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Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer‐related pathways. Compared with published models, our model has a higher C‐index and greater risk value. In summary, this four‐gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator., Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. Compared with published models, our model has a higher C‐index and greater risk value. This four‐gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.

Introduction Melanoma is a highly aggressive malignant skin tumor as well as the primary reason for skin cancer‐specific deaths. We first identified immune‐related long noncoding RNA (lncRNA) prognostic signature and found potential immunotherapeutic targets for melanoma cancer. Methods RNA‐seq data and clinical features of melanoma samples were obtained from The Cancer Genome Atlas. Samples of melanoma were randomly assigned to the training and testing cohort. The immune‐related lncRNA signature was then obtained via using univariate, LASSO, and multivariate Cox analysis of patients in the training cohort. Eight significant immune‐related lncRNA signature was then subsequently obtained through correlation analysis between immune‐related genes and lncRNAs. The association between risk score and immune cell infiltration was finally assessed using TIMER and CIBERSORT. Results Three hundred and fifty‐six immune‐related lncRNAs were obtained. Among them, eight immune‐related lncRNAs were identified to build a prognostic risk signature model. The model's performance was then confirmed using the Kaplan–Meier curves, risk plots, and time‐dependent receiver‐operating characteristic curves in the training cohort. The risk score was identified and confirmed as an independent prognostic factor through univariate and multivariate Cox regression analyses. These results were further verified in the testing and whole cohorts. CIBERSORT algorithm showed that the infiltration levels of T cells CD8, M1 macrophages, plasma cells, T cells CD4 memory activated, T cells gamma delta, and mast cells activated were significantly lower in the high‐risk group while the infiltration level of macrophages M0 was significantly lower in the low‐risk group. Conclusion The immune‐related lncRNA signature offers prognostic markers and potential immunotherapeutic targets for melanoma., We applied univariate Cox, LASSO, and multivariate Cox regression analysis to identify immune‐related lncRNAs with remarkable prognostic potential and constructed a multiple immune‐related lncRNAs signature to predict melanoma' prognosis. lncRNA signatures of immune‐related offers prognostic markers and potential immunotherapeutic targets for melanoma.

Background Ovarian cancer (OC) is the most lethal gynaecological tumor. Changes in glycolysis have been proven to play an important role in OC progression. We aimed to identify a novel glycolysis-related gene signature to better predict the prognosis of patients with OC. Methods mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype Tissue Expression (GTEx) database. The “limma” R package was used to identify glycolysis-related differentially expressed genes (DEGs). Then, a multivariate Cox proportional regression model and survival analysis were used to develop a glycolysis-related gene signature. Furthermore, the TCGA training set was divided into two internal test sets for validation, while the ICGC dataset was used as an external test set. A nomogram was constructed in the training set, and the relative proportions of 22 types of tumor-infiltrating immune cells were evaluated using the “CIBERSORT” R package. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined by single-sample gene set enrichment analysis (ssGSEA) with the “GSVA” R package. Finally, the expression and function of the unreported signature genes ISG20 and SEH1L were explored using immunohistochemistry, western blotting, qRT-PCR, proliferation, migration, invasion and xenograft tumor assays. Results A five-gene signature comprising ANGPTL4, PYGB, ISG20, SEH1L and IRS2 was constructed. This signature could predict prognosis independent of clinical factors. A nomogram incorporating the signature and three clinical features was constructed, and the calibration plot suggested that the nomogram could accurately predict the survival rate. According to ssGSEA, the signature was associated with KEGG pathways related to axon guidance, mTOR signalling, tight junctions, etc. The proportions of tumor-infiltrating immune cells differed significantly between the high-risk group and the low-risk group. The expression levels of ISG20 and SEH1L were lower in tumor tissues than in normal tissues. Overexpression of ISG20 or SEH1L suppressed the proliferation, migration and invasion of Caov3 cells in vitro and the growth of xenograft tumors in vivo. Conclusion Five glycolysis-related genes were identified and incorporated into a novel risk signature that can effectively assess the prognosis and guide the treatment of OC patients.

Background Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, ranks as the fifth most common cancer and has been the second most frequent cause of cancer-related death. RNA binding proteins (RBPs) are proteins that interact with different classes of RNA and are commonly detected in cells. Methods We used RNA sequencing data from TCGA to display dysfunctional RBPs microenvironments and provide potential useful biomarkers for HCC diagnosis and prognosis. Results 330 differently expressed RBPs (208 upregulated and 122 downregulated) were identified. KEGG were mainly enriched in RNA degradation, Influenza A, Hepatitis C, RIG-I-like receptor signaling pathway, Herpes simplex virus 1 infection and RNA transport. CBioPortal results demonstrated that these genes were altered in 50 samples out of 357 HCC patients (14%) and the amplification of BRCA1 was the largest frequent copy-number alteration. Conclusion Based on the online database, we identified novel RBPs markers for the prognosis of hepatocellular carcinoma.

Background Prognostic indicators in lung adenocarcinoma (LUAD) have been seeking under database analysis, and remarkable advance is on the way. Methods This study calculated the scores of stromal and immune components of the tumor microenvironment (TME) in 551 LUAD samples using the ESTIMATE algorithm on The Cancer Genome Atlas (TCGA) database. R package ''limma'' was used to selected differentially expressed genes (DEG). We have analyzed the DEGs by means of Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments. The protein‐protein network, univariate Cox analysis, and Lasso regression analysis were performed to selected survival‐related genes. Gene Set Enrichment Analysis (GSEA) represented the enriched pathway of CC chemokine receptor 2 (CCR2). The ratios of immune cells in the TME of each LUAD sample were obtained using the R package "limma" and CIBERSORT algorithm in R 4.0.2. Results The ImmuneScore was positively correlated with prognosis regarding survival rate, T classification of TNM stages, and clinicopathological staging characteristics. GO and KEGG enrichments showed DEGs were associated with immune‐related activities. Three genes of LUAD were selected from the PPI network and Cox proportional hazards regression analysis. CCR2 was the most survival correlated gene by Lasso regression analysis. GSEA results showed that C2 kegg gene sets in the CCR2 high‐expression group were mainly enriched in the B cell or T cell receptor signaling pathway and natural killer cell‐mediated cytotoxicity. Correlation of CCR2 expression with prognosis was conducted, implicating a positive correlation with the prognosis of survival rate and M classification, negative correlation with the prognosis of T and N classifications. The correlation between CCR2 and tumor‐infiltrating immune cells (TICs) was analyzed, and 14 kinds of TICs were found closely correlated with CCR2 expression through difference analysis. Conclusion Therefore, CCR2 has prognostic value as an immune indicator in LUAD., The violin diagram showed nine kinds of TICs were positively correlated with CCR2 expression, including memory B cell , CD8+ T cells, gamma delta T cells, monocytes, resting and actived T cells memory, macrophage M1, resting dendritic cells, and resting mast cells; five kinds of TICs were negatively correlated with CCR2 expression, including follicular helper T cells, activated NK cells, macrophage M0, activated dendritic cells, and activated mast cells. These results further supported that the levels of CCR2 affected the immune activity of TME. ​

Glioma accounts for 80% of all malignant brain tumours and is the most common adult primary brain tumour. Age is an important factor affecting the development of cancer, as somatic mutations accumulate with age. Here, we aimed to analyse the significance of age-dependent non-silent somatic mutations in glioma prognosis. Histological tumour grade depends on age at diagnosis in patients with IDH1, TP53, ATRX, and EGFR mutations. Age of patients with wild-type IDH1 and EGFR increased with increase in tumour grade, while the age of patients with IDH1 or EGFR mutation remained constant. However, the age of patients with EGFR mutation was higher than that of patients with IDH1 mutation. The hierarchical clustering of patients was dominantly separated by IDH1 and EGFR mutations. Furthermore, patients with IDH1 mutation were dominantly separated by TP53 and ATRX double mutation and its double wild-type counterpart. The age of patients with ATRX and TP53 mutation was lower than that of patients with wild-type ATRX and TP53. Patients with the double mutation showed poorer prognosis than those with the double wild type genotype. Unlike IDH1 mutant, IDH1 wild-type showed upregulation of expression of epithelial mesenchymal transition associated genes.

