A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma

Single-cell and bulk RNAseq unveils the immune infiltration landscape and targeted therapeutic biomarkers of psoriasis

Background

Psoriasis represents a multifaceted and debilitating immune-mediated systemic ailment afflicting millions globally. Despite the continuous discovery of biomarkers associated with psoriasis, identifying lysosomal biomarkers, pivotal as cellular metabolic hubs, remains elusive.

Methods

We employed a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA) to initially identify lysosomal genes. Subsequently, to mitigate overfitting and eliminate collinear genes, we applied 12 machine learning algorithms to screen robust lysosomal genes. These genes underwent further refinement through random forest (RF) and Lasso algorithms to ascertain the final hub lysosomal genes. To assess their predictive efficacy, we conducted receiver operating characteristic (ROC) analysis and verified the expression of diagnostic biomarkers at both bulk and single-cell levels. Furthermore, we utilized single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, and Pearson's correlation analysis to elucidate the association between immune phenotypes and hub lysosomal genes in psoriatic samples. Finally, employing the Cellchat algorithm, we explored potential mechanisms underlying the participation of these hub lysosomal genes in cell-cell communication.

Results

Functional enrichment analyses revealed a close association between psoriasis and lysosomal functions. Subsequent intersection analysis identified 19 key lysosomal genes, derived from DEGs, phenotypic genes of WGCNA, and lysosomal gene sets. Following the exclusion of collinear genes, we identified 11 robust genes, further refined through RF and Lasso, yielding 3 hub lysosomal genes (S100A7, SERPINB13, and PLBD1) closely linked to disease occurrence, with high predictive capability for disease diagnosis. Concurrently, we validated their relative expression in separate bulk datasets and single-cell datasets. A nomogram based on these hub genes may offer clinical advantages for patients. Notably, these three hub genes facilitated patient classification into two subtypes, namely metabolic-immune subtype 1 and signaling subtype 2. CMap analysis suggested butein and arachidonic fasudil as preferred treatment agents for subtype 1 and subtype 2, respectively. Finally, through Cellchat and correlation analysis, we identified PRSS3-F2R as potentially promoting the expression of hub genes in the psoriasis group, thereby enhancing keratinocyte-fibroblast interaction, ultimately driving psoriasis occurrence and progression.

Conclusion

Our study identifies S100A7, SERPINB13, and PLBD1 as potential diagnostic biomarkers, offering promising prospects for more precisely tailored psoriatic immunotherapy designs.

Ubenimex suppresses glycolysis mediated by CD13/Hedgehog signaling to enhance the effect of cisplatin in liver cancer

Background

Liver cancer ranks third in fatalities among all cancer-related deaths. As a traditional chemotherapy drug, the application of cis-Diamminedichloroplatinum (II) (cisplatin, CDDP) for the treatment of liver cancer is greatly limited by its side effects and high drug resistance. Therefore, we are in urgent need of a more effective and less toxic CDDP therapeutic regimen. Our research aimed to clarify the possible mechanism of ubenimex in enhancing the effect of CDDP on liver cancer.

Methods

The underlying mechanism was determined using Cell Counting Kit-8 (CCK-8) assay, flow cytometry, immunofluorescence, enzyme-linked immunosorbent assay (ELISA), transwell assay, wound healing assay and western blot assay.

Results

The data indicated that ubenimex suppressed the expression levels of glycolysis-related proteins by decreasing the expression levels of cluster of differentiation 13 (CD13), while overexpression of CD13 could restore the activity of glycolysis. The glycolysis inhibitor 2-deoxy-D-glucose enhanced the antiproliferative effect of ubenimex and CDDP. In addition, the inhibition of the activity levels of the Hedgehog (Hh) pathway members was accompanied by a decrease in CD13 expression, which was reversed following CD13 overexpression. Moreover, ubenimex inhibited the production of lactic acid and adenosine triphosphate (ATP), as well as the expression of key proteins involved in glycolysis, which was similar to the effects caused by the Hh inhibitor cyclopamine. However, the effects of ubenimex were mediated by targeting CD13, while cyclopamine exhibited no effects on CD13, suggesting that Hh signaling occurred in the downstream of CD13. The inhibition of glycolysis by cyclopamine was reduced following CD13 overexpression, which further indicated that ubenimex targeted the CD13/Hh pathway to inhibit glycolysis. Finally, wound healing and transwell assays and cell proliferation and apoptosis analysis demonstrated that ubenimex inhibited glycolysis by alleviating the CD13/Hh pathway, which in turn enhanced the effects of CDDP on inhibiting the progression of liver cancer.

