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Abstract Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P

Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P < 0.05). The effectiveness of this model was validated using the receiver operating characteristic (ROC) curve analysis, and this model proved superior in prognostic accuracy compared with other clinical features. Furthermore, the tumor immune dysfunction and exclusion (TIDE) score in the high-risk group was significantly higher than that in the low-risk group, suggesting that the high-risk group had a less favorable response to immunotherapy. Simultaneously, patients in the low-risk group exhibited significantly higher sensitivity to CTLA-4 monoclonal antibody therapy compared to those in the high-risk group, suggesting potential benefits of immunotherapy for patients in the low-risk group. The combination of high risk and low tumor mutational burden (TMB) could further shortened the OS of BLCA patients. Lastly, the drug sensitivity analysis revealed that the BLCA cells in the high-risk group showed an increased sensitivity to cisplatin, sunitinib, cetuximab, axitinib, docetaxel, saracatinib, vinblastine and pazopanib compared with those in the low-risk group. According to the Quantitative real time PCR results, we found that five lncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line. The disulfidptosis-related lncRNA risk model has a valuable effect in assessing the prognosis of BLCA patients. [ABSTRACT FROM AUTHOR]

To investigate whether PSGL-1 is correlated with the prognosis of cutaneous malignant melanoma. Genetic data of PSGL-1 and cutaneous malignant melanoma were extracted from genomewide association studies (GWAS) and analyzed using inverse variance weighting, MR-Egger regression, weighted median, weighted mode, and simple mode methods, and sensitivity analyses were performed on the data. Survival information of cutaneous malignant melanoma was extracted from the TCGA database, and grouped according to high and low PSGL-1 expression, survival curves were plotted by the Kaplan-Meier method, and the Log-rank test was used to compare the difference between the two survival periods. Exploring the effects of PSGL-1 on the biological functions of A375 cells through in vitro experiments. The inverse variance weighted method analysis of PSGL-1 on cutaneous malignant melanoma resulted in OR=0.666, 95% CI (0.494-0.899), P=0.008. The median survival was 44.70 months in the PSGL-1 high expression group and 29.13 months in the PSGL-1 low expression group, P=0.000 2. PSGL-1 inhibits the migration of A375 cells in vitro. Increased PSGL-1 expression is a protective factor against cutaneous malignant melanoma and is a potential prognostic marker for cutaneous melanoma. [ABSTRACT FROM AUTHOR]

Background: The overall survival (OS) rate of adult patients suffering from acute myeloid leukaemia (AML) remains unsatisfactory at less than 40%. Current risk stratification systems fail to provide accurate guidelines for precise treatment. Novel biomarkers for predicting prognosis are urgently needed. Plexin B2 (PLXNB2), a functional receptor of angiogenin (ANG), has been found to be aberrantly expressed in multitudinous tumours. We detected overexpression of PLXNB2 mRNA in AML via transcriptome microarray analysis. This study aims to explore the potential role of PLXNB2 as a biomarker of prognosis and a prospective target of AML. Methods: qRT‒PCR was conducted to verify the expression of PLXNB2 mRNA in bone marrow mononuclear cells from AML patients. Immunohistochemical and immunofluorescence staining were performed and confirmed increased expression of PLXNB2 protein in AML bone marrow tissues. Data on PLXNB2 expression, prognosis and clinical features were accessed from multiple bioinformatic databases, including The Cancer Genome Atlas (TCGA). Genes coexpressed and correlated with PLXNB2 were identified and analysed in the TCGA AML cohort. Metascape was applied for functional and pathway enrichment analysis of genes related to PLXNB2. Small molecular agents and traditional Chinese medicines potentially targeting genes related to PLXNB2 were screened via the Connectivity Map, TCMSP and HIT databases. Results: PLXNB2 mRNA and protein levels are higher in AML samples than in normal controls. Overexpression of PLXNB2 is associated with worse OS in AML. Patients with high PLXNB2 expression might benefit more from haematopoietic stem cell transplantation (HSCT) (indicated by prolonged OS) than those with only chemotherapy treatment. Differentially expressed genes between the high and low PLXNB2 expression groups were overlapped with PLXNB2-coexpressed genes, and genes that overlapped were enriched in immune functions, endothelial cell regulation and cell interaction gene sets, indicating the potential function of PLXNB2 in AML. A total of 36 hub genes were identified from the differentially expressed genes, and MRC1, IL10, CD163 and CCL22 had significant prognostic value for AML. Analysis of the connectivity map and traditional agents revealed that honokiol, morphines, triptolide and paeoniflorin could be potential treatment regimens. Conclusions: The overexpression of PLXNB2 is an adverse prognostic factor in adult AML patients and could be used as a potential biomarker. PLXNB2 might exert an oncogenic role by modulating immune functions, endothelial cell functions and cell interactions. AML patients with high PLXNB2 expression could benefit more from HSCT. Highlights: PLXNB2 expression could be a potential biomarker of poor prognosis in AML. HSCT could improve the prognosis of patients with high PLXNB2 expression. PLXNB2 might regulate immunity, endothelial cells and cell interactions. [ABSTRACT FROM AUTHOR]

Objective To explore the relationship between differentially expressed genes in tumor microenvironment and prognosis of gastric cancer. Methods Based on the gastric cancer gene data in the TCGA database, the differentially expressed genes related to stromal cells and immune cells were screened. The differentially expressed genes were analyzed by Gene Ontology enrichment analysis and protein-protein interaction network analysis, and the relationship between differentially expressed genes and the prognosis of gastric cancer was analyzed. Results There were statistically significant differences in the stromal score, immune score, and comprehensive score among gastric cancer patients with different pathological grades (P＜0.05). G3 grade patients had the highest immune score, G1 grade patients had a slightly higher score, and G2 grade patients had the lowest score. There was no statistically significant difference in the immune scores between G1 grade patients and G2 grade patients (P＞0.05). The stromal score increased with the increase of tumor grade. G1 grade patients with good differentiation had the lowest comprehensive score, followed by G2 grade patients with lower differentiation, and G3 grade patients with the highest degree of malignancy had the highest comprehensive score. There was no statistically significant difference in the comprehensive score between G1 grade patients and G2 grade patients (P＞0.05). The upregulated expression of genes was significantly correlated with the stromal score and immune score, respectively (P＜0.05). The expression level of CXCR4 (P=0.002), NPY1R (P=0.002), BPI (P=0.019), MCEMP1 (P=0.009), OLR1 (P=0.041), RETN (P=0.045) was the higher, the survival time of gastric cancer patients was the shorter. Conclusion Differentially expressed genes play an indispensable and important role in the immune response of gastric cancer tumors, with high expression of BPI, CXCR4, MCEMP1, NPY1R, OLR1, and RETN genes being adverse factors for the prognosis of gastric cancer. [ABSTRACT FROM AUTHOR]