We developed a predictive model associated with ferroptosis to provide a more comprehensive view of esophageal squamous cell carcinoma (ESCC) immunotherapy. Gene expression data and corresponding clinical outcomes were obtained from the GEO and The Cancer Genome Atlas (TCGA) databases, and a ferroptosis-related gene set was obtained from the FerrDb database. We identified 45 ferroptosis-related genes that were differentially expressed, including enrichment in genes involved in the immune system process. We established a ferroptosis-related gene-based prognostic model based on the results of univariate Cox regression and multivariate Cox regression analyses, with an area under the curve (AUC) of 0.76 (3 years). We found that the patients with low-risk scores showed a higher proportion of CD8+ T cells, CD4+ memory activated T cells, etc. Finally, a predictive ferroptosis-related prognostic nomogram, which included the predictive values of age, gender, grade, TNM stage, and risk score, was established to predict overall survival. In sum, we developed a ferroptosis-related gene-based prognostic model that provides novel insights into the prediction of ESCC prognosis and identifies the relevance of the immune microenvironment for patient outcomes., Graphical Abstract, Lu et al. developed a ferroptosis-related gene-based prognostic model that provides novel insights into the prediction of ESCC prognosis and identifies the relevance of the immune microenvironment for patient outcomes.

Hepatocellular carcinoma (HCC) remains an extremely lethal disease worldwide. High-throughput methods have revealed global transcriptome dysregulation; however, a comprehensive investigation of the complexity and behavioral characteristics of the competing endogenous RNA (ceRNA) network in HCC is lacking. In this study, we extracted the transcriptome (RNA) sequencing data of 371 HCC patients from The Cancer Genome Atlas platform. With the comparison of the high Myc expression (Mychigh) tumor and low Myc expression (Myclow) tumor groups in HCC, we identified 1,125 differentially expressed (DE) mRNAs, 589 long non-coding RNAs (lncRNAs), and 93 microRNAs (miRNAs). DE RNAs predicted the interactions necessary to construct an associated Myc ceRNA network, including 19 DE lncRNAs, 5 miRNAs, and 72 mRNAs. We identified a significant signature (long intergenic non-protein-coding [LINC] RNA 2691 [LINC02691] and LINC02499) that effectively predicted overall survival and had protective effects. The target genes of microRNA (miR)-212-3p predicted to intersect with DE mRNAs included SEC14-like protein 2 (SEC14L2) and solute carrier family 6 member 1 (SLC6A1), which were strongly correlated with survival and prognosis. With the use of the lncRNA-miRNA-mRNA axis, we constructed a ceRNA network containing four lncRNAs (LINC02691, LINC02499, LINC01354, and NAV2 antisense RNA 4), one miRNA (miR-212-3p), and two mRNAs (SEC14L2 and SLC6A1). Overall, we successfully constructed a mutually regulated ceRNA network and identified potential precision-targeted therapies and prognostic biomarkers., Graphical abstract, Zhang et al. provide evidence that a ceRNA-based SEC14L2/SLC6A1 axis has an impact on the changes in the tumor immune microenvironment and might be a novel important prognostic factor associated with the diagnosis and prognosis of HCC.

Testicular cancer is the most common malignant tumor in young men, and its incidence has increased in recent years. The tumor microenvironment (TME) plays a crucial role in the development and progression of tumors; however, the TME of testicular germ cell tumor (TGCT) is poorly understood. In this study, we downloaded information for 156 TGCT cases from The Cancer Genome Atlas (TCGA) database, used the ESTIMATE method to determine immune and stromal scores, and used CIBERSORT to calculate the proportion of tumor-infiltrating immune cells (TICs). The differentially expressed genes were subjected to a COX regression analysis and used for the construction of a protein–protein interaction (PPI) network. Toll-like receptor 2 (TLR2) was identified as a predictive marker by combining the results of the Cox regression analysis and PPI network. A survival analysis showed that TLR2 was positively correlated with TGCT survival. A gene set enrichment analysis indicated that genes in the high TLR2 expression group were enriched for cell adhesion molecules (CAMs) and the chemokine signaling pathway, and genes in the low TLR2 expression group were mainly enriched in the spliceosome. Regarding proportions of TICs, naive B cells and follicular helper T cells were negatively correlated with the expression of TLR2. This suggests that as TLR2 expression increases, the immunocompetence of the TME decreases. The expression of TLR2 may affect the prognosis of TGCT, suggesting that this locus can be used as a prognostic factor for TGCT.

Weijie Xie,1 Huan Guo,1 Jiawei Zhang,1 Li Hu,2 Yuqi Wu,1 Xiangwei Wang1,3 1Department of Urology & Carson International Cancer Center, Shenzhen University General Hospital & Shenzhen University Clinical Medical Academy Center, Shenzhen University, Shenzhen, People’s Republic of China; 2Department of Physiology, Shantou University of Medical College, Shantou, People’s Republic of China; 3Department of Urology, 3rd Affiliated Hospital and Department of Perioperative Medicine of Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, People’s Republic of ChinaCorrespondence: Xiangwei Wang; Yuqi WuDepartment of Urology & Carson International Cancer Center, Shenzhen University General Hospital & Shenzhen University Clinical Medical Academy Center, Shenzhen University, No. 1098, Xueyuan Road, Shenzhen University City, Nanshan District, Shenzhen, People’s Republic of ChinaTel +86 17796349157Fax +86 075521839000Email winn0324@szu.edu.cn; wuyq0531@szu.edu.cnPurpose: Prostate cancer is the most common malignant urinary tumor among men. Treatments are currently unsatisfactory for advanced prostate cancer. Cancer biology remains the basis for developing new antitumor drugs. Therefore, it is crucial to study the metabolic reprogramming, immune microenvironment, and immune evasion of tumors. This study aimed to clarify the relationship between tumor glycolysis and immune function in prostate cancer.Materials and Methods: We downloaded the gene expression matrix and clinical data of prostate cancer from The Cancer Genome Atlas. We studied the expression profiles and prognostic significance of glycolysis-related genes and used CIBERSORT to identify the proportion of tumor-infiltrating immune cells. Through differential gene expression analysis, gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and correlation analysis, we further explored the relationship between glycolytic activity and immune function. We also performed immunohistochemistry, Western blot and RT-qPCR experiments using human prostate cancer tissue and cell lines to verify the expression of some glycolytic genes, macrophage infiltration and polarization.Results: Among glycolysis-related genes, the expression of SLC16A3 in prostate cancer tissues was lower than that in normal tissues, but its high expression was associated with poor prognosis. In the high SLC16A3 expression group, several glycolysis-related genes also showed high expression, which was confirmed by immunohistochemistry experiments and Western blot. In high-glycolysis group, the expression of immune-related genes and the interleukin-17 (IL-17) signaling pathway were upregulated. CD8+ T cells, regulatory T cells, macrophages, and other immune cells were highly enriched. Among them, M2 macrophage infiltration was associated with poor prognosis.Conclusion: The enhanced glycolytic activity of prostate cancer may contribute to the formation of a pro-tumor immune microenvironment. The IL-17 signaling pathway may play an important mediating role in the interaction between tumor glycolysis and immune function.Keywords: glycolysis, tumor-infiltrating immune cells, IL-17 signaling pathway, TCGA

