Comprehensive bioinformatics analysis of CYB561 expression in breast cancer: Link between prognosis and immune infiltration

Background: Cytochrome b561 (CYB561) plays a critical role in neuroendocrine function, cardiovascular regulation, and tumor growth; however, the prognostic value of CYB561 in patients with breast cancer and the relationship between CYB561 expression and immune infiltration in breast cancer remain unclear. Methods: The mRNA expression and clinical data of patients with breast cancer were obtained from The Cancer Genome Atlas database. Functional enrichment analysis was used to explore underlying biological functions associated with CYB561. The methylation status of CYB561 was analyzed using the MethSurv database. The enrichment score of immune cell infiltration for CYB561 in breast cancer was calculated using single-sample gene set enrichment analysis. The prognostic value of CYB561 was evaluated using Kaplan-Meier method and Cox regression analysis. Based on the results of the multivariate Cox analysis, a nomogram was constructed to predict the effect of CYB561 expression on overall survival (OS). Results: The results showed that CYB561 was highly expressed in breast cancer tissues. Hypomethylation of CYB561 is associated with an unfavorable prognosis. In multivariate Cox regression analysis, CYB561 was an independent prognostic factor for OS. Functional enrichment analysis indicated that estrogen signaling pathway, inflammatory response, KRAS signaling pathway, epithelial-mesenchymal transition, leukocyte migration, and regulation of lymphocyte activation were strongly enriched in the low CYB561 expression group. Additionally, CYB561 expression was negatively correlated with immune infiltration of B cells, plasmacytoid dendritic cells, dendritic cells, and neutrophils. Conclusion: CYB561 may serve as a potential biomarker for breast cancer diagnosis and prognosis.     

Prognostic tumor microenvironment gene and the relationship with immune infiltration characteristics in metastatic breast cancer

The aim of this study was to reveal genes associated with breast cancer metastasis, to investigate their intrinsic relationship with immune cell infiltration in the tumor microenvironment, and to screen for prognostic biomarkers. Gene expression data of breast cancer patients and their metastases were downloaded from the GEO, TCGA database. R language package was used to screen for differentially expressed genes, enrichment analysis of genes, PPI network construction, and also to elucidate key genes for diagnostic and prognostic survival. Spearman’s r correlation was used to analyze the correlation between key genes and infiltrating immune cells. We screened 25 hub genes, FN1, CLEC5A, ATP8B4, TLR7, LY86, PTGER3 and other genes were differentially expressed in cancer and paraneoplastic tissues. However, patients with higher expression of CD1C, IL-18 breast cancer had a better prognosis in the 10 years survival period, while patients with high expression of FN1, EIF4EBP1 tumors had a worse prognosis. In addition, TP53 and HIF1 genes are closely related to the signaling pathway of breast cancer metastasis. In this study, gene expression of ATP8B4 and CD1C were correlated with cancer tissue infiltration of CD8⁺ T lymphocytes, while GSE43816, GSE62327 and TCGA databases showed that CD8⁺ T lymphocytes were closely associated with breast cancer progression. Functional enrichment analysis of genes based on expression differences yielded key genes of prognostic value in the breast cancer microenvironment.     

WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common immunogenic malignant tumor. Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC, the 5-year survival rate of patients is still very low. The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies. We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes. Weighted gene co-expression network analysis (WGCNA), univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and construct multi-gene signature-based risk model and nomogram for survival prediction. Support vector machine (SVM) based Cibersort’s deconvolution algorithm and gene set enrichment analysis (GSEA) is used to evaluate the changes in tumor immune microenvironment and pathway differences. The study found the favorable prognostic performance of miR-93-3p and identified 389 prognostic-related target genes. The risk model based on a novel 5-gene signature (cct5, cdk4, cenpa, dtnbp1 and flvcr1) was developed and has prominent prognostic significance in the training cohort (P < 0.0001) and validation cohort (P = 0.0016). The nomogram constructed by combining the gene signature and the AJCC stage further improves the survival prediction ability of the gene signature. The infiltration level of multiple immune cells (especially T cells, B cells and macrophages) were positively correlated with the expression of prognostic signature. In addition, we found that gene markers of T cells and B cells is monitored and regulated by prognostic signature. Meanwhile, several GSEA pathways related to the immune system are enriched in the high-risk group. In general, we integrated the WGCNA, LASSO COX and SVM algorithms to develop and verify 5-gene signatures and nomograms related to immune infiltration to improve the survival prediction of patients.     