Background: Cutaneous melanoma (CM) is an aggressive type of skin cancer. Even after standard treatment, the recurrence and malignant progression of CM were almost inevitable. The overall survival (OS) of patients with CM varied widely, making it critical for prognostic prediction. Based on the correlation between CCR6 and melanoma incidence, we aimed to investigate the prognostic role of CCR6 and its relationship with immune infiltration in CM. Methods: We obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze the CM expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram model had been developed. Kaplan-Meier survival analysis and log-rank test were used to estimate the relationship between OS and CCR6 expression. Results: CCR6 was significantly upregulated in CM. Functional enrichment analyses revealed that CCR6 was correlated with immune response. Most immune cells and immune checkpoints were positively correlated with CCR6 expression. Kaplan-Meier analyses showed that high CCR6 expression was associated with a good outcome in CM and its subtypes. Cox regression showed that CCR6 was an independent prognostic factor in patients with CM (HR = 0.550, 95% CI = 0.332-0.912, p<0.05). Conclusions: CCR6 is considered to be a new prognostic biomarker for patients with CM, and our study provides a potential therapeutic target for CM treatment. [ABSTRACT FROM AUTHOR]

Objective To establish a lncRNA prognostic risk model for gastrointestinal tumors based on the TCGA database and evaluate the prognosis of patients. Methods We collected the data of patients with esophageal cancer, gastric cancer, colon cancer and rectal cancer in the TCGA database. Univariate Cox analysis, Lasso and multivariate Cox analysis were performed to construct the prognostic risk scoring model. The model was validated and tested for independence. Time-dependent ROC curve analysis was performed to evaluate the clinical application value of the model. Results We established a prognostic risk model based on 13 lncRNAs. The three-year AUC of the training set and the validation set were 0.746 and 0.704, respectively. The pan-cancer data set was divided into high- and low-risk groups for survival analysis. The 5-year survival rate of the low-risk group was significantly higher than that of the high-risk group; among all cancer types, the five-year survival rates of the low-risk group were higher than those of the high-risk group. Multivariate Cox analysis showed that the risk score could be an independent indicator of prognosis. Conclusion The 13-gene prognostic risk score model is constructed successfully. The risk score obtained by this model can be used as an independent prognostic predictor of the patients with gastrointestinal cancer.

BackgroundCutaneous melanoma (CM) is an aggressive type of skin cancer. Even after standard treatment, the recurrence and malignant progression of CM were almost inevitable. The overall survival (OS) of patients with CM varied widely, making it critical for prognostic prediction. Based on the correlation between CCR6 and melanoma incidence, we aimed to investigate the prognostic role of CCR6 and its relationship with immune infiltration in CM.MethodsWe obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze the CM expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram model had been developed. Kaplan–Meier survival analysis and log-rank test were used to estimate the relationship between OS and CCR6 expression.ResultsCCR6 was significantly upregulated in CM. Functional enrichment analyses revealed that CCR6 was correlated with immune response. Most immune cells and immune checkpoints were positively correlated with CCR6 expression. Kaplan–Meier analyses showed that high CCR6 expression was associated with a good outcome in CM and its subtypes. Cox regression showed that CCR6 was an independent prognostic factor in patients with CM (HR = 0.550, 95% CI = 0.332–0.912, p

Tumor cells have significant heterogeneity in metabolism and are closely related to prognosis, gene mutation, and subtype. However, this association has not been demonstrated in reports of gastrointestinal tumors. In this study, we constructed four metabolic subtypes and identified four gene signatures using the expression data and clinical information of 252 metabolism-related genes from TCGA and NCBI databases for gastric adenocarcinoma (STAD) and colorectal cancer (COAD and READ). MC1 had the worst prognosis compared to other classifications. GSig1 was mainly related to drug metabolism and was the highest in MC1 with the worst prognosis, while the other subtypes were mainly related to glucose metabolism pathways. This difference also existed in other different malignant tumors. In addition, metabolic typing was associated with chemotherapeutic drug response and tumor heterogeneity, which indicated that monitoring metabolic typing could contribute to drug efficacy and gene-targeted therapy. In conclusion, we identified differences among subtypes in clinical characteristics such as prognosis and revealed the potential function of metabolic subtype in response to chemotherapeutic agents and oncogene mutations. This work highlighted the potential clinical meaning of metabolic subtype and characteristics in drug therapy and prognosis assessment of malignant tumors. [ABSTRACT FROM AUTHOR]

Objective To analyze the ferroptosis-related genes in colon cancer and construct a molecular prognostic model based on the expression of ferroptosis-related genes.Methods The transcriptome sequencing data of colon cancer and the clinical information of patients were downloaded from the Cancer Genome Atlas (TCGA ) database.The R 4.1.0 software was used to obtain the expression data of the ferroptosis-related genes and the differential genes were analyzed.The functional enrichment was used to analyze the biological effects and functions of differential genes.Combined with the survival data of patients, the COX regression analysis was used to screen out the genes related to the patient′s prognosis, and then the risk score equation was constructed.The patients with colon cancer were divided into high-risk group and low-risk group according to risk scores.The sensitivity and specificity of the prognostic model were assessed by receiver operating characteristic curve.Results Compared with normal tissues, there were 72 dysregulated ferroptosis-related genes in colon cancer tissues.The differential genes were mainly involved in ferroptosis and tumor-related signaling pathways.Four ferroptosis-related genes (DRD4,TFAP2 C,ENPP2,CAV1 ) were significantly related to the patient′s prognosis (P<0.05 ),which could be used for the construction of a risk prognosis model.The overall survival rate of patients in the high-risk group was poorer, and the difference was statistically significant (P<0.05 ).The areas under the curve of the molecular prognostic model based on ferroptosis-related genes for predicting the survival rates of patients at 1-year, 3-year and 5-year were 0.714,0.750 and 0.759,respectively, which had good sensitivity and specificity.Conclusion A molecular prognostic model based on ferroptosis-related genes has been successfully constructed, which can better predict the prognosis of colon cancer patients. [ABSTRACT FROM AUTHOR]