A primary factor in tumor morbidity and mortality, lung adenocarcinoma (LUAD) is known to be a major subtype of lung cancer, having the lowest survival rate among all other cancers. Using The Cancer Genome Atlas (TCGA) database the relationship between the immune infiltrate and the NUP62CL was explored and the value of the NUP62CL expression in the prognosis and diagnosis LUAD was examined. Using the logistic regression and the Wilcoxon signed‐rank test the relationship between the NUP62CL and the clinico‐pathological features was analyzed. There was a significant correlation between the clinical stage (p = 0.005), the N (p = 0.004), and the decreased expression of NUP62CL. The prognosis of LUAD with high NUP62CL expression was revealed to be worse than that with low NUP62CL expression (p, High expression of NUP62CL was associated with low OS in patients with LUAD. Cox regression model analysis indicated that NUP62CL was an important prognostic factor of LUAD. Maintaining telomere recombination may be an important signaling pathway for NUP62CL to play a role in LUAD. In addition, NUP62CL was related to the infiltration level of memory CD4+ T cells.

Background Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC. Methods Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature. Results Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively. Conclusion These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Background Hepatocellular carcinoma (HCC) is one of the most common and fatal cancers worldwide. HCC invasion and metastasis are crucial for its poor prognosis. SH3PXD2B is a scaffold protein and critical for intravascular and extravascular invasion and metastasis of various types of tumors. However, the role of SH3PXD2B in HCC progression remains unclear. Methods The levels of SH3PXD2B mRNA transcripts in the TCGA database and SH3PXD2B protein expression in the Human Protein Atlas were analyzed. Furthermore, the levels of SH3PXD2B expression in clinical samples were analyzed by quantitative RT-PCR and immunohistochemistry. The potential association of the levels of SH3PXD2B expression with clinicopathological characteristics, overall survival (OS), and recurrence-free survival (RFS) of HCC patients was analyzed. The impact of SH3PXD2B silencing by shRNA-based lentivirus transduction on the proliferation and invasion of human HCC Hep3B and Huh7 cells was determined. Results SH3PXD2B expression was up-regulated in HCC tissues in the TCGA and Human Protein Atlas as well as clinical samples, relative to that of non-tumor liver samples. The levels of SH3PXD2B expression in HCC tissues were significantly associated with higher HBV infection rate, higher HCC grades and TNM stages, higher Ki-67 expression, higher serum α-fetoprotein (AFP), a shorter OS and RFS of HCC patients. Functionally, SH3PXD2B silencing significantly inhibited the formation and function of invadopodia and the invasion of Hep3B and Huh7 cells, but did not affect their proliferation in vitro. Conclusions Our data suggest that SH3PXD2B may promote the invasion and metastasis of HCC and be a valuable therapeutic target and biomarker for treatment and prognosis of HCC.

Background RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. Methods We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan–Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. Results The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. Conclusions We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.

Purpose Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients. Methods The RNA‐seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value. Results Based on the immune score, we found that the high‐score group showed better clinical outcomes than the low‐score group. Twenty‐five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low‐risk group had longer overall survival than the high‐risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25‐gene prognostic signatures and several clinicopathological prognostic factors was constructed. Conclusions This study revealed the significance of immune infiltration and constructed a 25‐gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer., Our study constructed a 25‐gene prognostic signatures model that associated with immune infiltration. And it can be used as an independent prognostic factor for predicting clinical outcomes in patients with breast cancer.

Junqing Li,1,2,* Wenjie Zhou,1,2,* Jitao Wei,3 Xing Xiao,3 Tailai An,1 Wenhui Wu,1 Yulong He1,2 1Digestive Disease Center, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, People’s Republic of China; 2Department of Gastrointestinal Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People’s Republic of China; 3Scientific Research Center, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wenhui Wu Tel +86 18928805498Email doctorwuzsqy@126.comYulong He Tel +86 18922282223Email heyulong@mail.sysu.edu.cnPurpose: To investigate the prognostic value and biological function of RNA binding proteins (RBPs) in stomach adenocarcinoma (STAD).Materials and Methods: Datasets of the differentially expressed genes (DEGs) were downloaded from the TCGA-based (The Cancer Genome Atlas) GEPIA database, from which the differentially expressed RBPs were determined. Functions and prognostic values of these determined RBPs were systematically investigated by a series of methods in bioinformatics analysis. In addition, transwell assays were performed to explore the effect of PTBP1 in STAD cells.Results: Three hundred and sixty-two differentially expressed RBPs were determined, with 331 up-regulated and 31 down-regulated. Seven RBPs (PTBP1, PPIH, SMAD5, MSI2, RBM15, MRPS17, and ADAT3) were identified to be prognosis-related and adopted to construct a prognostic model. Compared with low-risk patients in TCGA training cohort, TCGA testing cohort and GEO cohort, high-risk patients had poorer overall survival (OS). The area under the ROC curves of this prognostic model were 0.804, 0.644 and 0.581 for TCGA training cohort, TCGA testing cohort and GEO cohort, respectively, justifying itself as a good prognostic model with reliable predictive ability. Using the seven identified RBPs, we then constructed a nomogram to generate a clinical utility model. The regulatory networks and functions of the seven RBPs were then investigated, the results of which demonstrated that MRPS17 and PTBP1 reduced the number of infiltrated immune cells. In-vitro experiments showed that the downregulation of PTBP1 weakened the migration and invasion capability of AGS and HGC27 cells.Conclusion: The seven-gene signature can be used as a reliable STAD prognostic biomarker, and these findings help us better understand the prognostic roles and functions of RBPs in STAD.Keywords: RNA binding proteins, stomach adenocarcinoma, TCGA, prognostic model, infiltrated immune cells

Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set GSE50161 was used to construct a co‐expression network for weighted gene co‐expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axonal guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM., The molecular mechanisms underlying the pathogenesis of glioblastoma multiforme remain unclear. In this study, data set GSE50161 was used to construct a co‐expression network for analysis. We found the two modules (dubbed brown and turquoise) and eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB and GARNL3 that were most strongly associated with GBM. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM.