Prognostic model for prostate cancer based on glycolysis-related genes and non-negative matrix factorization analysis

Background: Establishing an appropriate prognostic model for PCa is essential for its effective treatment. Glycolysis is a vital energy-harvesting mechanism for tumors. Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential. Methods: First, gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB). Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained, which were used in non-negative matrix factorization (NMF) to identify clusters. The correlation between clusters and clinical features was discussed, and the differentially expressed genes (DEGs) between the two clusters were investigated. Based on the DEGs, we investigated the biological differences between clusters, including immune cell infiltration, mutation, tumor immune dysfunction and exclusion, immune function, and checkpoint genes. To establish the prognostic model, the genes were filtered based on univariable Cox regression, LASSO, and multivariable Cox regression. Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model. A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting. Result: The genes obtained from MSigDB were enriched in glycolysis functions. Two clusters were identified by NMF analysis based on 272 glycolysis-related genes, and a prognostic model based on DEGs between the two clusters was finally established. The prognostic model consisted of LAMPS, SPRN, ATOH1, TANC1, ETV1, TDRD1, KLK14, MESP2, POSTN, CRIP2, NAT1, AKR7A3, PODXL, CARTPT, and PCDHGB2. All sample, training, and test cohorts from The Cancer Genome Atlas (TCGA) and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups. The area under the ROC curve showed great performance of this prognostic model. Conclusion: A prognostic model based on glycolysis-related genes was established, with great performance and potential significance to the clinical application.     

Profiles of immune status and related pathways in sepsis: evidence based on GEO and bioinformatics

Sepsis, characterized as life-threatening sequential organ failure, is caused by a dysregulated host immune response to a pathogen. Conventional practice for sepsis is to control the inflammation source and administer highgrade antibiotics. However, the mortality rate of sepsis varies from 25–30% and can reach 50% if a septic shock occurs. In our current study, we used bioinformatics technology to detect immune status profiles in sepsis at the genomic level. We downloaded and analyzed gene expression profiles of GSE28750 from the Gene Expression Omnibus (GEO) database to determine differential gene expression and immune status between sepsis and normal samples. Next, we used the CIBERSORT method to quantify the proportions of immune cells in the sepsis samples. Then we explored the differentially expressed genes (DEGs) related to sepsis. Furthermore, gene ontology (GO) function and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to present potential signaling pathways in sepsis. We found that in the sepsis samples, the CD8⁺ T cell fraction was consistently lower, based on the CIBERSORT method, whereas the neutrophil fraction was significantly higher in the sepsis samples. The GO function and KEGG pathway enrichment analysis identified 1573 DEGs that were significantly associated with neutrophil activation, neutrophil degranulation, neutrophil activation involved in the immune response, neutrophil-mediated immunity, and T cell activation in the biological processes group. In our study, we provided a first glance of associations between immune status and sepsis. Furthermore, our data regarding the reciprocal interaction between immune cells (neutrophils and CD8⁺ T cells) could improve our understanding of immune status profiles in sepsis. However, additional investigations should be performed to verify their clinical value.     

A novel prognostic gene signature,nomogram and immune landscape based on tanshinone IIA drug targets for hepatocellular carcinoma: Comprehensive bioinformatics analysis and <i>in vitro</i> experiments