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. 18 of 54 DEFRGs were correlated with overall survival (OS) (P<0.05). Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we selected 10 DEFRGs that were associated with OS to build a prognostic signature. Data from AML patients from the International Cancer Genome Consortium (ICGC) cohort as well as the First Affiliated Hospital of Wenzhou Medical University (FAHWMU) cohort were used for validation. Notably, the prognostic survival analyses of this signature passed with a significant margin, and the riskscore was identified as an independent prognostic marker using Cox regression analyses. Then we used a machine learning method (SHAP) to judge the importance of each feature in this 10- gene signature. Riskscore was shown to have the highest correlation with this 10-gene signature compared with each gene in this signature. Further studies showed that AML was significantly associated with immune cell infiltration. In addition, drug-sensitive analysis showed that 8 drugs may be beneficial for treatment of AML. Finally, the expressions of 10 genes in this signature were verified by real-time quantitative polymerase chain reaction. In conclusion, our study establishes a novel 10-gene prognostic risk signature based on ferroptosis-related genes for AML patients and FRGs may be novel therapeutic targets for AML. [ABSTRACT FROM AUTHOR]

• PIK3CA mutation correlates with effector memory and naïve CD4 T cell infiltration in ESCC. • PIK3CA mutation is linked to increased tumor mutation burden in ESCC. • ESCC cells with PIK3CA mutations show reduced sensitivity to p38 MAPK and JNK inhibitors. • Glycosaminoglycan degradation is involved in PIK3CA mutations in ESCC. Recent studies have increasingly focused on PIK3CA mutations in esophageal squamous cell carcinoma (ESCC); however, the clinicopathological significance of these mutations within the tumor microenvironment remains underexplored. This study aimed to evaluate and compare the clinicopathological significance of mutated PIK3CA in ESCC using in silico analyses of the ESCC dataset from the TCGA database. We assessed prognosis, differential expression, correlation with immune cell infiltration and immune checkpoint expression, heterogeneity, and drug sensitivity in comparison with wild-type PIK3CA. Our findings revealed that PIK3CA mutation is associated with increased tumor mutation burden and significantly correlated with the infiltration of CD4 naive and effector memory CD4 T cells. Additionally, ESCC cells harboring PIK3CA mutations exhibited reduced sensitivity to p38/JNK MAPK inhibitors compared to those with wild-type PIK3CA. Collectively, our in silico analysis suggests that mutational PIK3CA plays a role in resistance to p38 and JNK MAPK inhibitors in ESCC. [ABSTRACT FROM AUTHOR]

Objective To establish a prognostic model of fatty acid metabolism related genes for predicting the prognosis of renal clear cell carcinoma. Methods The differentially expressed fatty acid metabolism-related genes in renal clear cell carcinoma samples and normal samples in TCGA database were screened by R language software. The Cox proportional hazard regression model was used to select and establish a multigene prognostic model and the prognostic score was calculated. Patients were divided into high-risk group and low-risk group according to the median prognostic score. Kaplan-Meier survival curve was used to analyze the difference in two groups. The clinical pathological factors and prognostic score factors were included in the Cox regression model to analyze the factors affecting the survival of patients with renal clear cell carcinoma. ROC receiver operating curve analysis was used to evaluate the accuracy of the prognostic prediction model. The prognostic model of fatty acid metabolism-related genes and their correlation with clinical factors were analyzed. GSEA enrichment analysis analyzed the differences of gene sets in risk groups. Results A total of 4 differential genes（CPT1B, HADH, CYP4A11, and ACADSB） were selected to establish a prognostic model for genes related to fatty acid metabolism in renal cell carcinoma. The prognostic risk score （RS） formula is as follows: RS=0.490×CPT1B-0.428×HADH-0.11 × CYP4A11-0.372 × ACADSB. Kaplan-Meier survival analysis confirmed that the overall survival rate of patients with low-risk prognostic score was significantly higher in patients with overall renal clear cell carcinoma, and the difference was statistically significant （P<0.001）. Cox regression analysis showed that the prognostic model of genes related to age and fatty acid metabolism is an independent influencing factor for the prognosis of patients with renal clear cell carcinoma （P<0.01）.The 5 years’ AUC of the renal clear cell carcinoma ROC curve of the renal cancer fatty acid metabolism related gene model was 0.802.GSEA analysis showed that the difference of 81gene sets in the low-risk group was statistically significant （P<0.05）.Conclusion The prognostic model of renal cancer fatty acid metabolism-related genes can be used to predict the prognosis of patients with renal clear cell carcinoma, which is conducive to further guide clinical treatment. [ABSTRACT FROM AUTHOR]

Background: As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD). Methods: First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan-Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation. Results: LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into highand low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan-Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells. Conclusions: We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens. [ABSTRACT FROM AUTHOR]

Background: The role of cancer stem cells in esophageal squamous cell carcinoma (ESCC) remains unclear. Methods: The mRNA stemness index (mRNAsi) of 179 ESCC patients (GSE53625) was calculated using a machine learning algorithm based on their mRNA expression. Stemness-related genes were identified by weighted correlation network analysis (WGCNA) and LASSO regression, whose associations with mutation status, immune cell infiltrations, and potential compounds were also analyzed. The role of these genes in proliferation and their expressions was assessed in ESCC cell lines and 112 samples from our center. Results: The ESCC samples had significantly higher mRNAsi than the normal tissues. Patients with high mRNAsi exhibited higher worse OS. Seven stemness-related genes were identified by WGCNA and LASSO regression, based on which a risk-predicted score model was constructed. Among them, CST1, CILP, PITX2, F2RL2, and RIOX1 were favorable for OS, which were adverse for DPP4 and ZFHX4 in the GSE53625 dataset. However, RIOX1 was unfavorable for OS in patients from our center. In vitro assays showed that CST1, CILP, PITX2, F2RL2, and RIOX1 were pro-proliferated, which were opposite for DDP4 and ZFHX4. In addition, SMARCA4, NOTCH3, DNAH5, and KALRN were more mutated in the low-score group. The low-score group had significantly more memory B cells, monocytes, activated NK cells, and Tregs and less macrophages M2, resting mast cells, and resting dendritic cells. Conclusions: Seven stemness-related genes are significantly related to the prognosis, gene mutations, and immune cell infiltration of ESCC. Some potential anticancer compounds may be favorable for OS. [ABSTRACT FROM AUTHOR]