Background Lung adenocarcinoma (LUAD) is the most common pathology subtype of lung cancer. In recent years, immunotherapy, targeted therapy and chemotherapeutics conferred a certain curative effects. However, the effect and prognosis of LUAD patients are different, and the efficacy of existing LUAD risk prediction models is unsatisfactory. Methods The Cancer Genome Atlas (TCGA) LUAD dataset was downloaded. The differentially expressed immune genes (DEIGs) were analyzed with edgeR and DESeq2. The prognostic DEIGs were identified by COX regression. Protein-protein interaction (PPI) network was inferred by STRING using prognostic DEIGs with p value Results In total,1654 DEIGs were identified, of which 436 genes were prognostic. Gene functional enrichment analysis indicated that the DEIGs were involved in inflammatory pathways. We constructed 4 models using DEIGs. Finally, model 4, which was constructed using the 436 DEIGs performed the best in prognostic predictions, the receiver operating characteristic curve (ROC) was 0.824 for 3 years, 0.838 for 5 years, 0.834 for 10 years. High levels of FERMT2, FKBP3 and low levels of SMAD9, GATA2, ITIH4 expression are related to the poor overall survival in LUAD (p Conclusions In our study, we built an optimal prognostic signature for LUAD using DEIGs and verified the expression of selected genes in LUAD. Our result suggests immune signature can be harnessed to obtain prognostic insights.

Background Tumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients. Methods Gene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments. Results An EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-β/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion. Conclusion The EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis.

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune‐related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17‐IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17‐IRGP signature noticeably split patients into high‐ and low‐risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll‐like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17‐IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

Our analysis showed that ovarian cancer prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish between the tumor stage and histological grade in patients with ovarian cancer., Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis‐related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.

Qianhan Lin,1,2,* Xiaoxue Ma,1,2,* Shunxue Hu,3 Rui Li,1,2 Xuan Wei,1,2 Bing Han,1 Yanhui Ma,1 Peishu Liu,1,2,4 Yingxin Pang1,2,4 1Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, People’s Republic of China; 2Key Laboratory of Gynecologic Oncology of Shandong Province, Jinan, 250012, Shandong, People’s Republic of China; 3Department of Pathology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, People’s Republic of China; 4Shandong Engineering Laboratory for Urogynecology, Jinan, 250012, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Peishu LiuDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, People’s Republic of ChinaTel +8618560081988Email peishuliu@126.comYingxin PangDepartment of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, People’s Republic of ChinaTel +8618560081127Email yingxinpang@126.comPurpose: Nucleolin (NCL) is a multifunctional protein with oncogenic properties. NCL expression levels have been linked to the outcomes of various malignancies, but the clinical value of NCL in patients with endometrial carcinoma (EC) remains unclear. Here, the expression of NCL in EC tissues and its associations with patient outcomes were assessed.Patients and Methods: Data on NCL mRNA expression in EC and adjacent nonneoplastic tissues from The Cancer Genome Atlas (TCGA) were analyzed. In addition, NCL protein expression in 82 endometroid endometrial adenocarcinoma tissues and 15 non-malignant tissues was detected by immunohistochemistry.Results: Elevated NCL expression was markedly correlated with serous endometrial carcinoma (P< 0.001), advanced stage (P=0.029), and grade 3 (P< 0.001). High NCL levels were associated with poorer overall survival (OS) and disease-free survival (DFS) compared with intermediate or low NCL levels (OS: P=0.001, DFS: P=0.006). The multivariate Cox proportional hazards model showed that NCL expression was an independent poor prognostic factor for DFS (HR=1.282, CI=1.027– 1.601, P=0.028). A similar correlation between high expression levels of NCL and unfavorable DFS was found in endometrioid endometrial adenocarcinoma (HR=1.411, CI=1.083– 1.840, P=0.011). Positive extra-nuclear NCL expression (HR=3.377, 95% CI=1.029– 11.186, P=0.046) and low nuclear NCL expression (HR=0.233, 95% CI=0.068– 0.796, P=0.020) were independent prognostic factors for DFS in endometrioid endometrial adenocarcinoma.Conclusion: Heterotopic NCL is a potential prognostic biomarker for EC. Inhibiting the distribution of NCL from the nucleus to the cytoplasm and membrane may be a promising therapeutic strategy to improve outcomes in patients with EC with high NCL expression.Keywords: nucleolin, endometrial carcinoma, prognostic marker, TCGA

Background Utrophin (UTRN), as a tumor suppressor gene, is involved in various cancer progression. The function of UTRN in the melanoma process and the related molecular mechanisms are still unclear. Herein, we studied the function of UTRN in melanoma growth and the relevant molecular mechanisms. Methods Using the GEO database and UCSC Xena project, we compared the expression of UTRN in non-cancerous and melanoma tissues. Immunohistochemistry (IHC) staining, qRT-PCR and Western Blot (WB) were performed to evaluate UTRN expression in clinical samples. A total of 447 cases with UTRN expression data, patient characteristics and survival data were extracted from TCGA database and analyzed. After stable transduction and single cell cloning, the proliferation ability of A375 human melanoma cells was analyzed by Cell Counting Kit‑8 (CCK) and 5‑ethynyl‑2′‑deoxyuridine (EdU) incorporation assays. GSEA was performed to predict the mechanism by which UTRN regulated melanoma growth. Then WB analysis was used to assess the protein expression levels of pathway signaling in overexpression (EXP) melanoma cells. Epac activator 8-pCPT-2′-O-Me-cAMP was then used to evaluate the proliferation ability by activation of p38 and JNK/c-Jun signaling pathways. Results Data from GEO and UCSC Xena project indicated that UTRN expression was decreased in melanoma. Experiment on clinical samples further confirmed our finding. TCGA results showed that a reduced expression of UTRN in 447 melanoma samples was associated with advanced clinical characteristics (T stage, Clark level, ulceration), shorter survival time and poorer prognosis. In addition, up-regulated UTRN expression inhibited melanoma cell proliferation when compared to control group. MAPK signaling pathway was presented in both KEGG and BioCarta databases by using GSEA tool. WB results confirmed the down-regulated expression of p38, JNK1 and c-Jun in EXP group when compared to control group. Epac activator 8-pCPT-2′-O-Me-cAMP treatment could partially rescue proliferation of tumor cells. Conclusion We have demonstrated that reduced UTRN predicted poorer prognosis and UTRN inhibited melanoma growth via p38 and JNK1/c-Jun pathways. Therefore, UTRN could serve as a tumor suppressor and novel prognostic biomarker for melanoma patients.

Tumor necrosis factor (TNF) and its receptors are widely expressed in non-small cell lung cancer (NSCLC). TNF has an established role in inflammation and also plays a key role in inflammation-induced cancer. TNF can induce cell death in cancer cells and has been used as a treatment in certain types of cancer. However, TNF is likely to play an oncogenic role in multiple types of cancer, including NSCLC. TNF is a key activator of the transcription factor NF-κB. NF-κB, in turn, is a key effector of TNF in inflammation-induced cancer. Data from The Cancer Genome Atlas database suggest that TNF could be a biomarker in NSCLC and indicate a complex role for TNF and its receptors in NSCLC. Recent studies have reported that TNF is rapidly upregulated in NSCLC in response to targeted treatment with epidermal growth factor receptor (EGFR) inhibition, and this upregulation leads to NF-κB activation. The TNF upregulation and consequent NF-κB activation play a key role in mediating both primary and secondary resistance to EGFR inhibition in NSCLC, and a combined inhibition of EGFR and TNF can overcome therapeutic resistance in experimental models. TNF may mediate the toxic side effects of immunotherapy and may also modulate resistance to immune checkpoint inhibitors. Drugs inhibiting TNF are widely used for the treatment of various inflammatory and rheumatologic diseases and could be quite useful in combination with targeted therapy of NSCLC and other cancers.