Conclusions

Ubenimex inhibits glycolysis by targeting the CD13/Hh pathway, thus playing an anti-tumor role together with CDDP. This study demonstrated the adjuvant effect of ubenimex from the perspective of Hh signal-dependent glycolysis regulation.

Construction and validation of a novel IGFBP3-related signature to predict prognosis and therapeutic decision making for Hepatocellular Carcinoma

Background

IGFBP3 plays a pivotal role in carcinogenesis by being anomalously expressed in some malignancies. However, the clinical value of IGFBP3 and the role of IGFBP3-related signature in HCC remain unclear.

Methods

Multiple bioinformatics methods were used to determine the expression and diagnostic values of IGFBP3. The expression level of IGFBP3 was validated by RT-qPCR and IHC. A IGFBP3-related risk score (IGRS) was built via correlation analysis and LASSO Cox regression analysis. Further analyses, including functional enrichment, immune status of risk groups were analyzed, and the role of IGRS in guiding clinical treatment was also evaluated.

Results

IGFBP3 expression was significantly downregulated in HCC. IGFBP3 expression correlated with multiple clinicopathological characteristics and demonstrated a powerful diagnostic capability for HCC. In addition, a novel IGRS signature was developed in TCGA, which exhibited good performance for prognosis prediction and its role was further validated in GSE14520. In TCGA and GSE14520, Cox analysis also confirmed that the IGRS could serve as an independent prognostic factor for HCC. Moreover, a nomogram with good accuracy for predicting the survival of HCC was further formulated. Additionally, enrichment analysis showed that the high-IGRS group was enriched in cancer-related pathways and immune-related pathways. Additionally, patients with high IGRS exhibited an immunosuppressive phenotype. Therefore, patients with low IGRS scores may benefit from immunotherapy.

Conclusions

IGFBP3 can act as a new diagnostic factor for HCC. IGRS signature represents a valuable predictive tool in the prognosis prediction and therapeutic decision making for Hepatocellular Carcinoma.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

GOT2 Silencing Promotes Reprogramming of Glutamine Metabolism and Sensitizes Hepatocellular Carcinoma to Glutaminase Inhibitors

Hepatocellular carcinoma (HCC) is one of the primary liver malignancies with a poor prognosis. Glutamic-oxaloacetic transaminase 2 (GOT2) is a highly tissue-specific gene in the liver, but the roles GOT2 plays in the progression of HCC remain unclear. Here, we report that GOT2 is downregulated in HCC tumor tissues and that low expression of GOT2 is associated with advanced progression and poor prognosis. In HCC cells, knockdown of GOT2 promoted proliferation, migration, and invasion. In mouse models of HCC, loss of GOT2 promoted tumor growth as well as hematogenous and intrahepatic metastasis. Mechanistically, silencing of GOT2 enhanced glutaminolysis, nucleotide synthesis, and glutathione synthesis by reprogramming glutamine metabolism to support the cellular antioxidant system, which activated the PI3K/AKT/mTOR pathway to contribute to HCC progression. Furthermore, HCC with low expression of GOT2 was highly dependent on glutamine metabolism and sensitive to the glutaminase inhibitor CB-839 in vitro and in vivo. Overall, GOT2 is involved in glutamine metabolic reprogramming to promote HCC progression and may serve as a therapeutic and diagnostic target for HCC.

SIGNIFICANCE

Altered glutamine metabolism induced by GOT2 loss supports HCC growth and metastasis but confers a targetable vulnerability to glutaminase inhibitors.