To explore the expression and prognostic value of ferroptosis-related genes in patients with kidney renal clear cell carcinoma (KIRC), relevant sequencing data of KIRC were downloaded from the TCGA database and intersected with retrieved ferroptosis-related genes for difference analysis. Then, univariate and multivariate Cox regression analyses were used for screening the genes with prognostic value. The risk scoring model was constructed to predict the survival of patients, and the model was verified. The high and low risk groups were enriched with GO and KEGG pathways to explore the possible reasons for the risk differences. The ssGSEA analysis was used to assess the level of immune infiltration between the high and low risk groups. In the tumor tissues and normal tissues of KIRC patients, a total of 21 differential ferroptosis-related genes were obtained. Through univariate Cox regression analysis, 28 genes related to the prognosis of KIRC were obtained. Then Lasso regression and multivariate Cox regression analysis were performed, and results showed that ten genes were included in the model. The calculation formula was: risk score 二(0.024 5) X ALOX5 expression value + (0.126 0) X CBS expression value + (0.199 5) X CD44 expression value+(0.218 3) XCHAC1 expression value+(-0.295 9) XHMGCR expression value+(0.036 7) X MT1G expression value+(0.061 4)xSLC7A11 expression value+(-0.080 7)xFDFT1 expression value+(0.160 3) XPEBP1 expression value+(-0.220 5) XGOT1 express value. The survival status diagram showed that the high risk group had more deaths than the low risk group. The ROC curve showed that the risk scoring model had certain predictive ability. K-M survival analysis showed that the overall survival rate of the high risk group was lower than that of the low risk group (P = 5.73 X 10 13). The GO and KEGG enrichment analyses showed that there were significant differences in the immune status and IL-17 signaling pathway between the high and low risk groups. Further ssGSEA enrichment showed that there were significant differences in the scores of most immune cells between the high and low risk groups. The prognostic risk scoring model based on ferroptosis-related genes can be used to predict the prognosis of KIRC, and designing targets for ferroptosis-related genes may be a new option for the treatment of KIRC. [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Journal of Cancer Biotherapy is the property of Editorial Office of Chinese Journal of Cancer Biotherapy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment. [ABSTRACT FROM AUTHOR]

The objective was to construct a prognostic risk model of stomach adenocarcinoma (STAD) based on The Cancer Proteome Atlas (TCPA) to search for prognostic biomarkers. Protein data and clinical data on STAD were downloaded from the TCGA database, and differential expressions of proteins between carcinoma and para-carcinoma tissues were screened using the R package. The STAD data were randomly divided into a training set and a testing set in a 1:1 ratio. Subsequently, a linear prognostic risk model of proteins was constructed using Cox regression analysis based on training set data. Based on the scores of the prognostic model, sampled patients were categorized into two groups: a high-risk group and a low-risk group. Using the testing set and the full sample, ROC curves and K-M survival analysis were conducted to measure the predictive power of the prognostic model. The target genes of proteins in the prognostic model were predicted and their biological functions were analyzed. A total of 34 differentially expressed proteins were screened (19 up-regulated, 15 down-regulated). Based on 176 cases in the training set, a prognostic model consisting of three proteins (COLLAGEN VI, CD20, TIGAR) was constructed, with moderate prediction accuracy (AUC=0.719). As shown by the Kaplan-Meier and survival status charts, the overall survival rate of the low-risk group was better than that of the high-risk group. Moreover, a total of 48 target proteins were identified to have predictive power, and the level of proteins in hsa05200 (Pathways in cancer) was the highest. According to the results of the Univariate and multivariate COX analysis, tri-protein was identified as an independent prognostic factor. Therefore, the tri-protein prognostic risk model can be used to predict the likelihood of STAD and guide clinical treatment.

Recent studies reveal that tumor microenvironment contributes to breast cancer (BRCA) development, progression, and therapeutic response. However, the contribution of the tumor microenvironment-related genes in routine diagnostic testing or therapeutic decision making for BRCA remains elusive. Immune/stromal/ESTIMATE scores calculated by the ESTIMATE algorithm quantify immune and stromal components in a tumor, and thus can reflect tumor microenvironment. To investigate the association of the tumor microenvironment-related genes with invasive BRCA prognosis, here we analyzed the immune/stromal/ESTIMATE scores in combination with The Cancer Genome Atlas (TCGA) database in invasive BRCA. We found that immune/stromal/ESTIMATE scores were significantly correlated with the invasive BRCA clinicopathological factors. Based on the immune/stromal/ESTIMATE scores, we extracted a series of differential expression genes (DEGs) related to the tumor microenvironment. Survival analysis was further performed to identify a list of high-frequency DEGs (HF-DEGs), which exhibited prognostic value in invasive BRCA. Importantly, consistent with the results of bioinformatics analysis, immunohistochemistry results showed that high SASH3 expression was associated with a good prognosis in invasive BRCA patients. Our findings suggest that the tumor microenvironment-related HF-DEGs identified in this study have prognostic values and may serve as potential biomarkers and therapeutic targets for invasive BRCA.

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

Gastric cancer (GC) is a common malignant tumor of the digestive system. Recent studies revealed that high gamma-glutamyl-transferase 5 (GGT5) expression was associated with a poor prognosis of gastric cancer patients. In the present study, we aimed to confirm the expression and prognostic value of GGT5 and its correlation with immune cell infiltration in gastric cancer. First, we compared the differential expression of GGT5 between gastric cancer tissues and normal gastric mucosa in the cancer genome atlas (TCGA) and GEO NCBI databases using the most widely available data. Then, the Kaplan-Meier method, Cox regression, and univariate logistic regression were applied to explore the relationships between GGT5 and clinical characteristics. We also investigated the correlation of GGT5 with immune cell infiltration, immune-related genes, and immune checkpoint genes. Finally, we estimated enrichment of gene ontologies categories and relevant signaling pathways using GO annotations, KEGG, and GSEA pathway data. The results showed that GGT5 was upregulated in gastric cancer tissues compared to normal tissues. High GGT5 expression was significantly associated with T stage, histological type, and histologic grade (p < 0.05). Moreover, gastric cancer patients with high GGT5 expression showed worse 10-years overall survival (p = 0.008) and progression-free intervals (p = 0.006) than those with low GGT5 expression. Multivariate analysis suggested that high expression of GGT5 was an independent risk factor related to the worse overall survival of gastric cancer patients. A nomogram model for predicting the overall survival of GC was constructed and computationally validated. GGT5 expression was positively correlated with the infiltration of natural killer cells, macrophages, and dendritic cells but negatively correlated with Th17 infiltration. Additionally, we found that GGT5 was positively co-expressed with immune-related genes and immune checkpoint genes. Functional analysis revealed that differentially expressed genes relative to GGT5 were mainly involved in the biological processes of immune and inflammatory responses. In conclusion, GGT5 may serve as a promising prognostic biomarker and a potential immunological therapeutic target for GC, since it is associated with immune cell infiltration in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes , ARL9, CMYA5 , and STEAP3 , were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients. [ABSTRACT FROM AUTHOR]