Bladder cancer is one of the most common malignant tumors worldwide. Accordingly, its incidence and mortality are high. One of the characteristics of cancer is genomic instability. New studies suggest that long non-coding RNAs (lncRNAs) play an important role in maintaining genomic instability. This study aimed to identify a genomic instability-associated lncRNA signature to predict the outcome of patients with bladder cancer. We downloaded data for bladder cancer patients from The Cancer Genome Atlas database to obtain lncRNA expression profiles as well as somatic mutation profiles. Using the lncRNA computational framework, a genomic instability-related lncRNA signature (GIlncSig) was established and the prognostic value of this signature was assessed and validated. A five-lncRNA signature based on genomic instability (CFAP58-DT, MIR100HG, LINC02446, AC078880.3, and LINC01833) was obtained from 58 differentially expressed lncRNAs. Patients were divided into high-risk and low-risk groups, with the high-risk group having a substantially worse prognosis than the low-risk group. Univariate and multivariate Cox analyses indicated that GIlncSig may be an independent prognostic factor; this finding was subsequently validated. In addition, enrichment analysis indicated that GIlncSig is associated with genomic instability in bladder cancer. GIlncSig has a predictive value for the prognosis of bladder cancer patients and provides guidance for the clinical treatment of these patients.

Background: ADPRH is a modulator of CD8+ T cell functions, and dysregulation of ADPRH has been identified to involve in carcinogenesis of cancers. However, the association of ADPRH with low grade glioma (LGG) remains unclear. Methods: The expression of ADPRH in LGG was first analyzed in GLIOVIS and GEPIA databases and then validated by real-time PCR (rt-PCR), immunochemistry and human protein atlas (HPA). Univariate and multivariate Cox analysis and Kaplan-Meier plots were designed to assess the prognostic value of ADPRH in LGG. The correlation of ADPRH and immune infiltration was evaluated by data in TIMER and ESTIMATE databases. Gene set enrichment analysis was conducted to detect biological processes associated with ADPRH. Results: ADPRH was significantly upregulated in LGG in comparison to non-tumor brain samples in transcriptomic and proteomic levels. The high ADPRH expression indicated unfavorable overall survival (OS) and progression-free survival (PFS) in patients with LGG using Kaplan-Meier plots. And multivariate Cox analysis demonstrated the expression level of ADPRH was an independent prognosis-predicting index for OS and PFS of LGG patients in all cohorts separately. Gene Set Enrichment Analysis (GSEA) indicated that high expression of ADPRH was involved in the upregulation of P53 signaling pathway, KRAS signaling pathway, IL6/JAK-STAT3 signaling and TNF-beta signaling pathways. By TIMER and ESTIMATE databases, we identified ADPRH expression had strong correlation with tumor immune infiltrating cells (TIICs). Conclusions: In summary, our findings demonstrated that ADPRH might be a potential prognostic biomarker and correlated with TIICs in LGG.

Purpose Somatic alteration of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) is a crucial therapeutic target in breast cancer (BC) and PI3Kα-specific inhibitor Alpelisib has been used in clinics. This study investigates the PIK3CA alterations in Chinese and Caucasians BC patients for the purpose of selecting anti-PI3K therapy. Methods The molecular profile of the PIK3CA gene was analyzed in 412 Chinese patients with untreated invasive BC using a 540 gene next-generation sequencing panel. The results were compared with data of the Caucasian BC patients in The Cancer Genome Atlas (TCGA-white). Results PIK3CA alterations were frequently found in BC of estrogen receptor (ER) positive (49.3%, p = 0.024), low ki67 proliferation index (58.3%, p = 0.007) and low pathological grade (grade I/II/III 80%, 53.4%, 35.9%, p p p = 0.01). Across four molecular subtypes, ER + /human epidermal growth factor receptor 2 positive (HER2 +) tumors harbored the most PIK3CA alterations (51.6%), while ER-/HER2- harbored the least alteration (30.0%) but the most copy number amplification (19.05%). Conclusion PIK3CA alterations prevail in Chinese BC patients and have different molecular features compared to that of Caucasians. The results provide precise annotations of PIK3CA genomic alterations of Chinese in the context of application of PIK3CA inhibitor.

Rad51, a DNA-repair-related gene, has been reported to be involved in multiple cancers. However, its link with immune infiltration in liver cancer still unknown. Therefore, more research into the roles and activities of Rad51 in hepatocellular carcinoma (HCC) is required. The International Cancer Genome Consortium (ICGC) was used to identify the DNA repair gene Rad51, and has been proved to be overexpressed in HCC patients. We plotted the Kapan-Meier curve, demonstrating that patients with high expression of Rad51 have a poor prognosis. By analyzing the patient data, we discovered that high expression of Rad51 in HCC is linked to clinical stage, pathological T stage, grade, and age. Rad51 was found to be an independent prognostic factor for HCC patients using the multivariate cox model. Moreover, Rad51 expression was found to be associated with the infiltration of immune cells (B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells) and was intimately linked to the expression of immune cell markers in HCC. Through the analysis of differentially coexpressed genes (DCGs) of Rad51, GO and KEGG enrichment analyses suggested that the expression level of Rad51 might be relevant to neuroactive ligand-receptor interactions, the cell cycle, DNA replication, homologous recombination, oocyte meiosis, and the Fanconi anemia pathway. These findings indicated that Rad51 is a valuable biomarker for the prognosis of patients with liver cancer and that its expression has a significant correlation with immune infiltrations. Abbreviations: HCC: hepatocellular carcinoma; ICGC: International Cancer Genome Consortium TCGA: The Cancer Genome Atlas; TIMER: Tumor Immune Estimation Resource; CAF: Cancer-associated fibroblast; GEPIA: Gene Expression Profiling Interactive Analysis; GSEA: Gene set enrichment analysis; OS: overall survival; PFS: progression-free survival; RFS: relapse-free survival; DSS: disease-specific survival. Partial cor: partial correlation coefficient; HPA: Human Protein Atlas; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; CAF: Cancer-associated fibroblast; DCGs: differentially co-expressed genes, Graphical Abstract

