Prognosis-associated Genes Research Articles

Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer

Colorectal cancer (CRC) is the third most common malignant tumor. DNA damage plays a crucial role in tumorigenesis, and abnormal DNA repair pathways affect the occurrence and progression of CRC. In the current study, we aimed to construct a DNA repair-related gene (DRG) signature to predict the overall survival (OS) of patients with CRC patients. The differentially expressed DRGs (DE-DRGs) were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic gene signature was identified by univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated using the Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. The gene set enrichment analysis (GSEA) was performed to explore the underlying biological processes and signaling pathways. ESTIMATE and CIBERSORT were implemented to estimate the tumor immune score and immune cell infiltration status between the different risk group. The half-maximal inhibitory concentration (IC50) was evaluated to representing the drug response of this signature. Nine DE-DRGs (ESCO2, AXIN2, PLK1, CDC25C, IGF1, TREX2, ALKBH2, ESR1 and MC1R) signatures was constructed to classify patients into high- and low-risk groups. The risk score was an independent prognostic indicator of OS (hazard ratio > 1, P < 0.001). The genetic alteration analysis indicated that the nine DE-DRGs in the signature were changed in 63 required samples (100%), and the major alteration was missense mutation. Function enrichment analysis revealed that the immune response and mtotic sister chromatid segregation were the main biological processes. The high-risk group had higher immune score than the low-risk group. What’s more, low-risk patients were more sensitive to selumetinib and dasatinib. The nine DE-DRGs signature was significantly associated with OS and provided a new insight for the diagnosis and treatment of CRC.

The identification of liver metastasis- and prognosis-associated genes in pancreatic ductal adenocarcinoma

BackgroundPancreatic ductal adenocarcinoma (PDAC) is an often fatal malignancy with an extremely low survival rate. Liver metastasis, which causes high mortality, is the most common recurring metastasis for PDAC. However, the mechanisms underlying this liver metastasis and associated candidate biomarkers are unknown.MethodsWe performed mRNA profiling comparisons in 8 primary tumors (T) and 12 liver metastases (M) samples using the Gene Expression Omnibus (GEO) database. After determining differentially expressed genes (DEG), gene ontology (GO), pathway enrichment and protein–protein interaction (PPI) network analyses were performed to determine DEG functions. Then, Cytoscape was used to screen out significant hub genes, after which their clinical relevance was investigated using The Cancer Genome Atlas (TCGA) resources. Furthermore, prognosis-associated gene expression was validated using Oncomine and TCGA database. Lastly, associations between prognosis-associated genes, immune cells and immunological checkpoint genes were evaluated using the Tumor Immune Estimation Resource (TIMER).ResultsIn total, 102 genes were related to liver metastasis and predominantly involved in cell migration, motility, and adhesion. Using Cytoscape, this number was narrowed down to 16 hub genes. Elevated mRNA expression levels for two of these genes, SPARC (P = 0.019) and TPM1 (P = 0.037) were significantly correlated with poor disease prognosis. For the remaining 14, expression was not related to overall patient survival. SPARC had higher expression in patients with metastatic PDAC than those with non-metastatic PDAC in TCGA dataset. SPARC and TPM1 levels were also positively correlated with the immune infiltration of specific cell types. Additionally, both genes exhibited strong co-expression associations with immune checkpoint genes.ConclusionsCombined, we suggest SPARC has high potential as biomarker to predict liver metastasis during PDAC. Additionally, both SPARC and TPM1 appeared to recruit and regulate immune-infiltrating cells during these pathophysiological processes.

Human cytomegalovirus-induced immune regulation is correlated with poor prognosis in patients with colorectal cancer.

Evidence suggests that human cytomegalovirus (HCMV) infection may be implicated in the progression of colorectal cancer (CRC). However, the correlation between HCMV infection and survival outcomes in patients with CRC remains unclear. Here, we constructed a flow algorithm to identify HCMV sequences based on the RNA-seq data of patients with CRC derived from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The patients' clinical information matrix was used to calculate the Euclidean distance to filter out suitable patients not infected with HCMV, combined with patients' survival outcomes, to reveal how HCMV infection is involved in CRC progression. HCMV infection is widespread in patients with CRC, and the prevalence of HCMV infection ranges from 10 to 36% in four independent CRC datasets, with infection being concentrated in carcinoma tissue rather than in normal tissue. In addition, HCMV-positive patients had a poor survival prognosis, with three HCMV genes, UL82, UL42, and UL117, associated with poor patient survival outcomes. Most importantly, we suppose that the regulation of immune function by HCMV may be key to the poor prognosis of patients with CRC. We found that HCMV infection was associated with poor prognosis in CRC patients and identified three prognosis-associated HCMV genes. The regulation of immune function caused by HCMV infection was the key factor, while HCMV-positive patients with CRC mostly presented with a state of immunosuppression. This may provide new ideas for the personalized treatment of patients with CRC, especially with respect to immunotherapy.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