Recent studies reveal that tumor microenvironment contributes to breast cancer (BRCA) development, progression, and therapeutic response. However, the contribution of the tumor microenvironment-related genes in routine diagnostic testing or therapeutic decision making for BRCA remains elusive. Immune/stromal/ESTIMATE scores calculated by the ESTIMATE algorithm quantify immune and stromal components in a tumor, and thus can reflect tumor microenvironment. To investigate the association of the tumor microenvironment-related genes with invasive BRCA prognosis, here we analyzed the immune/stromal/ESTIMATE scores in combination with The Cancer Genome Atlas (TCGA) database in invasive BRCA. We found that immune/stromal/ESTIMATE scores were significantly correlated with the invasive BRCA clinicopathological factors. Based on the immune/stromal/ESTIMATE scores, we extracted a series of differential expression genes (DEGs) related to the tumor microenvironment. Survival analysis was further performed to identify a list of high-frequency DEGs (HF-DEGs), which exhibited prognostic value in invasive BRCA. Importantly, consistent with the results of bioinformatics analysis, immunohistochemistry results showed that high SASH3 expression was associated with a good prognosis in invasive BRCA patients. Our findings suggest that the tumor microenvironment-related HF-DEGs identified in this study have prognostic values and may serve as potential biomarkers and therapeutic targets for invasive BRCA. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes (DEGs) in patients with and without MVI based on TCGA data, established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI.Methods: In this paper, gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator, MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally, we validated two genes, HOXD9 and HOXD10, using two sets of HCC tissue microarrays from 260 patients.Results: Twenty-three MVI-status-related key genes were identified. Based on the key genes, we built a classification model using random forest and time-dependent receiver operating characteristic (ROC), which reached 0.814. Then, we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously, using two sets of 260 patients' HCC tissue microarrays, we validated two key genes, HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI, and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10.Conclusion: We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI. [ABSTRACT FROM AUTHOR]

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Background: CD28 expression is correlated withm alignancy development in long-term survivors after liver transplantation. Immune cell activation is mediated by the interaction of CD28 with CD4 and CD8. Objectives: In this study, we attempted to investigate the expression level and prognostic value of CD28 in hepatocellular carcinoma (HCC). Methods: A total of 54HCC patients with complete clinical information were examined. The expression level of CD28 in HCC tissues was detected by immunohistochemistry. The correlations of CD28 expression with clinical characteristics, CD4+/CD8+ T-cells, and prognosis in HCC were analyzed. The expression profile of CD28 and survival time of HCC patients were retrieved from the TCGA database, followed by survival analysis. Results: The positive expression rate of CD28 in HCC tissues was 70.73%. The CD28 expression was significantly higher in the positive expression group (area: 659174.9 670060, IOD: 123348.3 106348.6) than in the negative expression group (area: 8405.7 9983.3, IOD: 1959.6 2117.7) (P < 0.01). The CD4+ and CD8+ cell counts were 526.13 258.17 cells/ L and 383.93 223.39 cells/ L, respectively. The expression level of CD28was significantly related to the degree of differentiation and the number of CD4+ and CD8+ T-cells (P < 0.05). The survival time of patients was longer in the positive CD28 expression group than in the negative expression group. Based on the CD28 expression profiles of 406 HCC patients retrieved from the TCGA database, patients with high CD28 expression showed a better prognosis than those with low expression (P < 0.05). Conclusions: CD28may play a vital role in the occurrence, development, and prognosis of HCC by interacting with CD4+ and CD8+ T-cells. Thus, CD28 could be suggested as the immune checkpoint target for HCC treatment. [ABSTRACT FROM AUTHOR]

Background: To classify triple-negative breast cancer (TNBC) immunotyping using the public database, analyze the differences between subtypes in terms of clinical characteristics and explore the role and clinical significance of immune subtypes in TNBC immunotherapy. Methods: We downloaded TNBC data from the cBioPortal and GEO databases. The immune genes were grouped to obtain immune gene modules and annotate their biological functions. Log-rank tests and Cox regression were used to evaluate the prognosis of immune subtypes (IS). Drug sensitivity analysis was also performed for the differences among immune subtypes in immunotherapy and chemotherapy. In addition, dimension reduction analysis based on graph learning was utilized to reveal the internal structure of the immune system and visualize the distribution of patients. Results: Significant differences in prognosis were observed between subtypes (IS1, IS2, and IS3), with the best in IS3 and the worst in IS1. The sensitivity of IS3 to immunotherapy and chemotherapy was better than the other two subtypes. In addition, Immune landscape analysis found the intra-class heterogeneity of immune subtypes and further classified IS3 subtypes (IS3A and IS3B). Immune-related genes were divided into seven functional modules (The turquoise module has the worst prognosis). Five hub genes (RASSF5, CD8A, ICOS, IRF8, and CD247) were screened out as the final characteristic genes related to poor prognosis by low expression. Conclusions: The immune subtypes of TNBC were significantly different in prognosis, gene mutation, immune infiltration, drug sensitivity, and heterogeneity. We validated the independent role of immune subtypes in tumor progression and immunotherapy for TNBC. This study provides a new perspective for personalized immunotherapy and the prognosis evaluation of TNBC patients in the future. [ABSTRACT FROM AUTHOR]

Gliomas are the most common malignant tumors of the central system. MAML2 is a co-activator of the NOTCH signaling pathway. This study aimed to verify the diagnostic and prognostic value of MAML2 gene expression and clinical parameters in low-grade glioma(LGG). First, the gene expression data and clinical data of patients were downloaded from TCGA LGG database, and statistical methods were used to verify the difference of MAML2 gene expression as well as the relationship between clinical parameters and the diagnosis and prognosis of LGGs. In the TCGA LGG cohort, it was found that the expression of MAML2 gene in LGG was significantly higher than that in the normal tissues (P<0.001), whose differential expression could be used as potential diagnostic marker for LGG. Meanwhile, the overall survival rate of LGG patients in the low expression group of MAML2 was lower than that in the high expression group (P= 0.005 2). In addition, univariate and multivariate analyses suggested that tumor grade, recurrence, and low expression of MAML2 were independent risk factors for LGGs. The results of this study suggest that MAML2 gene may be a potential molecular marker for the diagnosis and prediction of LGGs, but further experiments are needed. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. We constructed four SF-risk-models using 12 survival-related SFs. In Luminal-A, Luminal-B, Her-2, and Basal-Like BRCA, SF-risk-models for three genes (PAXBP1, NKAP, and NCBP2), four genes (RBM15B, PNN, ACIN1, and SRSF8), three genes (LSM3, SNRNP200, and SNU13), and three genes (SRPK3, PUF60, and PNN) were constructed. These models have a promising prognosis-predicting power. The co-expression and protein-protein interaction analysis suggest that the 12 SFs are highly functional-connected. Pathway analysis and gene set enrichment analysis suggests that the functional role of the selected 12 SFs is highly context-dependent among different BRCA subtypes. We further constructed four AS-risk-models with good prognosis predicting ability in four BRCA subtypes by integrating the four SF-risk-models and 21 survival-related AS-events. This study proposed that SFs and ASs were potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. We constructed four SF-risk-models using 12 survival-related SFs. In Luminal-A, Luminal-B, Her-2, and Basal-Like BRCA, SF-risk-models for three genes (PAXBP1 , NKAP , and NCBP2), four genes (RBM15B , PNN , ACIN1 , and SRSF8), three genes (LSM3 , SNRNP200 , and SNU13), and three genes (SRPK3 , PUF60 , and PNN) were constructed. These models have a promising prognosis-predicting power. The co-expression and protein-protein interaction analysis suggest that the 12 SFs are highly functional-connected. Pathway analysis and gene set enrichment analysis suggests that the functional role of the selected 12 SFs is highly context-dependent among different BRCA subtypes. We further constructed four AS-risk-models with good prognosis predicting ability in four BRCA subtypes by integrating the four SF-risk-models and 21 survival-related AS-events. This study proposed that SFs and ASs were potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA. [ABSTRACT FROM AUTHOR]