Background: The expression patterns and prognostic significance of the Rho family GTPases in acute myeloid leukemia have not been systematically studied yet. Methods: In our study, we analyzed the expression patterns of 21 Rho family GTPases gene members in AML patients based on GEPIA database. 10 gene members with significant differential expression in AML tissue and healthy tissue were selected for subsequent research. Survival curve analysis in TCGA and GEO dataset preliminary showed that RhoBTB3 is related with the prognosis of non-M3 AML patients. The differential expression of RhoBTB3 on AML bone marrow and normal bone marrow was verified by RT-qPCR. We performed Kaplan-Meier survival analysis and Multivariate Cox analysis to assess the prognostic value of RhoBTB3 in non-M3 AML patients with different treatment regimens. Gene functional enrichment analysis of RhoBTB3 was performed using GO, KEGG and PPI network. Results: The AML patients from TCGA database were partitioned into 2 groups based on different treatment regimens: chemotherapy group and allo-HSCT group. In chemotherapy group, patients with higher expression level of RhoBTB3 showed relatively longer OS and EFS, multivariate Cox analysis revealed high RhoBTB3 mRNA expression as an independent favorable prognostic factor. However, in allo-HSCT group, no significant difference of OS and EFS were found between RhoBTB3 high and low subgroups. Meanwhile, allo-HSCT could circumvent the unfavorable prognosis that was associated with downregulation of RhoBTB3. Functional enrichment analysis showed the association of RhoBTB3 expression with several fundamental physiological components and pathways, including extracellular matrix components, extracellular structure organization, and cytokine-cytokine receptor interaction. Conclusions: Our study identified RhoBTB3 as a prognostic marker and may aid in the selection of the appropriate treatment options between chemotherapy and allo-HCST in non-M3 AML patients. Further researches are necessary to clarify the involvement of RhoBTB3 in the pathogenesis of AML.

Rationale: Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play crucial roles in cancer progression; however, only few have been characterized in detail. The current study aimed to identify a novel cancer driver lncRNA in glioblastoma and colon adenocarcinoma. Methods: We performed whole transcriptome analysis of TCGA pan-cancer datasets to compare the lncRNA expression profiles of tumor and paired normal tissues. In situ hybridization of tissue sections was performed to validate the expression data and determine the localization of lncRNAs that may be linked to glioblastoma and colon adenocarcinoma. Chromatin isolation by RNA purification (ChIRP), chromatin immunoprecipitation (ChIP), and Co-immunoprecipitation (Co-IP) assays were performed to assess the interaction between lncRNA, proteins, and chromatin. The functional significance of the identified lncRNAs was verified in vitro and in vivo by knockdown or exogenous expression experiments. Results: We found a lncRNA ENST00000449248.1 termed PRC2 and DDX5 associated lncRNA (PRADX) that is highly expressed in glioblastoma and colon adenocarcinoma cells and tissues. PRADX, mainly located in the nucleus of tumor cells, could bind to EZH2 protein via the 5' terminal sequence. Moreover, PRADX increased the trimethylation of H3K27 in the UBXN1 gene promoter via PRC2/DDX5 complex recruitment and promoted NF-κB activity through UBXN1 suppression. Knockdown of PRADX significantly inhibited tumor cell viability and clonogenic growth in vitro. In xenograft models, PRADX knockdown suppressed tumor growth and tumorigenesis and prolonged the survival of tumor-bearing mice. Conclusions: PRADX acts as a cancer driver and may serve as a potential therapeutic target for glioblastoma and colon adenocarcinoma.

Cyclin-dependent kinase inhibitor 2A (CDKN2A) gene methylation has been paramount in the development of malignant masses. The purpose of the conducted research was to evaluate the mechanism and involvement of methylation in regards to the CDKN2A gene and the specific locus region in gastric cancer (GC) with comprehensive statistical analysis utilizing statistics acquired from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) revealed that the level of CDKN2A gene methylation and its locus in GC tissues was increased compared to para-cancerous tissues. In multivariate analysis, low methylation of CDKN2A gene, cg03079681, cg04026675, cg07562918, and cg13601799 locus were independently linked to better OS. In addition, the methylation of CDKN2A gene, cg00718440, cg03079681, cg04026675, cg07562918, cg10848754, cg14069088 and cg14430974 locus were negative correlated with CDKN2A gene expression. Meanwhile, the methylation of cg12840719 locus was positively correlated with CDKN2A gene expression. GSEA showed that hallmark_kras_signaling_dn, hallmark_myogenesis, and hallmark_epithelial_mesenchymal_transition pathways were enriched in the CDKN2A gene hypermethylation phenotype. Taken together, the low methylation of CDKN2A gene, cg03079681, cg04026675, cg07562918, and cg13601799 locus indicated a better prognosis in GC. The methylation levels of cg14069088 were most negatively correlated with CDKN2A gene expression. Hallmark_kras_signaling_dn, Hallmark_myogenesis, and hallmark_epithelial_mesenchymal_transition pathways might be important in the regulation of CDKN2A gene hypermethylation., Graphical abstract

N6-methyladenosine (m6A) RNA methylation regulators play a regulatory role in tumor pathogenesis and development. However, the role of m6A regulator genes in ovarian cancer (OC) has not been fully elucidated. This study aims to investigate the mRNA expressions, clinicopathological features, and prognostic values of m6A regulators in OC. Here, we demonstrate that the 17 m6A RNA methylation regulators are differentially expressed in ovarian cancer and normal tissues. By using consensus clustering, all ovarian cancer patients can be divided into two subgroups (cluster 1 and 2) based on the expression of 17 m6A RNA methylation regulators. Using Gene Set Enrichment Analysis, we identified that cluster 1 was most connected to oxidative phosphorylation pathways. Regression models identified that prognosis is associated with HNRNPA2B1, KIAA1429, and WTAP. qRT-PCR result show that the expression trends of HNRNPA2B1 and KIAA1429 are consistent with the predicted results. Multivariate Cox regression analysis results show that the risk score was an independent predictive factor in OV. The overall survival of high-risk patients was significantly shorter than that of low-risk patients. ROC curve analysis showed that the prognostic signature precisely predicted the 5-year survival of OV patients. A nomogram was developed to predict each patient’s survival probability and well calibrated and showed a satisfactory discrimination. Dendritic fraction, macrophage fraction, and neutrophil fraction showed higher fraction in high-risk patients. In conclusion, m6A RNA methylation regulators are vital participants in ovarian cancer pathology, and three-gene mRNA levels are valuable factors for prognosis predictions., Graphical Abstract

Bladder cancer (BLCA) is a common malignancy and has a poor prognosis. Single nucleotide polymorphisms (SNPs) in genes are closely associated with the tumorigenesis and tumor development and yet are not well illustrated in BLCA. In this study, we downloaded SNP‐related data and transcriptome profiling of BLCA from the Cancer Genome Atlas (TCGA) database and identified high frequency mutation genes using Maftools package and differentially expressed genes (DEGs) between BLCA and normal bladder tissues by the Limma package. Then, the overlapping genes between high frequency mutation genes and DEGs were obtained by the Venn diagram tool. These overlapping genes were analyzed by Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and drug–gene interaction analysis. As a result, 33 overlapping mutant genes were obtained, which were enriched in multiple KEGG pathways (e.g. cellular senescence) and GO items (e.g. muscle organ development, costamere and actin binding). A significant gene module was constructed by PPI network analysis and included 12 hub genes (SYNE1, DMD, ATM, EP300, ANK2, LAMA2, FAT1, SRRM2, MACF1, TSC1, VCAN and RXRA). Moreover, ATM, RXRA, TSC1, DMD, EP300, LAMA2 and VCAN were drug targets. These findings provide important bioinformatics and theoretical basis for understanding the pathogenesis of BLCA and exploring the detailed mechanisms of mutant genes in the disease.

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors. Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model. Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis. Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635. Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.