Background: Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.Methods: A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.Results: Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61–3.53) in the validation set I, 1.35 (95% CI, 1.06–1.71) in the validation set II, and 2.71 (95% CI, 1.77–4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.Conclusions: In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

Comprehensive characterization of m6A methylation and its impact on prognosis, genome instability, and tumor microenvironment in hepatocellular carcinoma

BackgroundN6-methyladenosine (m6A) RNA regulation was recently reported to be important in carcinogenesis and cancer development. However, the characteristics of m6A modification and its correlations with clinical features, genome instability, tumor microenvironments (TMEs), and immunotherapy responses in hepatocellular carcinoma (HCC) have not been fully explored.MethodsWe systematically analyzed the m6A regulator-based expression patterns of 486 patients with HCC from The Cancer Genome Atlas and Gene Expression Omnibus databases, and correlated these patterns with clinical outcomes, somatic mutations, TME cell infiltration, and immunotherapy responses. The m6A score was developed by principal component analysis to evaluate m6A modifications in individual patients.ResultsM6A regulators were dysregulated in HCC samples, among which 18 m6A regulators were identified as risk factors for prognosis. Three m6A regulator-based expression patterns, namely m6A clusters, were determined among HCC patients by m6A regulators with different m6A scores, somatic mutation counts, and specific TME features. Additionally, three distinct m6A regulator-associated gene-based expression patterns were also identified based on prognosis-associated genes that were differentially expressed among the three m6A clusters, showing similar properties as the m6A regulator-based expression patterns. Higher m6A scores were correlated with older age, advanced stages, lower overall survival, higher somatic mutation counts, elevated PD-L1 expression levels, and poorer responses to immune checkpoint inhibitors. The m6A score was validated as an independent and valuable prognostic factor for HCC.ConclusionM6A modification is correlated with genome instability and TME in HCC. Evaluating m6A regulator-based expression patterns and the m6A score of individual tumors may help identify candidate biomarkers for prognosis prediction and immunotherapeutic strategy selection.

Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma

SummaryThe development of reliable biomarkers has been an urgent issue as well as a hot spot of research on the diagnosis, treatment, and prognostic evaluation of hepatocellular carcinoma (HCC). Here, we established and validated two field cancerization profile-based prognostic signatures (gene expression score [GES] and immune score [IS]) for HCC. Our study confirmed that field cancerization profile-based models outperform conventional models on risk evaluation, offering insights for further studies on prognostic model construction. The nomogram constructed by combining GES, IS, and TNM stage was proved effective in improving the individualized prediction of the overall risk of patients. Distinct peritumoral characteristics were observed in several immune cells (e.g., CD8 T cells and dendritic cells), which might explain the diversified prognosis and clinical benefit of immunotherapy. Moreover, a series of drug targets, prognosis-associated genes, and pathways were identified, which may contribute to molecular mechanism studies as well as therapeutic target development of HCC.

Development and Validation of a Necroptosis-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

Necroptosis is a form of cell death, this study aimed to explore and verify the relationship between clear cell renal cell carcinoma and necroptosis, and to construct a necroptosis-related prognostic signature for diagnosis and treatment of clear cell renal cell carcinoma. 159 necroptosis-related genes were screened from the Kyoto encyclopedia of genes and genomes database of which 51 were differentially expressed in clear cell renal cell carcinoma, which included 16 prognosis-associated genes. Enrichment analysis showed that most of the 16 genes were related to necroptosis, autophagy and the Wingless-related integration site signaling pathway. From the 16 genes, 11 independent necroptosis-related genes associated with clear cell renal cell carcinoma prognosis were screened by least absolute shrinkage and selection operator regression and multivariate Cox regression. These genes (BH3 interacting domain death agonist, Janus kinase 3, RANBP2-type and C3HC4- type zinc finger containing 1, solute carrier family 25 member 4, interferon gamma receptor 2, phospholipase A2 group IVD, toll-like receptor 3, H2A clustered histone 7, interferon regulatory factor 9, phospholipase A2 group IVB and H2A clustered histone 17) were used to construct a prognostic signature which was assessed by multivariate Cox regression analysis. Also, the relationship between the risk score and the stage and grade was determined. Receiver operating characteristic analysis and the decision curve analysis demonstrated the validity and clinical value of the prediction model which was internally validated. In this study, we explored the potential link between clear cell renal cell carcinoma and necroptosis to establish and validate an 11 gene prognostic signature that can effectively predict clear cell renal cell carcinoma prognosis.

Social Deprivation Independently Predicts Survival in Younger Patients with Acute Myeloid Leukemia (Alliance)

Social Deprivation Independently Predicts Survival in Younger Patients with Acute Myeloid Leukemia (Alliance)

Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes.

Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In the present study, a non-small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores.Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail.Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model's effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions.Conclusion: The risk prediction model established with seven ADME genes in the present study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.

A Pyroptosis-Related Gene Panel in Prognosis Prediction and Immune Microenvironment of Human Endometrial Cancer.