Background: Tanshinone IIA, one of the main ingredients of Danshen, is used to treat hepatocellular carcinoma (HCC). However, potential targets of the molecule in the therapy of HCC are unknown. Methods: In this study, we collected the tanshinone IIA targets from public databases for investigation. We screened differentially expressed genes (DEGs) across HCC and normal tissues using mRNA expression profiles from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression models were used to identify and construct the prognostic gene signature. Results: Finally, we discovered common genes across tanshinone IIA targets and HCC DEGs. We reported Fatty acid binding protein-6 (FABP6), Polo-like Kinase 1 (PLK1), deoxythymidylate kinase (DTYMK), Uridine Cytidine Kinase 2 (UCK2), Enhancer of Zeste Homolog 2 (EZH2), and Cytochrome P450 2C9 (CYP2C9) as components of a gene signature. The six-gene signature’s prognostic ability was evaluated using the Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC), multivariate Cox regression analysis, and the nomogram. The mRNA level and protein expression of UCK2 were experimentally validated after treatment with different concentrations of tanshinone IIA in HEPG2 cells. CIBERSORTx, TIMER2.0, and GEPIA2 tools were employed to explore the relationship between the prognostic signature and immune cell infiltration. Conclusion: We established a six-gene signature as a reliable model with significant therapeutic possibility for prognosis and overall survival estimation in HCC patients, which might also benefit medical decision-making for appropriate treatment.     

Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer

Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance. And we developed a nomogram by combing the risk score with multiple clinical characteristics. CIBERSORT and TIMER algorithms confirmed that there are significant differences in tumor-infiltrating immune cells in different risk groups. In addition, gene set enrichment analysis shows 6 pathways that differ between high- and low-risk group. The immune-related gene signature effectively predicts the survival and immune infiltration of breast cancer patients and is expected to provide more effective immunotherapy targets for the prognosis prediction of breast cancer.     

ABCC8 is correlated with immune cell infiltration and overall survival in lower grade glioma

ATP binding cassette subfamily C member 8 (ABCC8) encodes a protein regulating the ATP-sensitive potassium channel. Whether the level of ABCC8 mRNA in lower grade glioma (LGG) correlates with immune cell infiltration and patient outcomes has not been evaluated until now. Comparisons of ABCC8 expression between different tumors and normal tissues were evaluated by exploring publicly available datasets. The association between ABCC8 and tumor immune cell infiltration, diverse gene mutation characteristics, tumor mutation burden (TMB), and survival in LGG was also investigated in several independent datasets. Pathway enrichment analysis was conducted to search for ABCC8-associated signaling pathways. Through an online database, we found that ABCC8 expression in LGG was lower than in normal tissues. Then, the association of ABCC8 expression and immune cell infiltration in LGG was discussed. As we expected, the ABCC8 mRNA levels were negatively associated with non-T immune cell infiltration levels in all datasets. Consistently, TCGA_LGG RNA-seq data revealed that ABCC8 downregulated several non-T immune cell-associated signaling pathways in gene set enrichment analysis. Different ABCC8 expression groups showed diverse gene mutation characteristics and TMB. The high expression of ABCC8 was linked to improved survival of LGG patients. A pathway enrichment analysis of ABCC8-associated genes indicated that the GABAergic synapse signaling pathway might be involved in regulating immunity in LGG. Our findings show that ABCC8 reflects LGG tumor immunity and is an ideal prognostic biomarker for LGG.     

Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma

PSMD14 played a vital role in initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database-Liver Hepatocellular Carcinoma (LIHC). Additionally, we used multi-dimensional bioinformatics analysis to construct and validate a PSMD14-based immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. Calibration curves confirmed the good consistency between the clinical nomogram prediction and the actual observation. Gene set enrichment analyses (GSEA) revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Besides, the rt-PCR further validates the expression of seven immune genes in HCC cells. Our study identified a novel PSMD14-based signature for HCC prognosis prediction, it provided new potential prognostic biomarkers and therapeutic targets for immunotherapy of HCC.     

An integrated bioinformatics analysis and experimental study identified key biomarkers CD300A or CXCL1, pathways and immune infiltration in diabetic nephropathy mice