Cytochrome B5 type A alleviates HCC metastasis via regulating STOML2 related autophagy and promoting sensitivity to ruxolitinib

The incidence of hepatocellular carcinoma (HCC) is increasing in the world. However, its role and underlying molecular mechanism in HCC progression remain unclear. We found that CYB5A plays a key role in HCC metastasis by inhibiting the JAK1/STAT3 pathway through binding to STOML2. CYB5A combined with STOML2 can predict the outcome of patients. To demonstrate the effect of CYB5A on JAK1 inhibitor function, we applied Ruxolitinib in metastatic tumors with high CYB5A expression and found that it slowed disease progression and prolonged survival in mice. To the best of our knowledge, this study is the first to report the Ruxolitinib effect on the metastatic ability of HCC cells in vivo and in vitro.

Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes

Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified.

Methods: Based on the tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score.

Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981–3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in sensitivity to immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib between the low-risk and high-risk groups.

Conclusion: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.

A Glycolysis-Related Gene Signature Correlates With the Characteristics of the Tumor Immune Microenvironment and Predicts Prognosis in Patients With Hepatocellular Carcinoma

Aim: To develop a glycolysis-related gene signature that correlated with the characteristics of the tumor immune microenvironment and had good predictive power for overall survival (OS) in hepatocellular carcinoma (HCC).

Methods: Gene expression profiles, RNA sequencing data, clinical characteristics and survival information for 407 patients with HCC and 58 healthy controls were downloaded from the TCGA database. GSEA 4.1.0 software was used to evaluate the glycolysis-related pathways enriched in HCC compared to normal liver tissue. Univariate Cox, Least Absolute Shrinkage, Selection Operator, and two-step multivariate Cox analyses were used to construct a glycolysis-related gene signature for prognostic prediction. The glycolysis-related gene signature was combined with clinical characteristics to generate a nomogram. Tumor-infiltrating immune cell profiles and PD-L1 protein expression in HCC tissues were investigated.

Results: The gene expression profiles of HCC tissues were enriched in glycolysis-related pathways. A glycolysis-related gene signature was used to categorize patients as high-risk or low-risk, where high-risk patients had significantly worse OS. Receiver operating characteristic curves confirmed the predictive capability of the glycolysis-related gene signature for OS (AUC >0.80). There was a significant difference in M0 macrophage (p = 0.017), dendritic cell (p = 0.043), B cell (p = 0.0018), CD4 T cell (p = 0.003), Treg (p = 0.01) and mast cell (p = 0.02) content and PD-L1 protein expression (p = 0.019) between HCC tissues in patients in the high-risk and low-risk groups.

Conclusion: We established a glycolysis-related gene signature for OS in HCC that was predictive in training and test TCGA cohorts and correlated with the characteristics of the HCC tumor immune microenvironment. The glycolysis-related gene signature may guide clinical decision-making concerning patient selection for immunotherapy in HCC.

Circ_0036412 affects the proliferation and cell cycle of hepatocellular carcinoma via hedgehog signaling pathway

Background

Hepatocellular carcinoma (HCC), as the most common type of liver cancer, is characterized by high recurrence and metastasis. Circular RNA (circRNA) circ_0036412 was selected for studying the underlying mechanisms of HCC.

Methods

Quantitative real time-polymerase chain reaction (qRT-PCR) and western blot analyzed gene and protein expression. Functional experiments evaluated HCC cell proliferation, apoptosis and cell cycle in vitro. In vivo experiments detected HCC carcinogenesis in vivo. Fluorescence in situ hybridization (FISH) assays evaluated the subcellular distribution. Luciferase reporter, Chromatin immunoprecipitation (ChIP), DNA pulldown, RNA-binding protein immunoprecipitation (RIP), and RNA pulldown assays detected the underlying mechanisms.

Results

Circ_0036412 is overexpressed in HCC cells and features circular structure. PRDM1 activates circ_0036412 transcription to regulate the proliferation and cell cycle of HCC cells in vitro. Circ_0036412 modulates Hedgehog pathway. GLI2 propels HCC growth in vivo. Circ_0036412 up-regulates GLI2 expression by competitively binding to miR-579-3p, thus promoting the proliferation and inhibiting cell cycle arrest of HCC cells. Circ_0036412 stabilizes GLI2 expression by recruiting ELAVL1. Circ_0036412 propels the proliferation and inhibits cell cycle arrest of HCC cells in vitro through Hedgehog pathway.