Introduction: Renal clear cell carcinoma (ccRCC) is a common tumor of the urinary system, most of which are primary malignant tumors with high metastatic rate and remaining incurable. Ferroptosis is a newly discovered form of iron-dependent programmed cell necrosis in recent years, which is inextricably linked to the occurrence and development of tumors progression. Due to the complexity of the interaction between genes in ccRCC, the research on the pathogenesis of ccRCC is still not remarkably accurate. Therefore, whether ferroptosis-related genes (FRGs) can play a role in predicting prognosis in ccRCC needs to be discussed. Methods: We entered the Cancer Genome Mapping Project (TCGA) database and downloaded the relevant genes and clinical research data of ccRCC patients. Lasso Cox regression was used to construct a multi-gene prognostic model in the TCGA cohort. R language software was used for drawing pictures related to our study. Results: Most of the genes involved in ferroptosis (86.2%) existing differences between the tumor and normal tissues in the TCGA public gene database. In terms of univariate Cox regression analysis, 20 differentially expressed genes (DEGs) were associated with prognosis and survival (P< 0.05). A prognostic model of 12 FRGs was constructed, and patients were segmented into two different groups depending on how risky they are. Considering overall survival, the high-risk group is dramatically lower than the low-risk group (P< 0.001). In multivariate Cox regression analysis, risk scores and stage turned out be an independent prognostic factor (P< 0.001). GO and KEGG analysis and ssGSEA analysis of DEGs revealed that these genes were related to immune-related pathways (P< 0.05). Conclusion: This study established and identified the changes in FRGs expression and prognostic factors of ccRCC, which can be helpful for prognosis evaluation and clinical treatment of this disease. [ABSTRACT FROM AUTHOR]

Objective:To investigate the effect of Helicobacter pylori (Hp) infection on gene expression of ataxia-telangiectasia mutant (ATM) in gastric cancer cells and its clinical significance. Methods: The gastric cancer related RNAseq data was obtained from the TCGA database to compare the differential expressions of ATM. The correlation between ATM expression and clinicopathological parameters as well as the prognostic value of ATM was analyzed. Kaplan-Meier method was used for survival analysis, LinkedOmics database was utilized to analyze ATM-related genes, and R language was used for GO and KEGG enrichment analysis. Twelve pairs of gastric cancer tissues and para-cancerous tissues resected in the Affiliated Hospital of Guizhou Medical University from March 2019 to December 2019 as well as gastric cancer cell lines (AGS and BGC823) were collected for this study. Bacterial Hp GZ7 at a MOI (multiplicity of infection) of 40:1 was used to infect the cells. The protein expression of ATM in gastric cancer tissues was detected using immunohistochemical staining, and the mRNA expression of ATM in gastric cancer tissues and cells was determined using qPCR. Results: TCGA data showed that the miRNA expression of ATM in gastric cancer tissues and Hp-infected gastric cancer tissues was significantly higher than that in para-cancerous tissues (all P<0.01). The miRNA expression of ATM in the gastric cancer tissues was positively correlated with pathological parameters such as T and AJCC staging (all P<0.05); High ATM expression was accompanied with significantly reduced survival rate (P<0.05). Experiment results showed that the protein expression of ATM in gastric cancer tissues was significantly higher than that in para-cancerous tissues (P<0.01), and the miRNA expression of ATM in Hp-infected gastric cancer cells was significantly higher than that in the uninfected gastric cancer cells (P<0.01). In gastric cancer, ATM gene was positively correlated with 12 461 genes such as NPAT (P<0.05), and negatively correlated with 7 764 genes such as MIF (P<0.05). The GO and KEGG enrichment analyses showed that ATM is enriched in the pathways such as DNA repair complexes, transcription disorders in cancer. Conclusion: ATM gene is highly expressed in GC tissues, and its high expression results in decreased survival rate of patients. ATM gene is associated with pathological parameters such as patient's T stage and AJCC stage, and the increase in ATM expression level caused by Hp infection may be one of the reasons for Hp caused gastric cancer. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma and has poor prognosis in the locally advanced stage. Ferroptosis, a relatively new type of cell death, has gained significant attention in recent years. This study aimed to explore the prognostic value of ferroptosis-related genes (FRGs) in ccRCC. In this study, 50 differentially expressed FRGs between ccRCC and adjacent normal kidney tissues were identified, 26 of them correlated with overall survival (OS) (P < 0.05). Eight optimal FRGs were selected by Lasso regression and multivariate Cox regression analysis, and used to construct a new prognostic risk signature to predict the prognosis of ccRCC patients. In addition, the signature passed the validation of prognostic survival analyses by a significant margin, and the risk score was identified as an independent prognostic marker via Cox regression analyses. Further studies indicated that the signature was significantly correlated with immune cell infiltration. Moreover, the levels of eight FRGs were examined in ccRCC. Collectively, the 8-FRG prognostic risk signature helps the clinicians predict the prognosis and OS of the patients, and standardize prognostic assessments. [ABSTRACT FROM AUTHOR]