Diabetic nephropathy (DN) is a common microvascular complication that easily leads to end-stage renal disease. It is important to explore the key biomarkers and molecular mechanisms relevant to diabetic nephropathy (DN). We used highthroughput RNA sequencing to obtain the genes related to DN glomerular tissues and healthy glomerular tissues of mice. Then we used LIMMA to analyze differentially expressed genes (DEGs) between DN and non-diabetic glomerular samples. And we performed KEGG, gene ontology functional (GO) enrichment, and gene set enrichment analysis to reveal the signaling pathway of the disease. The CIBERSORT algorithm based on support vector machine was used to determine the immune infiltration score. Random forest algorithm and Cytoscape obtained hub genes. Finally, we applied co-staining, immunohistochemical staining, RT-qPCR and western blotting to validate the protein and mRNA expression of both hub genes. We obtained 913 DEGs mainly related to inflammatory factors and immunity. GSEA results showed that differential genes were mainly enriched in IL-17 signaling pathway, lipid and atherosclerosis, rheumatoid arthritis, TNF signaling pathway, neutrophil extracellular trap formation, Staphylococcus aureus infection and other pathways. The intersection of the random forest algorithm and Cytoscape revealed both hub genes of CD300A and CXCL1. Experiments have shown that the both key genes of CD300A and CXCL1 shown increased expression in glomerular podocytes, and are related to the inflammation of diabetic nephropathy. And immunohistochemical staining and RT-qPCR further confirmed that the protein and mRNA expression level of CD300A or CXCL1 in glomeruli tissue in DN mice were increased. The expression levels of CD300A and CXCL1 increased significantly under HG (high glucose) stimulation, further confirming that diabetes can lead to increased levels of CD300A and CXCL1 at the cellular level. Through bioinformatics analysis, machine learning algorithms, and experimental research, CD300A and CXCL1 are confirmed as both potential biomarkers in diabetic nephropathy. And we further revealed the main pathways of differential genes and the differentially distributed immune infiltrating cells in diabetic nephropathy.     

Comprehensive analysis reveals an arachidonic acid metabolism-related gene signature in patients with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous, making its prognosis prediction difficult. The arachidonic acid (AA) cascade is involved in carcinogenesis. Therefore, the metabolic enzymes of the AA cascade consist of lipoxygenases (LOXs), phospholipase A2s (PLA2s), and cyclooxygenases (COXs) along with their metabolic products, including leukotrienes. Nevertheless, the prognostic potential of AA metabolism-associated PDAC has not been explored. Herein, the mRNA expression patterns and the matching clinical information of individuals with PDAC were abstracted from online data resources. We employed the LASSO Cox regression model to develop a multigene clinical signature in the TCGA queue. The GEO queue and the ICGC queue were employed as the validation queue. There was differential expression of a significant number of AA metabolism-associated genes (56.8%) between PDAC and neighboring nonmalignant tissues in the TCGA queue. Univariate Cox regression demonstrated that 13 of the differentially expressed genes (DEGs) were linked to overall survival (OS) (p < 0.05). A 6-gene clinical signature was developed for stratifying the PDAC patients into two risk groups, with the high-risk group patients exhibiting remarkably lower OS than the low-risk group patients (p < 0.001 in the TCGA data set and the ICGC queue, and p = 0.001 in the GEO data set). The multivariate Cox data revealed the risk score as an independent OS predictor (HR > 1, p < 0.01). The receiver operating characteristic (ROC) curve verified the predictive potential of our signature. The expression and alteration of the six genes in PDAC were also validated using online databases. Functional analyses demonstrated that immune-linked cascades were enriched, and the immune status was remarkably different between the high- and low-risk groups. In summary, an AA metabolism-associated clinical gene signature can be applied for prognostic estimation in PDAC.     

SPP1 and the risk score model to improve the survival prediction of patients with hepatocellular carcinoma based on multiple algorithms and back propagation neural networks

Hepatocellular carcinoma (HCC) is associated with poor prognosis and fluctuations in immune status. Although studies have found that secreted phosphoprotein 1 (SPP1) is involved in HCC progression, its independent prognostic value and immune-mediated role remain unclear. Using The Cancer Genome Atlas and Gene Expression Omnibus data, we found that low expression of SPP1 is significantly associated with improved survival of HCC patients and that SPP1 expression is correlated with clinical characteristics. Univariate and multivariate Cox regression confirmed that SPP1 is an independent prognostic factor of HCC. Subsequently, we found that T cell CD4 memory-activated monocytes, M0 macrophages, and resting mast cells showed significant differences in penetration in the high and low SPP1 expression groups. Next, we used the Weighted Gene Co-Expression Network and Least Absolute Shrinkage Sum Selection Operator algorithms to construct a risk score for the 9-immune-related genes signature. The risk score showed a good ability to identify high and low-risk patients and improved survival prediction. We also used multivariate Cox regression to validate that risk score was significantly correlated with SPP1 and overall survival. Lastly, the Back-Propagation Neural Network confirmed the reliability of the results of multiple algorithms. In conclusion, the findings suggest that SPP1 is an independent marker of HCC survival and immunotherapy.     