Conclusions

Circ_0036412 affects the proliferation and cell cycle of HCC via Hedgehog signaling pathway. It offers an insight into the targeted therapies of HCC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03305-x.

Expression of IER3 in hepatocellular carcinoma: clinicopathology, prognosis, and potential regulatory pathways

Background

Immediate early response 3 (IER3) is correlated to the prognosis of several cancers, but the precise mechanisms underlying the regulation by IER3 of the occurrence and development of hepatocellular carcinoma (HCC) remain unknown.

Methods

The expression level of IER3 was examined by using in-house immunohistochemistry (IHC), public gene chip, and public RNA-sequencing (RNA-seq). The standardized mean difference (SMD) was calculated to compare the expression levels of IER3 between HCC patients and controls. The summary receiver operating characteristics (sROC) was plotted to comprehensively understand the discriminatory capability of IER3 between HCC and non-HCC group. The Kaplan-Meier curves and the combined hazard ratios (HRs) were used to determine the prognostic value of IER3 in HCC. Moreover, differentially expressed genes (DEGs) and co-expression genes (CEGs) were used to explored the molecular mechanisms of IER3 underlying HCC. hTFtarget was used to predict the transcription factors (TFs) of IER3. The binding site of TFs and the IER3 promoter region was forecasted using the JASPAR website. The relevant ChIP-seq data were used to determine whether TF peaks were present in the IER3 transcription initiation.

Results

A significantly increased expression of IER3 protein was found in HCC tissue relative to non-HCC tissue as detected by IHC (p < 0.001). Compared to 1,263 cases of non-HCC tissues, IER3 in 1483 cases of HCC tissues was upregulated (SMD = 0.42, 95% confidence interval [CI] [0.09-0.76]). The sROC showed that IER3 had a certain ability at differentiating HCC tissues (area under the curve (AUC) = 0.65, 95% CI [0.61-0.69]). Comprehensive analysis of the effect of IER3 on the prognosis of patients with HCC demonstrated that higher IER3 expression was associated with poor prognosis in HCC (HRs = 1.30, 95% CI [1.03-1.64]). Pathway enrichment analysis revealed that IER3-related genes were mostly enriched in the PI3K-Akt signaling pathway, cancer-related signaling pathways, the p53 signaling pathway, and other signaling pathways. Regulatory factor X5 (RFX5) was identified as a possible regulator of IER3-related TF.

Conclusion

IER3 may be a potential prognostic marker for HCC. The molecular mechanisms of IER3 in HCC warrant further study.

Circular RNA circKIF2A Contributes to the Progression of Neuroblastoma Through Regulating PRPS1 Expression by Sponging miR-377-3p

Neuroblastoma is a malignant tumor originating from the primitive neural crest. Circular RNA (circRNA) Kinesin Superfamily Protein 2A (circKIF2A, also known as hsa_circ_0129276) has been reported to be upregulated in neuroblastoma. However, the molecular mechanism of circKIF2A participated in neuroblastoma is poorly defined. We analyzed the expression levels of circKIF2A, microRNA-377-3p (miR-377-3p), and phosphoribosyl pyrophosphate synthetase 1 (PRPS1) in neuroblastoma tissues and cell lines (SK-N-AS and LAN-6) and explored their roles. The expression levels of CircKIF2A and PRPS1 were increased and that of miR-377-3p were decreased in 21 neuroblastoma tissues and cells. Functionally, the silencing of circKIF2A inhibited cell proliferation, migration, invasion, and glycolysis, boosted apoptosis in neuroblastoma cells in vitro, and blocked the growth of subcutaneously transplanted tumors in nude mice. Mechanically, circKIF2A could work as a sponge of miR-377-3p to enhance PRPS1 expression. CircKIF2A knockdown impedes cell proliferation, metastasis, and glycolysis partly by regulating the miR-377-3p/PRPS1 axis, suggesting that targeting circKIF2A can be a feasible therapeutic strategy for neuroblastoma.

A Novel Nine-Gene Signature Associated With Immune Infiltration for Predicting Prognosis in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its prognosis remains unsatisfactory. The identification of new and effective markers is helpful for better predicting the prognosis of patients with HCC and for conducting individualized management. The oncogene Aurora kinase A (AURKA) is involved in a variety of tumors; however, its role in liver cancer is poorly understood. The aim of this study was to establish AURKA-related gene signatures for predicting the prognosis of patients with HCC.

