab +/− olaparib for OR4M1. Conclusions: In relation to OvCa, asprosin appears to regulate numerous signalling pathways in-vitro. The prognostic potential of OR4M1 in liquid biopsies should also be explored further.

The pathophyiological role of aminoacyl-tRNA synthetases in digestive system diseases

Aminoacyl-tRNA synthetases (ARSs) catalyze the ligation of amino acids to their cognate transfer RNAs and are indispensable enzymes for protein biosynthesis in all the cells. Previously, ARSs were considered simply as housekeeping enzymes, however, they are now known to be involved in a variety of physiological and pathological processes, such as tumorigenesis, angiogenesis, and immune response. In this review, we summarize the role of ARSs in the digestive system, including the esophagus, stomach, small intestine, colon, as well as the auxiliary organs such as the pancreas, liver, and the gallbladder. Furthermore, we specifically focus on the diagnostic and prognostic value of ARSs in cancers, aiming to provide new insights into the pathophysiological implications of ARSs in tumorigenesis.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Developing an Immune-Related Signature for Predicting Survival Rate and the Response to Immune Checkpoint Inhibitors in Patients With Glioma

Background: Glioma is one of the most aggressive cancer types affecting the central nerve system, with poor overall survival (OS) rates. The present study aimed to construct a novel immune-related signature to predict prognosis and the efficiency of immunotherapy in patients with glioma.

Methods: The mRNA expression data and other clinical information of patients with glioblastoma multiforme (GBM) and low grade glioma (LGG) were obtained from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. The immune-related genes were obtained from the Immunology Database and Analysis Portal database. Subsequently, an immune-related signature was created following the results obtained from the Least Absolute Shrinkage and Selection Operator regression model. To validate the predictability of the signature, Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves were created. Moreover, both univariate and multivariate analyses were carried out using the OS between this signature and other clinicopathologic factors, and a nomogram was constructed. In addition, the association between signature, immune cell infiltration, tumor mutation burden and immunophenoscore were determined.

Results: Results of the present study using 118 GBM and LGG samples uncovered 15 immune-related genes that were also differently expressed in glioma samples. These were subsequently used to construct the immune-related signature. This signature exhibits the ability to predict prognosis, the infiltration of immune cells in the tumor microenvironment and the response of patients with glioma to immunotherapy.

Conclusion: Results of the present study demonstrated that the aforementioned novel immune-related signature may accurately predict prognosis and the response of patients with glioma to immunotherapy.

Evaluation of the Prognostic Relevance of Differential Claudin Gene Expression Highlights Claudin-4 as Being Suppressed by TGFβ1 Inhibitor in Colorectal Cancer

Background: Claudins (CLDNs) are a family of closely related transmembrane proteins that have been linked to oncogenic transformation and metastasis across a range of cancers, suggesting that they may be valuable diagnostic and/or prognostic biomarkers that can be used to evaluate patient outcomes. However, CLDN expression patterns associated with colorectal cancer (CRC) remain to be defined.

Methods: The mRNA levels of 21 different CLDN family genes were assessed across 20 tumor types using the Oncomine database. Correlations between these genes and patient clinical outcomes, immune cell infiltration, clinicopathological staging, lymph node metastasis, and mutational status were analyzed using the GEPIA, UALCAN, Human Protein Atlas, Tumor Immune Estimation Resource, STRING, Genenetwork, cBioportal, and DAVID databases in an effort to clarify the potential functional roles of different CLDN protein in CRC. Molecular docking analyses were used to probe potential interactions between CLDN4 and TGFβ1. Levels of CLDN4 and CLDN11 mRNA expression in clinical CRC patient samples and in the HT29 and HCT116 cell lines were assessed via qPCR. CLDN4 expression levels in these 2 cell lines were additionally assessed following TGFβ1 inhibitor treatment.