The autophagy cell, which can inhibit the formation of tumor in the early stage and can promote the development of tumor in the late stage, plays an important role in the development of tumor. Therefore, it has potential significance to explore the influence of autophagy-related genes (AAGs) on the prognosis of hepatocellular carcinoma (HCC). The differentially expressed AAGs are selected from HCC gene expression profile data and clinical data downloaded from the TCGA database, and human autophagy database (HADB). The role of AAGs in HCC is elucidated by GO functional annotation and KEGG pathway enrichment analysis. Combining with clinical data, we selected age, gender, grade, stage, T state, M state, and N state as Cox model indexes to construct the multivariate Cox model and survival curve of Kaplan Meier (KM) was drawn to estimate patients' survival between high- and low-risk groups. Through an ROC curve drawn by univariate and multivariate Cox regression analysis, we found that seven genes with high expression levels, including HSP90AB1, SQSTM1, RHEB, HDAC1, ATIC, HSPB8, and BIRC5 were associated with poor prognosis of HCC patients. Then the ICGC database is used to verify the reliability and robustness of the model. Therefore, the prognosis model of HCC constructed by autophagy genes might effectively predict the overall survival rate and help to find the best personalized targeted therapy of patients with HCC, which can provide better prognosis for patients. [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSCC) is an immunosuppressive malignancy accompanied by noted alterations in various immune cells and cytokines. Recognition of the immune system’s role in contributing to cancer development is an important advancement in our original understanding of carcinoma. We obtained HNSCC gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. We assessed the relative proportion of 22 Infiltrating immune cell types in both HNSCC and adjacent non-cancer tissues using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, identifying the influence of the immune cells content in tumor staging and survival prediction. We further predicted the tumor purity, and the presence of infiltrating stromal/immune cells in HNSCC tissues using Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, identifying its potential correlation with patient survival. Stromal and immune score-associated differentially expressed genes (DEGs) were subsequently verified and their roles in immune response were displayed by functional enrichment analysis and protein-protein interaction (PPI) network. Our research demonstrated the underlying association between the immune microenvironment and HNSCC, and the results were intended to serve as valuable terms for HNSCC diagnosis, prognosis, and targeted immune therapy.

Head and neck squamous cell carcinoma (HNSCC) is an immunosuppressive malignancy accompanied by noted alterations in various immune cells and cytokines. Recognition of the immune system's role in contributing to cancer development is an important advancement in our original understanding of carcinoma. We obtained HNSCC gene expression and clinical data from The Cancer Genome Atlas (TCGA) database. We assessed the relative proportion of 22 Infiltrating immune cell types in both HNSCC and adjacent non-cancer tissues using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, identifying the influence of the immune cells content in tumor staging and survival prediction. We further predicted the tumor purity, and the presence of infiltrating stromal/immune cells in HNSCC tissues using Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, identifying its potential correlation with patient survival. Stromal and immune score-associated differentially expressed genes (DEGs) were subsequently verified and their roles in immune response were displayed by functional enrichment analysis and protein-protein interaction (PPI) network. Our research demonstrated the underlying association between the immune microenvironment and HNSCC, and the results were intended to serve as valuable terms for HNSCC diagnosis, prognosis, and targeted immune therapy. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. Methods: In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan–Meier plotter. Result: Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion: Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients. [ABSTRACT FROM AUTHOR]

Objectives: To explore the clinical features and prognosis of normal karyotype acute myeloid leukemia (NK-AML) pediatric patients with WT1 mutations. Methods: The clinical data and prognostic information of 220 NK-AML pediatric patients were selected from target-AML project of The Cancer Genome Atlas (TCGA) database. Survival analyses were performed for NK-AML pediatric patients with different combinations of mutations. Results: We found that 28(12.7%) NK-AML patients harbored WT1 mutations. The positive rate of FLT3-ITD in the WT1-mutated group was higher than that in the WT1 wild-type group (P = 0.002). In contrast, WT1 mutation and NPM1 mutation were mutually exclusive (P = 0.013). Furthermore, the WT1-mutated group suffered lower rates of complete remission (CR) (P < 0.001 and P < 0.001, respectively) but higher rates of minimal residual disease (MRD) (P = 0.003 and P = 0.021, respectively) after both one and two courses of induction chemotherapy. Patients with WT1 mutations had significantly worse overall survival (OS) and event-free survival (EFS) in both univariate (P < 0.001 and P = 0.007, respectively) and multivariate survival analyses (P < 0.001 and P < 0.001, respectively). The stratification analysis showed that for FLT3-ITD positive patients, WT1 mutations predicted shorter OS (P = 0.003) and EFS (P < 0.001). Conclusion: WT1 mutations conferred an independent poor prognosis for NK-AML pediatric patients. [ABSTRACT FROM AUTHOR]

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Objective To screen and validate the key genes related to the prognosis of lung adenocarcinoma, and to explore its regulatory pathway. Methods Transcriptome data of lung adenocarcinoma were downloaded from GEO and TCGA database to screen out the common differentially expressed genes. Lasso was introduced into the Cox model for further screening of key genes associated with prognosis. We calculated the risk scores of 500 patients with lung adenocarcinoma obtained from TCGA database, and then the patients were divided into the high-risk group and the low-risk group; with the median risk score as the critical value, we compared the 5-year survival rates of the two groups. GEPIA and HPA databases were used to analyze the expression of key prognostic genes and their proteins in cancer tissues. Kaplan-Plotter database was used to analyze the relationship between key prognostic genes and the prognosis of patients with lung adenocarcinoma. TIMER databases were utilized to analyze the correlation between the abundance of 6 immune infiltrating cells and the expression of key prognostic genes in lung adenocarcinoma. Gene Set variation Analysis (GSV A) method was used to pre 如t the regulatory pathways of key prognostic genes in lung adenocarcinoma. Results Totally, 166 common differentially expressed genes were identified from TCGA and GEO databases. Regression analysis screened out DCN, RRAS, ECT2 and PCP4 as key genes related to the prognosis of lung adenocarcinoma. The 5-year survival rates of the high-risk and low risk groups were 29. 3% and 48. 4%, respectively (both P < 0. 01). Compared with the normal tissues, the expression levels of DCN, RRAS mRNA and protein in the lung adenocarcinoma tissues were down-regulated, while the expression levels of PCP4 and ECT2 mRN A and protein were up-regulated in the lung adenocarcinoma tissue (all P < 0. 05). The 5-year survi val rate of patients with high RRAS, PCP4 and ECT2 expression was significantly lower than that of patients with low express ion, and the 5-year survival rate of patients with high DCN expression was significantly higher than that of patients with low expression (all P < 0. 01). GSV A results revealed that DCN, RRAS, ECT2 and PCP4 might affect the prognosis of lung adenocarcinoma by regulating cell cycle, DNA damage repair and other pathways. Conclusions DCN, RRAS, ECT2 and PCP4 are key genes related to prognosis in patients with lung adenocarcinoma. The high expression of RRAS, PCP4 and ECT2 and the low expression of DCN in the cancer tissues all indicate poor prognosis, and the mechanism may be related to activation of immune cells, regulation of cell cycle, DNA damage repair and other pathways. [ABSTRACT FROM AUTHOR]