A novel prognostic target-gene signature and nomogram based on an integrated bioinformatics analysis in hepatocellular carcinoma

There is currently no effective solution to the problem of poor prognosis and recurrence of HCC. The technology of immunotherapy and prognosis of genetic material has made continuous progress in recent years. In the study, a 5-gene signature was established for the prognosis of HCC through biological information, and the immune infiltration of HCC patients was studied. After studied HCC patients’ immune infiltration, the paper screened the differential target genes of miR-126-3p in HCC downloaded from TCGA database, and uses WGCNA method to select the modular genes highly relevant to M2 macrophage. Then we use LASSO and COX regression analysis technology to establish the 5-gene signature. The nomogram is established by combining the prognostic score and clinical phenotype. Cibersort was empolyed to observe the immune infiltration in HCC patients. We revealed the biological pathways of HCC-related genes through GSEA and Metascape. The bioinformatics analysis of 2495 differential target genes finally constructed a 5-gene signature with a reliable prognostic ability (CDCA8, SLC41A3, PPM1G, TCOF1, GRPEL2). The combination of prognostic score and AJCC_Stage resulted in a more reliable prognosis ability. At the same time, 10 immune cells that are differentially expressed in HCC patients were also found. 8 GSEA pathways related to the prognosis were found. In the study, a reliable 5-gene signature was established based on the differential target gene of miR-126-3p to study the immune infiltration in HCC patients. It provides help for HCC-related prognosis research and immunotherapy.     

A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis

Purpose: Iron metabolism maintains the balance between iron absorption and excretion. Abnormal iron metabolism can cause numerous diseases, including tumor. This study determined the iron metabolism-related genes (IMRGs) signature that can predict the prognosis of acute myeloid leukemia (AML). The roles of these genes in the immune microenvironment were also explored. Methods: A total of 514 IMRGs were downloaded from the Molecular Characteristics Database (MSigDB). IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model. AML patients were stratified into high-risk groups (cluster 1) and low-risk groups (cluster 2) based on the mean value of the risk score. The accuracy and prognosis prediction potential of the risk-score model was evaluated using Kaplan-Meier and receiver operating characteristics analysis. The stromal score, immune scores, and immune cells infiltrated in AML samples were estimated using CIBERSORT, MCPcountre, and Xcell algorithms. The role of immune checkpoint genes in the AML microenvironment and the prognostic value of the IMRGs were also evaluated. Results: An AML prognosis prediction model was established based on the eight most critical IMRGs. Further analyses revealed that the model could accurately predict AML prognosis. The expression of IMRGs correlated with the infiltration of several immune cells and could influence response to certain chemotherapy drugs and immunotherapy. Conclusion: A model based on IMRGs can accurately predict the overall survival and disease-free survival of AML patients.     

A pan-cancer analysis of the biological function and clinical value of BTLA in tumors