Results: These analyses revealed that COAD and READ tissues exhibited the upregulation of CLDN1, CLDN2, CLDN3, CLDN4, CLDN7, and CLDN12 as well as the downregulation of CLDN5 and CLDN11 relative to control tissues. Higher CLDN11 and CLDN14 expression as well as lower CLDN23 mRNA levels were associated with poorer overall survival (OS) outcomes. Moreover, CLDN2 and CLDN3 or CLDN11 mRNA levels were significantly associated with lymph node metastatic progression in COAD or READ lower in COAD and READ tissues. A positive correlation between the expression of CLDN11 and predicted macrophage, dendritic cell, and CD4+ T cell infiltration was identified in CRC, with CLDN12 expression further being positively correlated with CD4+ T cell infiltration whereas a negative correlation was observed between such infiltration and the expression of CLDN3 and CLDN15. A positive correlation between CLDN1, CLDN16, and neutrophil infiltration was additionally detected, whereas neutrophil levels were negatively correlated with the expression of CLDN3 and CLDN15. Molecular docking suggested that CLDN4 was able to directly bind via hydrogen bond with TGFβ1. Relative to paracancerous tissues, clinical CRC tumor tissue samples exhibited CLDN4 and CLDN11 upregulation and downregulation, respectively. LY364947 was able to suppress the expression of CLDN4 in both the HT29 and HCT116 cell lines.

Conclusion: Together, these results suggest that the expression of different CLDN family genes is closely associated with CRC tumor clinicopathological staging and immune cell infiltration. Moreover, CLDN4 expression is closely associated with TGFβ1 in CRC, suggesting that it and other CLDN family members may represent viable targets for antitumor therapeutic intervention.

Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma

BACKGROUND

Glycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear.

METHODS

We performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for in vitro experimental validation.

RESULTS

Using the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (P<0.05). In addition, machine learning identified INSR as the gene with the highest importance for OS metastasis, and the transwell assay verified that INSR significantly promoted OS cell metastasis.

CONCLUSIONS

A risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and in vitro experiments also confirmed the important role of INSR in promoting OS migration.

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning

BACKGROUND

Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through bioinformatics and machine learning.

METHODS

The authors screened the dataset GSE51024 from the GEO database for Gene set enrichment analysis (GSEA), and performed differentially expressed genes (DEGs) of glycolytic pathway gene sets. Then, Cox regression analysis was used to identify prognosis-associated glycolytic genes and establish a risk model. Further, the validity of the risk model was evaluated using the dataset GSE67487 in GEO database, and finally, a specimen classification model was constructed by support vector machine (SVM) and random forest (RF) to further screen prognostic genes.

RESULTS

By DEGs, five glycolysis-related pathway gene sets (17 glycolytic genes) were identified to be highly expressed in MPM tumor tissues. Also 11 genes associated with MPM prognosis were identified in TCGA-MPM patients, and 6 (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) of them were included by Multi-factor COX analysis to construct a prognostic risk model for MPM patients, with Area under the ROC curve (AUC) was 0.830. Further, dataset GSE67487 also confirmed the validity of the risk model, with a significant difference in overall survival (OS) between the low-risk and high-risk groups (P < 0.05). The final machine learning screened the five prognostic genes with the highest risk of MPM, in order of importance, were ALDH2, KIF20A, COL5A1, ADH1B and SDC1.

CONCLUSIONS

A risk model based on six glycolytic genes (ALDH2, KIF20A, COL5A1, ADH1B, SDC1, VCAN) can effectively predict the prognosis of MPM patients.

Glycolysis Changes the Microenvironment and Therapeutic Response Under the Driver of Gene Mutation in Esophageal Adenocarcinoma

Background: Esophageal cancer is one of the most leading and lethal malignancies. Glycolysis and the tumor microenvironment (TME) are responsible for cancer progressions. We aimed to study the relationships between glycolysis, TME, and therapeutic response in esophageal adenocarcinoma (EAC). Materials and Methods: We used the ESTIMATE algorithm to divide EAC patients into ESTIMATE ^high and ESTIMATE ^low groups based on the gene expression data downloaded from TCGA. Weighted gene co-expression network analysis (WGCNA) and Gene Set Enrichment Analysis (GSEA) were performed to identify different glycolytic genes in the TME between the two groups. The prognostic gene signature for overall survival (OS) was established through Cox regression analysis. Impacts of glycolytic genes on immune cells were assessed and validated. Next, we conducted the glycolytic gene mutation analysis and drug therapeutic response analysis between the two groups. Finally, the GEO database was employed to validate the impact of glycolysis on TME in patients with EAC. Results: A total of 78 EAC patients with gene expression profiles and clinical information were included for analysis. Functional enrichment results showed that the genes between ESTIMATE ^high and ESTIMATE ^low groups (N = 39, respectively) were strongly related with glycolytic and ATP/ADP metabolic pathways. Patients in the low-risk group had probabilities to survive longer than those in the high-risk group (p < 0.001). Glycolytic genes had significant impacts on the components of immune cells in TME, especially on the T-cells and dendritic cells. In the high-risk group, the most common mutant genes were TP53 and TTN, and the most frequent mutation type was missense mutation. Glycolysis significantly influenced drug sensitivity, and high tumor mutation burden (TMB) was associated with better immunotherapeutic response. GEO results confirmed that glycolysis had significant impacts on immune cell contents in TME. Conclusion: We performed a comprehensive study of glycolysis and TME and demonstrated that glycolysis could influence the microenvironment and drug therapeutic response in EAC. Evaluation of the glycolysis pattern could help identify the individualized therapeutic regime.