Background: To analyze the differences in gene expression levels of chemokine CXCL-12 and its receptor CXCR4 in gastric cancer and the relationship between their correlations with the clinical prognosis of gastric cancer. Methods: The information on gastric cancer in the TCGA (The Cancer Genome Atlas) database was downloaded from the Broad GDAC FIREHOSE, including CXCL-12 and CXCR4 gene expression data of 415 gastric cancer tissues and 35 normal gastric tissues; clinical information of 392 gastric cancer cases. All patients were divided into either a correlated (significantly higher or lower correlation between CXCL12 and CXCR4 expression) or uncorrelated groups. Wilcoxon rank sum test was used to analyze the differential gene expressions of CXCL-12 and CXCR4 between gastric cancer tissues and normal gastric tissues. Furthermore, one-way analysis of variance and Kaplan-Meier survival analysis were used to analyze the differential gene expressions of CXCL-12 and CXCR4 and the prognosis in patients with different stages of gastric cancer. Gastric cancer patients were divided into two groups according to whether CXCL-12 and CXCR4 gene expressions were correlated or not. Kaplan-Meier survival analysis was used to analyze the three-year survival of the two groups. Results: There were differences between CXCL-12 and CXCR4 expression in 415 gastric cancer tissues and 35 normal gastric tissues. No statistically significant difference between CXCL-12 and CXCR4 was detected in different stages of gastric cancer. There were differences of the five-year survival in different stages of gastric cancer. Further analysis showed that the three-year survival in the correlated group was superior compared to the uncorrelated one. Conclusions: The gene expression of CXCL-12 and CXCR4 was significantly different between gastric cancer tissues and normal gastric tissues. Moreover, the correlation between CXCL-12 and CXCR4 gene expression may be used as a predictor of clinical prognosis in patients with gastric cancer. [ABSTRACT FROM AUTHOR]

Alternative splicing (AS) is an important mechanism that is responsible for the production of protein diversity. An increasing body of evidence has suggested that out-of-control AS is closely related to the genesis and development of cancer. Systematic analysis of genome-wide AS in head and neck squamous cell carcinoma (HNSCC) has not yet been carried out, and consideration of this topic remains at the preliminary stage and requires further investigation. In this study, systemic bioinformatic analysis was carried out on the genome-wide AS events of 555 clinical HNSCC samples from the TCGA database. Firstly, we statistically analyzed the distributions of seven AS event types in HNSCC samples. Then, through univariate survival analysis, we observed the relationship between AS and the prognosis of the disease and found that 437 intersections of AS events were significantly related to overall survival. Among them, 335 cross-genes showed a high degree of consistency in the genes associated with overall survival and recurrence. The overall survival was significantly related to AS events. Besides, the frequency of overall survival-related ES events was evidently reduced, while the AP and the AT events were increased. In addition, AT events accounted for the largest proportion. Further, multiple regression model analysis proved that AS could become a new classification method for HNSCC, and KEGG enrichment analysis proved that most genes and proteins interacting with AS events had different biological functions and were associated with a variety of diseases. Finally, through the selection of characteristic HNSCC genes and the construction of a prognostic model, seven cross-genes related to survival and recurrence were screened out, and these characteristic genes were verified by multivariate survival model analysis so as to classify the prognosis at different splicing times and gene expression levels. These results have laid a solid foundation for our further research and play a decisive role in showing the correlation of AS with the prognosis of HNSCC.

Background Aurora kinase B (AURKB) is an important carcinogenic factor in various tumors, while its role in clear cell renal cell carcinoma (ccRCC) still remains unclear. This study aimed to investigate its prognostic value and mechanism of action in ccRCC. Methods Gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. R software was utilized to analyze the expression and prognostic role of AURKB in ccRCC. Gene set enrichment analysis (GSEA) was used to analyze AURKB related signaling pathways in ccRCC. Results AURKB was expressed at higher levels in ccRCC tissues than normal kidney tissues. Increased AURKB expression in ccRCC correlated with high histological grade, pathological stage, T stage, N stage and distant metastasis (M stage). Kaplan-Meier survival analysis suggested that high AURKB expression patients had a worse prognosis than patients with low AURKB expression levels. Multivariate Cox analysis showed that AURKB expression is a prognostic factor of ccRCC. GSEA indicated that genes involved in autoimmune thyroid disease, intestinal immune network for IgA production, antigen processing and presentation, cytokine-cytokine receptor interaction, asthma, etc., were differentially enriched in the AURKB high expression phenotype. Conclusions AURKB is a promising biomarker for predicting prognosis of ccRCC patients and a potential therapeutic target. In addition, AURKB might regulate progression of ccRCC through modulating intestinal immune network for IgA production and cytokine-cytokine receptor interaction, etc. signaling pathways. However, more research is necessary to validate the findings.

Alternative splicing (AS) is an important mechanism that is responsible for the production of protein diversity. An increasing body of evidence has suggested that out-of-control AS is closely related to the genesis and development of cancer. Systematic analysis of genome-wide AS in head and neck squamous cell carcinoma (HNSCC) has not yet been carried out, and consideration of this topic remains at the preliminary stage and requires further investigation. In this study, systemic bioinformatic analysis was carried out on the genome-wide AS events of 555 clinical HNSCC samples from the TCGA database. Firstly, we statistically analyzed the distributions of seven AS event types in HNSCC samples. Then, through univariate survival analysis, we observed the relationship between AS and the prognosis of the disease and found that 437 intersections of AS events were significantly related to overall survival. Among them, 335 cross-genes showed a high degree of consistency in the genes associated with overall survival and recurrence. The overall survival was significantly related to AS events. Besides, the frequency of overall survival-related ES events was evidently reduced, while the AP and the AT events were increased. In addition, AT events accounted for the largest proportion. Further, multiple regression model analysis proved that AS could become a new classification method for HNSCC, and KEGG enrichment analysis proved that most genes and proteins interacting with AS events had different biological functions and were associated with a variety of diseases. Finally, through the selection of characteristic HNSCC genes and the construction of a prognostic model, seven cross-genes related to survival and recurrence were screened out, and these characteristic genes were verified by multivariate survival model analysis so as to classify the prognosis at different splicing times and gene expression levels. These results have laid a solid foundation for our further research and play a decisive role in showing the correlation of AS with the prognosis of HNSCC. [ABSTRACT FROM AUTHOR]