B and T-lymphocyte attenuator (BTLA) plays an immunosuppressive role by inhibiting T- and B-cell functions. BTLA is associated with a variety of diseases, especially cancer immunity. However, the function of BTLA in various cancers and its clinical prognostic value have still not been comprehensively analyzed. This study aimed to identify the relationship between BTLA and cancer from the perspectives of differences in BTLA expression, its clinical value, immune infiltration, and the correlation with immune-related genes in various cancers. Data regarding mRNA expression, miRNA expression, lncRNA expression, and clinical data of patients of 33 existing cancers were collected from the TCGA database. Target miRNA of BTLA and the lncRNA that interacts with the target miRNA were obtained from the StarBase database. Based on bioinformatics analysis methods, the relationship between various types of cancers and BTLA was thoroughly investigated, and a competing endogenous RNA network of BTLA, target miRNA, and interacting lncRNA was constructed. The Kaplan-Meier (KM) prognostic analysis of BTLA and target miRNA (has-miR-137) in various types of cancers was completed using the KM plotter. BTLA expression varied in different cancers, with statistical significance in nine cancer types. KM plotter to analyze the overall survival (OS) and regression-free survival prognosis of cancer patients revealed that the BTLA expression was statistically different in the OS of 11 types of cancers out of 21 types of cancers; the OS of 8 type of cancers was also statistically different. Correlation analysis of tumor immune genes revealed a positive correlation of BTLA expression with immunosuppressive gene (CTLA4 and PDCD1) expression. Gene Set Enrichment Analysis showed that BTLA and its co-expressed genes mainly act through biological processes and pathways, including immune response regulation, cell surface receptor signaling pathway, antigen binding, antigen receptor-mediated signaling pathway, and leukocyte migration. BTLA has the potential as a prognostic marker for CLL, COAD, NSCLC, and OV and a diagnostic marker for CLL, COAD, and KIRC. BTLA has a close and complex relationship with the occurrence and development of tumors, and cancer immunotherapy for BTLA is worthy of further analysis.     

Prognostic prediction and expression validation of NSD3 in pan-cancer analyses

Background: Nuclear receptor binding SET domain protein-3 (NSD3) is a histone lysine methyltransferase and a crucial regulator of carcinogenesis in several cancers. We aimed to investigate the prognostic value and potential function of NSD3 in 33 types of human cancer. Methods: The data were obtained from The Cancer Genome Atlas. Kaplan-Meier analysis, CIBERSORT, gene set enrichment analysis, and gene set variation analysis were performed. The expression of NSD3 was measured using quantitative real-time polymerase chain reaction and western blot. Results: The expression of NSD3 was altered in pan-cancer samples. Patients with higher levels of NDS3 generally had shorter overall survival and disease-specific survival. Levels of NSD3 were positively correlated with DNA copy number variation (CNV) in pan-cancer. NSD3 expression was also associated with tumor mutation burden and microsatellite instability. The levels of immune-cell infiltration differed significantly between high and low NSD3 expression. NSD3 negatively correlated with levels of CD8+ T cells. Functional enrichment analysis showed that while NSD3 expression was positively associated with several immune cell-related and histone methylation-related pathways, it was negatively correlated with cell metabolism-related, drug transport-related, and drug metabolism-related pathways. NSD3 levels in the cell lines tested were significantly different. In U251 and NCI-H23 cells, silencing NSD3 inhibited cell proliferation and promoted apoptosis. Conclusions: NSD3 expression was changed in pan-cancer samples that was also verified in cell lines. NSD3 was associated with CNV and immune-cell infiltration. A poor prognosis was predicted in patients with high expression of NSD3. NSD3 might hence be a potential marker for predicting tumor prognosis.     

Identification of a 10-pseudogenes signature as a novel prognosis biomarker for ovarian cancer

The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late stage. Identifying the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently warranted. The survival-related pseudogenes retrieved from the Cancer Genome Atlas database were screened by univariate Cox regression analysis and further assessed by least absolute shrinkage and selection operator (LASSO) method. A risk score model based on the prognostic pseudogenes was also constructed. The pseudogene-mRNA regulatory networks were established using correlation analysis, and their potent roles in the ovarian cancer progression were uncovered by functional enrichment analysis. Lastly, ssGSEA and ESTIMATE algorithms was used to evaluate the levels of immune cell infiltrations in cancer tissues and explore their relationship with risk signature. A prediction model of 10-pseudogenes including RPL10P6, AC026688.1, FAR2P4, AL391840.2, AC068647.2, FAM35BP, GBP1P1, ARL4AP5, RPS3AP2, and AMD1P1 was established. The 10-pseudogenes signature was demonstrated to be an independent prognostic factor in patient with ovarian cancer in the random set (hazard ratio [HR] = 2.512, 95% confidence interval [CI] = 2.03–3.11, P < 0.001) and total set (HR = 1.71, 95% CI = 1.472–1.988, P < 0.001). When models integrating with age, grade, stage, and risk signature, the Area Under Curve (AUC) of the 1-year, 3-year, 5-year and 10-year Receiver Operating Characteristic curve in the random set and total set were 0.854, 0.824, 0.855, 0.805 and 0.679, 0.697, 0.739, 0.790, respectively. The results of functional enrichment analysis indicated that the underlying mechanisms by which these pseudogenes influence cancer prognosis may involve the immune-related biological processes and signaling pathways. Correlation analysis showed that risk signature was significantly correlated with immune cell infiltration and immune score. We identified a novel 10-pseudogenes signature to predict the survival of patients with ovarian cancer, and that may serve as novel possible prognostic biomarkers and therapeutic targets for ovarian cancer.     