Methods: We first analyzed the expression of AURKA in liver cancer and its prognostic significance in different data sets. Subsequently, we selected genes with prognostic value related to AURKA and constructed a gene signature based on them. The predictive ability of the gene signature was tested using the HCC cohort development and verification data sets. A nomogram was constructed by integrating the risk score and clinicopathological characteristics. Finally, the influence of the gene signature on the immune microenvironment in HCC was comprehensively analyzed.

Results: We found that AURKA was highly expressed in HCC, and it exhibited prognostic value. We selected eight AURKA-related genes with prognostic value through the protein-protein interaction network and successfully constructed a gene signature. The nine-gene signature could effectively stratify the risk of patients with HCC and demonstrated a good ability in predicting survival. The nomogram showed good discrimination and consistency of risk scores. In addition, the high-risk group showed a higher percentage of immune cell infiltration (i.e., macrophages, myeloid dendritic cells, neutrophils, and CD4+T cells). Moreover, the immune checkpoints SIGLEC15, TIGIT, CD274, HAVCR2, and PDCD1LG2 were also higher in the high-risk group versus the low-risk group.

Conclusions: This gene signature may be useful prognostic markers and therapeutic targets in patients with HCC.

A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

Glycolysis related gene expression signature in predicting prognosis of laryngeal squamous cell carcinoma

Researches have suggested that aerobic glycolysis can reflect the development and progression of most carcinomas. We aimed to investigate whether glycolysis-related genes (GRGs) are associated with overall survival in laryngeal squamous cell carcinoma (LSCC). Here, we identified differentially expressed GRGs in TCGA dataset and microarray sample of GSE27020 from GEO database. A set of two glycolytic gene signatures, including DDIT4 and PLOD2 was screened through Cox and Lasso regression. The risk score was calculated using the gene expression of the two GRGs. The high-risk group presented a poor prognosis through Kaplan–Meier method. The ROC curve indicated good prediction performance in survival based on the validation of four cohorts. Univariate and multivariate Cox regression analyses suggested that two-gene signature could be an independent risk factor in LSCC. A total of 17 LSCC patients were enrolled to clarify the genetic expression through using reverse transcription-polymerase chain reaction (RT-PCR). A visualized nomogram was then constructed to predict 1-, 3-, and 5-year overall survival. Taken together, two novel glycolytic gene signatures were discovered and validated, providing a potential therapeutic and overall survival (OS)-prediction biomarker for LSCC.

A novel prognostic signature based on four glycolysis-related genes predicts survival and clinical risk of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common cancer with limited cure and poor survival. In our study, a bioinformatic analysis was conducted to investigate the role of glycolysis in the pathogenesis and progression of HCC.

METHODS

Single-sample gene set enrichment analysis (ssGESA) was used to calculate enrichment scores for each sample in TCGA-LIHC and GEO14520 according to the glycolysis gene set. Weighted gene co-expression network analysis identified a gene module closely related to glycolysis, and their function was investigated. Prognostic biomarkers were screened from these genes. Cox proportional hazard model and least absolute shrinkage and selection operator regression were used to construct the prognostic signature. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curve analyses evaluated the prediction performance of the prognostic signature in TCGA-LIHC and ICGC-LIRI-JP. Combination analysis data of clinical features and prognostic signature constructed a nomogram. Area under ROC curves and decision curve analysis were used to compare the nomogram and its components.

RESULTS

The glycolysis pathway was upregulated in HCC and was unfavorable for survival. The determined gene module was mainly enriched in cell proliferation. A prognostic signature (CDCA8, RAB5IF, SAP30, and UCK2) was developed and validated. KM and ROC curves showed a considerable predictive effect. The risk score derived from the signature was an independent prognostic factor. The nomogram increased prediction efficiency by combining risk signature and TNM stage and performed better than component factors in net benefit.

CONCLUSION

The gene signature may contribute to individual risk estimation, survival prognosis, and clinical management.

Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population

BACKGROUND

Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers.

METHODS

First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples.

RESULTS

Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10^-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets.

CONCLUSIONS

Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.