Background Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment. Material/methods 422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens. Results The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P P-value Conclusions Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Hepatocellular carcinoma (HCC) is a liver disease with a complex underlying mechanism, and patients with HCC have low survival rates. Iron metabolism plays a crucial role in the pathogenesis of HCC; however, the prognostic value of iron metabolism-related and methylated genes for HCC needs to be further explored. In the present study, we identified differentially expressed genes (DEGs) that play a role in iron metabolism and DNA methylation in HCC from The Cancer Genome Atlas. Four of these DEGs, whose expression levels are correlated with HCC prognosis, namely, RRM2, FTCD, CYP2C9, and ATP6V1C1, were further used to construct a prognostic model for HCC, wherein the risk score was calculated using the gene expression of the four DEGs. This could be used to predict the overall survival of HCC patients for 1, 3, and 5 years. Results of a multivariate Cox regression analysis further indicated that the risk score was an independent variable correlated with the prognosis of HCC patients. The identified gene signature was further validated using an independent cohort of HCC patients from the International Cancer Genome Consortium. Weighted gene co-expression network analysis and gene set enrichment analysis were performed to identify potential regulatory mechanisms of the gene signature in HCC. Taken together, we identified key prognostic factors of iron metabolism-related and methylated genes for HCC, providing a potential treatment strategy for HCC.

Lung cancer is the most common cancer worldwide, leading to high mortality each year. Metabolic pathways play a vital role in the initiation and progression of lung cancer. We aimed to establish a prognostic prediction model for lung adenocarcinoma (LUAD) patients based on a metabolism-associated gene (MTG) signature. Differentially expressed (DE)-MTGs were screened from The Cancer Genome Atlas (TCGA) LUAD cohorts. Univariate Cox regression analysis was performed on these DE-MTGs to identify genes significantly correlated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression was performed on the resulting genes to establish an optimal risk model. Survival analysis was used to assess the prognostic ability of the model. The prognostic value of the gene signature was further validated in independent Gene Expression Omnibus (GEO) datasets. A gene signature with 13 metabolic genes was identified as an independent prognostic factor. Kaplan-Meier survival analysis demonstrated the good performance of the risk model in both TCGA training and GEO validation cohorts. Finally, a nomogram incorporating clinical parameters and the metabolic gene signature was constructed to help individualize outcome predictions. The calibration curves showed excellent agreement between the actual and predicted survival., Graphical Abstract, A reliable prognostic metabolism-associated gene signature based on TCGA database was identified. The gene signature indicates a dysregulated metabolic microenvironment and might suggest therapeutic targets for LUAD patients. The nomogram based on the metabolism-associated gene signature and clinical parameters might favor personalized therapeutic strategies.

Background In terms of biological behavior, gene regulation, or signaling pathways, there is a certain similarity between tumorigenesis and embryonic development of humans. Three germ layer structure exhibits the distinct ability to form specific tissues and organs. Methods The present study set out to investigate the genetic mutation characteristics of germ layer differentiation-related genes using the tumor cases of the cancer genome atlas (TCGA) database. Results These tumor samples were divided into three groups, including the ectoderm, mesoderm, and endoderm. Children cases less than 9 years old accounted for a larger proportion for the cases in the ectoderm and mesoderm groups; whereas the middle-aged and elderly individuals (from 50 to 89 years old) were more susceptible to tumors of endoderm. There was a better prognosis for the cases of mesoderm, especially the male with the race of White, compared with the other groups. A missense mutation was frequently detected for the cases of ectoderm and endoderm, while deletion mutation was common for that of mesoderm. We could not identify the ectoderm, mesoderm, or endoderm-specific mutated genes or variants with high mutation frequency. However, there was a relatively higher mutation incidence of endoderm markers (GATA6, FOXA2, GATA4, AFP) in the endoderm group, compared with the groups of ectoderm and mesoderm. Additionally, four members (SMO, GLI1, GLI2, GLI3) within the Hedgehog signaling pathway genes showed a relatively higher mutation rate in the endoderm group than the other two groups. Conclusions TCGA tumors of ectoderm, mesoderm, and endoderm groups exhibit the distinct subject distribution, survival status, and genomic alteration characteristics. The synergistic mutation effect of specific genes closely related to embryonic development may contribute to the tumorigenesis of tissues or organs derived from the specific germ layers. This study provides a novel reference for exploring the functional connection between embryogenesis and tumorigenesis.

Yuanyong Wang,1,* Naixin Liang,2,* Zhiqiang Xue,1 Xinying Xue3,4 1Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, People’s Republic of China; 2Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Department of Respiratory Disease, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Department of Respiratory Disease, School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xinying XueDepartment of Respiratory Disease, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of ChinaEmail xinyingxue2010@163.comZhiqiang XueDepartment of Thoracic Surgery, Chinese PLA General Hospital, Beijing, People’s Republic of ChinaEmail xuezhiqiang301@126.comBackground and Aims: Esophageal adenocarcinoma (EAC) patients usually have a poor prognosis without early diagnosis. In this study, we aimed to identify a novel signature to improve the prediction of overall survival (OS) in EAC.Methods: Eighty-one and 68 samples from The Cancer Genome Atlas (TCGA) and GSE19417 dataset were included for discovery and survival validation, respectively. In the TCGA cohort, a total of 1,811 DEmRNAs, 1,096 DElncRNAs, and 31 DEmiRNAs were identified between EAC and normal esophagus tissues. A mRNA–miRNA–lncRNA ceRNA network of EAC was established, which consisted of 94 DEmRNAs, 13 DEmiRNAs, and 46 DElncRNAs.Results: In this study, we identified eight genes (UBE2B, LAMP2, B3GNT2, TAF9B, EFNA1, PHF8, PIGA, and NEURL1) which were related to survival in EAC. The independent external microarray data from the Gene Expression Omnibus (GEO) was used to validate these candidate genes. The prognostic ability of the signature was also validated in EAC patients in our hospital. Patients assigned to the high-risk group had a poor overall survival rate compared with the low-risk.Conclusion: The current study provides novel insights into the mRNA-related ceRNA network in EAC and the eight mRNA biomarkers may be independent prognostic signatures in predicting the survival of EAC patients.Keywords: esophageal adenocarcinoma, signature, competing endogenous RNA, overall survival, TCGA

High grade serous ovarian cancer is characterized by relatively few mutations occurring at low frequency, except in TP53. However other genetic aberrations such as copy number variation alter numerous oncogenes and tumor suppressor genes. Oncogenes are positive regulators of tumorigenesis and play a critical role in cancer cell growth, proliferation, and survival. Accumulating evidence suggests that they are crucial for the development and the progression of high grade serous ovarian carcinoma (HGSOC). Though many oncogenes have been identified, no successful inhibitors targeting these molecules and their associated pathways are available. This review discusses oncogenes that have been identified recently in HGSOC using different screening strategies. All the genes discussed in this review have been functionally characterized both in vitro and in vivo and some of them are able to transform immortalized ovarian surface epithelial and fallopian tube cells upon overexpression. However, it is necessary to delineate the molecular pathways affected by these oncogenes for the development of therapeutic strategies.