Development of a prognostic signature for esophageal cancer based on a novel 7-DNA damage repair genes signature

Esophageal cancer (EC) was an aggressive malignant neoplasm characterized by high morbidity and poor prognosis. Identifying the changes in DNA damage repair genes helps to better understand the mechanisms of carcinoma progression. In this study, by comparing EC samples and normal samples, we found a total of 132 DDR expression with a significant difference. Moreover, we revealed higher expression of POLN, PALB2, ATM, PER1, TOP3B and lower expression of HMGB1, UBE2B were correlated to longer OS in EC. In addition, a prognostic risk score based on 7 DDR gene expression (POLN, HMGB1, TOP3B, PER1, UBE2B, ATM, PALB2) was constructed for the prognosis of EC. Meanwhile, EC cancer samples were divided into 3 subtypes based on 132 DDR genes expressions. Clinical profile analysis showed cluster C1 and C2 showed a similar frequency of T2, which was remarked higher than that in cluster 3. Moreover, we found the immune cell inflation levels were significantly changed in different subtypes of EC. The infiltration levels of T cell CD8+, B cell and NK cells were greatly higher in cluster 2 than that in cluster 1 and cluster 3. The results showed T cell CD4+ infiltration levels were dramatically higher in cluster 1 than that in cluster 2 and cluster 3. Finally, we perform bioinformatics analysis of DEGs among 3 subtypes of EC and found DDR genes may be related to multiple signaling, such as Base excision repair, Cell cycle, Hedgehog signaling pathway, and Glycolysis/Gluconeogenesis. These results showed DDR genes may serve as new target for the prognosis of EC and prediction of the potential response of immune therapy in EC.     

Prognosis and immunological role of HLA-DMA in lung adenocarcinoma

Background: HLA-DMA presents pathogen-derived antigens to CD4+ and CD8+ T cells, respectively, and plays a significant part in initiating the immune response. So far, the impact of HLA expression on the prognosis of BC cells is controversial, because few studies have shown that the expressions of some HLA genes are related to the improvement of the survival rate. Up till now, however, the relationship between HLA-DMA and LUAD has not yet been assessed. Methods: We analyzed the TCGA database and assessed the prognostic value of HLA-DMA in LUAD. We conducted the Kruskal–Wallis and Wilcoxon signed-rank test and utilized logistic regression to assess the role of clinical-pathologic characteristics and HLA-DMA expression. Kaplan–Meier method and the multivariate and univariate Cox regression were also used for evaluating the prognosis-related factors of LUAD. GSEA was used to identify HLA-DMA-related key pathways. The ssGSEA of the TCGA data was used to investigate the correlations between HLA-DMA and cancer immune cell infiltration. Results: Low HLA-DMA expression was related to poorer disease-specific survival (DSS) and overall survival (OS) of LUAD patients. GSEA revealed that HLA-DMA was tightly interrelated with an immune response by the reactome activation of anterior hox genes in hindbrain development during the early embryogenesis signaling pathway. The expression of HLA-DMA was positively associated with cytotoxic cell infiltration and negatively related to the Th2 cell infiltration according to the ssGSEA. Western blotting and the CCK-8 assay showed that KD-HLA-DMA could significantly increase the proliferation of A549 cells and significantly reduce cell pyroptosis. Conclusion: All the observations implied that HLA-DMA was associated with patient prognosis and immune infiltration in LUAD.