Transcriptional and Epigenetic Bioinformatic Analysis of Claudin-9 Regulation in Gastric Cancer

Gastric cancer is a heterogeneous disease that represents 5% to 10% of all new cancer cases worldwide. Advances in histological diagnosis and the discovery of new genes have admitted new genomic classifications. Nevertheless, the bioinformatic analysis of gastric cancer databases has favored the detection of specific differentially expressed genes with biological significance. Claudins, a family of proteins involved in tight junction physiology, have emerged as the key regulators of cellular processes, such as growth, proliferation, and migration, associated with cancer progression. The expression of Claudin-9 in the gastric cancer tissue has been linked to poor prognosis, however, its transcriptional and epigenetic regulations demand a more comprehensive analysis. Using the neural network promoter prediction, TransFact, Uniprot-KB, Expasy-SOPMA, protein data bank, proteomics DB, Interpro, BioGRID, String, and the FASTA protein sequence databases and software, we found the following: (1) the promoter sequence has an unconventional structure, including different transcriptional regulation elements distributed throughout it, (2) GATA 4, GATA 6, and KLF5 are the key regulators of Claudin-9 expression, (3) Oct1, NF-κB, AP-1, c-Ets-1, and HNF-3β have the higher binding affinity to the CLDN9 promoter, (4) Claudin-9 interacts with cell differentiation and development proteins, (5) CLDN9 is highly methylated, and (6) Claudin-9 expression is associated with poor survival. In conclusion, Claudin-9 is a protein that should be considered a diagnostic marker as its gene promoter region binds to the transcription factors associated with the deregulation of cell control, enhanced cell proliferation, and metastasis.

PI3K Pathway Inhibition with NVP-BEZ235 Hinders Glycolytic Metabolism in Glioblastoma Multiforme Cells

Glioblastoma (GBM) is the most lethal primary brain cancer that lacks effective molecular targeted therapies. The PI3K/AKT/mTOR pathway is activated in 90% of all Glioblastoma multiforme (GBM) tumors. To gain insight into the impact of the PI3K pathway on GBM metabolism, we treated U87MG GBM cells with NVP-BEZ235 (PI3K and mTOR a dual inhibitor) and identified differentially expressed genes with RNA-seq analysis. RNA-seq identified 7803 differentially regulated genes in response to NVP-BEZ235. Gene Set Enrichment Analysis (GSEA) identified two glycolysis-related gene sets that were significantly enriched (p < 0.05) in control samples compared to NVP-BEZ235-treated samples. We validated the inhibition of glycolytic genes by NVP-BEZ235 and examined the impact of the FOXO1 inhibitor (AS1842856) on these genes in a set of GBM cell lines. FOXO1 inhibition alone was associated with reduced LDHA expression, but not ENO1 or PKM2. Bioinformatics analyses revealed that PI3K-impacted glycolytic genes were over-expressed and co-expressed in GBM clinical samples. The elevated expression of PI3K-impacted glycolytic genes was associated with poor prognosis in GBM based on Kaplan-Meier survival analyses. Our results suggest novel insights into hallmark metabolic reprogramming associated with the PI3K-mTOR dual inhibition.