Prostate cancer (PCa) is a leading adult malignant tumor. Recent research has shown that speckle-type BTB/POZ protein (SPOP) mutant is the top frequently mutated gene in PCa, which makes it an important biomarker. In this paper, we aimed at identifying critical genes and pathways related to SPOP mutation in PCa. Recent The Cancer Genome Atlas data showed that 12% of patients with PCa were SPOP mutant. There were 1,570 differentially expressed genes, and online enrichment analysis showed that these genes were mainly enriched in metabolism, pathways in cancer and reactive oxygen species. INS, GNG13, IL6, HTR5A, SAA1, PPY, CXCR5, CXCL13, CD19 and CCL20 were identified as hub genes. The lower SPOP expression level was associated with poor prognosis. In all, our findings showed that various pathways and genes could play critical roles in SPOP mutation in PCa, providing potential options for individualized treatment.

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant–drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant–drug interactions and pharmacogenetic cancer variants.

Background Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients’ prognosis is in urgent need. Methods In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed. Result In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system. Conclusion Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.

Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

Lung cancer is the leading cause of cancer‐related death worldwide. Previous studies revealed that endoplasmic reticulum oxidoreductase 1 alpha (ERO1L) played critical roles in the malignant behaviors of several cancer types, but its role in non‐small cell lung cancer (NSCLC) remained unclear. In this study, we identified 26 upregulated and 102 downregulated genes in NSCLC using bioinformatics analyses, and these genes were enriched in the biological processes of the cell cycle. ERO1L was remarkably upregulated in NSCLC and overexpression of ERO1L was associated with poor prognosis of NSCLC. ERO1L deficiency markedly suppressed NSCLC cell proliferation, colony formation, migration, and invasion. ERO1L depletion caused a dramatically decreased expression of cell cycle‐related factors in NSCLC cells. Collectively, our data validated that ERO1L could function as a tumor promoter in NSCLC, indicating the potential of targeting ERO1L for the treatment of NSCLC.

Long noncoding RNAs (lncRNAs) dysregulation leads to malignant progression of lung cancer. Our study profiled differentially expressed lncRNA and mRNA in tumor and normal tissues of lung adenocarcinoma (LUAD). Further, analysis of the gene expression profiles of LUAD tissues (n = 533) and normal tissues (n = 59) from The Cancer Genome Atlas (TCGA). A total of 138 lncRNAs were differentially expressed between LUAD tissues and normal tissues (false discovery rate [FDR] q, We performed a study in 8 lung adenocarcinomas patients and validated our observations using The Cancer Genome Atlas (TCGA) dataset. We found that 138 lncRNAs were upregulated or down regulated similarly to those in our microarray study. The lncRNA–mRNA co‐expression network was constructed based on the correlation analysis. Our results revealed that the lncRNA A2M‐AS1 exhibited the highest correlation with the co‐expressed mRNAs, indicating that it might play a key role in regulating differential gene expression in lung adenocarcinomas patients.

Because of the complexity of cancer‐immune system interactions, combinations of biomarkers will be required for predicting individual patient responses to treatment and for monitoring combination strategies to overcome treatment resistance. To this end, the “immunogram” has been proposed as a comprehensive framework to capture all relevant immunological variables. Here, we developed a method to convert transcriptomic data into immunogram scores (IGS). This immunogram includes 10 molecular profiles, consisting of innate immunity, priming and activation, T cell response, interferon γ (IFNG) response, inhibitory molecules, regulatory T cells, myeloid‐derived suppressor cells (MDSCs), recognition of tumor cells, proliferation, and glycolysis. Using genes related to these 10 parameters, we applied single‐sample gene set enrichment analysis (ssGSEA) to 9417 bulk RNA‐Seq data from 9362 cancer patients with 29 different solid cancers in The Cancer Genome Atlas (TCGA). Enrichment scores were z‐score normalized (Z) for each cancer type or the entire TCGA cohort. The IGS was defined by the formula IGS = 3 + 1.5 × Z so that patients would be well distributed over a range of scores from 1 to 5. The immunograms constructed in this way for all individual patients in the entire TCGA cohort can be accessed at “The RNA‐Seq based Cancer Immunogram Web” (https://yamashige33.shinyapps.io/immunogram/)., We propose a novel scoring and visualization method for “immunogram” to assess the cancer immunity status of individual patients using the large TCGA dataset and RNA‐Seq of each patient. Immunograms for each individual will be a useful tool for precision immuno‐oncology, although their application needs to be validated in clinical trials.

Uterine corpus endometrial carcinoma (UCEC) is the most common type of gynecologic malignancy worldwide. Despite advances in the treatments of UCEC, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer pathogenesis and progression. Thus, we aimed to identify the landscape of m6A regulators and build a prognostic gene signature in UCEC. In this study, we first analyzed copy number variations (CNVs), single nucleotide variations (SNVs) and gene expression profiles as well as matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Next, we determined that CNVs in m6A regulatory genes had a significant negative impact on patient survival. The mRNA expression levels of a total of 16 m6A regulators were significantly correlated with different CNV patterns. Using univariate Cox regression analysis, IGF2BP1, KIAA1429, IGF2BP3, YTHDF3, and IGF2BP2 were found to be closely associated with UCEC patient survival outcomes. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models, we built a 3-gene (IGF2BP3, KIAA1429 and IGF2BP1) signature of m6A regulators with prognostic value in UCEC that could effectively predict patient prognosis (log-rank test p-value < 0.0001). In addition, risk scores were significantly different between patients stratified by tumor stage, SNV, and CNV. Multivariate Cox regression analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value < 0.05). Gene enrichment analysis indicated that high IGF2BP1 gene expression is associated with cytoplasmic stress granules. KIAA1429 gene expression is associated with cellular nucleic acid metabolism. The expression of the IGF2BP3 gene is associated with RNA binding processes. In conclusion, we determined that genetic alterations in m6A regulatory genes could be effective and reliable biomarkers for UCEC prognosis prediction.

The purpose of this study was to investigate the genetic variation, gene expression differences, and clinical significance of SUMOylation regulators in pan-cancers. Based on previous studies, we gained a better understanding of the biological process of SUMOylation and the status of current research. In the present study, we employed a wide range of bioinformatics methods. We used genetic variation and mRNA expression data in the Cancer Genome Atlas (TCGA) to construct a panoramic view of the single nucleotide variants, copy number variants, and gene expression changes in SUMOylation regulators in various tumors. Subsequently, we used the String website and the Cytoscape tool to construct the PPI network between these regulators. We used the GSCALite website to determine the relationship between these regulators and cancer pathways and drug sensitivity. We constructed images of co-expression between these regulators using the R programming language. Using clinical data from TCGA, we performed hazard ratio analysis for these regulators in pan-cancer. Most importantly, we used these regulators to successfully establish risk signatures related to patient prognosis in multiple tumors. Finally, in KIRC, we conducted gene-set enrichment analysis (GSEA) of the five molecules in its risk signatures. We found that these five molecules are involved in multiple cancer pathways. In short, we have comprehensively interpreted the detailed biological process of SUMOylation at the genetic level for the first time, successfully constructed multiple risk signatures, and conducted GSEA in KIRC. We believe that these findings provide credible and valuable information that is relevant for future clinical diagnoses and scientific research.

Background Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis. Methods In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan–Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out. Results It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC = 0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes. Conclusion These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.