Circβ-catenin promotes tumor growth and Warburg effect of gallbladder cancer by regulating STMN1 expression

Gallbladder cancer (GBC) is the most malignant cancer of the biliary tract cancer and presents poor prognosis. CircRNAs have been identified as critical regulators of multiple stages in tumor progression. In the study, we first demonstrated that circular RNA circβ-catenin expression was upregulated in GBC tissues when compared to adjacent normal tissues and associated with advanced clinical stage and poor prognosis in GBC patients. Silencing of circβ-catenin obviously suppressed GBC cell proliferation and cell cycle progression in vitro, but circβ-catenin overexpression had the opposite effects. In vivo, silencing of circβ-catenin inhibited tumor growth. Furthermore, we also found that circβ-catenin promoted GBC cell lactate production, pyruvate production, ATP quantity, and extracellular acidification rate (ECAR), which suggested that circβ-catenin regulated Warburg effect in GBC. Mechanistic analysis further highlighted that circβ-catenin promoted Stathmin 1 (STMN1) expression through sponging miR-223 in GBC progression. In addition, knockdown of STMN1 inhibited cell growth and Warburg effect in GBC. In summary, our findings indicated that circβ-catenin/miR-223/STMN1 axis could regulate cell growth and Warburg effect in GBC. Targeting circβ-catenin might be a potential therapeutic strategy for GBC.

A Novel Prognostic Model Based on Autophagy-Related Long Non-Coding RNAs for Clear Cell Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, of which the clear cell renal cell carcinoma (ccRCC) accounts for the most subtypes. The increasing discoveries of abundant autophagy-related long non-coding RNAs (ARLNRs) lead to a resurgent interest in evaluating their potential on prognosis prediction. Based on a large number of ccRCC gene samples from TCGA and clinics, ARLNRs analysis will provide a novel perspective into this field.

Methods

We calculated the autophagy scores of each sample according to the expression levels of autophagy-related genes (ARGs) and screened the survival-related ARLNRs (sARLNRs) of ccRCC patients by Cox regression analysis. The high-risk group and the low-risk group were distinguished by the median score of the autophagy-related risk score (ARRS) model. The functional annotations were detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The expression levels of two kinds of sARLNRs in the renal tumor and adjacent normal tissues and cell lines were verified.

Results

There were 146 ARLNRs selected by Pearson analysis. A total of 30 sARLNRs were remarkably correlated with the clinical outcomes of ccRCC patients. Eleven sARLNRs (AC002553.1, AC092611.2, AL360181.2, AP002807.1, AC098484.1, AL513218.1, AC008735.2, MHENCR, AC020907.4, AC011462.4, and AC008870.2) with the highest prognosis value were recruited to establish the ARRS in which the overall survival (OS) in the high-risk group was shorter than that in the low-risk group. ARRS could be treated as an independent prognostic factor and has significant correlations with OS. The distributions of autophagy genes were different between the high-risk group and the low-risk group. In addition, we also found that the expression levels of AC098484.1 in ccRCC cell lines and tumor tissues were lower than those in HK-2 and adjacent normal tissues, but AL513218.1 showed the inverse level. Furthermore, the AC098484.1 expressed decreasingly with the more advanced T-stages, but AL513218.1 gradually increased.

Conclusion

Our study identified and verified some sARLNRs with clinical significances and revealed their potential values on predicting prognoses of ccRCC patients, which may provide a novel perspective for autophagy-related research and clinical decisions.

PGK1 Is a Key Target for Anti-Glycolytic Therapy of Ovarian Cancer: Based on the Comprehensive Analysis of Glycolysis-Related Genes

Reprogramming of energy metabolism is a key hallmark of cancer, which provides a new research perspective for exploring the development of cancer. However, the most critical target of anti-glycolytic therapy for ovarian cancer remains unclear. Therefore, in the present study, Oncomine, GEPIA, and HPA databases, combined with clinical specimens of different histological types of ovarian cancer were used to comprehensively evaluate the expression levels of glycolysis-related metabolite transporters and enzymes in ovarian cancer. We selected phosphoglycerate kinase 1 (PGK1), which showed the greatest prognostic value in the Kaplan-Meier Plotter database, for subsequent validation. Immunochemistry assays confirmed that PGK1 was highly expressed in ovarian cancer. The PGK1 expression level was an independent risk factor for the survival and prognosis of patients with ovarian cancer. Functional analysis showed that the PGK1 expression level was positively correlated with the infiltration of neutrophils. Cell experiments confirmed that inhibiting PGK1 expression in ovarian cancer cells could reduce the epithelial-mesenchymal transition (EMT) process, resulting in loss of cell migration and invasion ability. The small molecule NG52 dose-dependently inhibited the proliferation of ovarian cancer cells. In addition, NG52 reduced the EMT process and reversed the Warburg effect by inhibiting PGK1 activity. Therefore, PGK1 is an attractive molecular target for anti-glycolytic therapy of ovarian cancer.

Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma

Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2. The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.