As the second common diagnosed cancer among gynecological tumors, endometrial cancer (EC) has heterogeneous pathogenesis and clinical manifestations. Therefore, prognosis prediction that considers gene expression value and clinical characteristics, is helpful to patients with EC. We downloaded RNA expression and clinical data from the TCGA database. We achieved 4 DEPRGs and constructed the PRG panel by univariate, lasso and multivariate Cox analysis. Based on the median value of the risk score, patients were divided into two groups. The Kaplan–Meier curve suggested that the patients with lower risk scores had better clinical outcomes of EC. AUC of ROC curves suggested the panel can be used as an independent predictor. Future analysis indicated the positive correlations between risk score and clinical characteristics. What’s more, we performed GO and KEGG functional analysis and immune environment exploration to get an understanding of the potential molecular mechanism and immunotherapeutic target. To future validate the panel, we found that the relapse-free and overall survival probability of 4 prognostic DEPRGs between high-expression group and low-expression group were different through the Kaplan–Meier plotter in UCEC. In addition, GEPIA database and RT-PCR experiment indicated GPX4 and GSDMD were highly expressed in UCEC compared to normal endometrial tissue, and TIRAP and ELANE were downregulated. This study identified a PRG panel to predict the prognosis immune microenvironment in human EC. Then, Kaplan–Meier analysis and AUC below the ROC curves was used to validate the panel. In addition, Chi-square was used to show the clinical significance. GO, KEGG and GSEA were used to show the functional differences. Different immune-related databases were used to analyze the immune characteristics. The Kaplan–Meier plotter website was used to assess the effect of genes on survival. GEPIA and RT-PCR were used to analyze the expression level. In summary, we identified 4 prognosis-associated pyroptosis-related genes (ELANE, GPX4, GSDMD, and TIRAP). The panel can also predict prognosis prediction and immune microenvironment in human endometrial cancer.

Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment

BackgroundBreast cancer is the most common malignancy in women. Cancer driver gene-mediated alterations in the tumor microenvironment are critical factors affecting the biological behavior of breast cancer. The purpose of this study was to identify the expression characteristics and prognostic value of cancer driver genes in breast cancer.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets are used as the training and test sets. Classified according to cancer and paracancerous tissues, we identified differentially expressed cancer driver genes. We further screened prognosis-associated genes, and candidate genes were submitted for the construction of a risk signature. Functional enrichment analysis and transcriptional regulatory networks were performed to search for possible mechanisms by which cancer driver genes affect breast cancer prognosis.ResultsWe identified more than 200 differentially expressed driver genes and 27 prognosis-related genes. High-risk group patients had a lower survival rate compared to the low-risk group (P<0.05), and risk signature showed high specificity and sensitivity in predicting the patient prognosis (AUC 0.790). Multivariate regression analysis suggested that risk scores can independently predict patient prognosis. Further, we found differences in PD-1 expression, immune score, and stromal score among different risk groups.ConclusionOur study confirms the critical prognosis role of cancer driver genes in breast cancer. The cancer driver gene risk signature may provide a novel biomarker for clinical treatment strategy and survival prediction of breast cancer.

Identification of Prognosis-Associated Biomarkers in Thyroid Carcinoma by a Bioinformatics Analysis.

BackgroundThis study aimed to identify the key genes associated with prognosis in thyroid cancer (TC), and to explore potential pathways.MethodsGSE66783, GSE58545, and GSE129562 datasets were used to identify differentially expressed genes (DEGs) between tumor and normal tissues, followed by KEGG analyses on DEGs. The protein–protein interaction (PPI) network of DEGs was subsequently constructed to find the top 10 hub genes and seed genes in the whole network. Furthermore, the mRNA expressions of hub genes and prognostic values were explored. Regarding the seed gene, pathway activity score and GSEA analyses were conducted as well.Results1) A total of 183 DEGs were consistently expressed in three datasets comprising 76 up-regulated and 107 down-regulated genes. DEGs were mainly enriched in the p53 signaling pathway, complement and coagulation cascades, and hedgehog signaling pathway. 2) The top 10 hub genes, including CCND1, TIMP1, ICAM1, MET, PLAU, LDLR, PLAUR, ITGA2, ITGA3, and LGALS3, were identified. All hub genes were highly expressed in TC compared with normal samples. 3) High expression of CCND1, TIMP1, MET, and LGALS3 statistically correlated with a favorable prognosis of patients. Poor survival was observed in patients with ITGA2 and ITGA3 high expression. There was no association between ICAM1, PLAU, and PLAUR expression and survival of patients. LGALS3 and TIMP1 were further identified as independent prognostic factors in TC. 4) Among 10 hub genes, TIMP1 was determined as the seed gene, indicating its significance in the whole network. We further found that in most of the famous cancer-related pathways, TIMP1 higher expression caused a lower pathway activity, suggesting its inhibitory effect to these pathways in TC. In addition, TIMP1 positively correlated with the p53 signaling pathway, complement, and coagulation cascades involved in TC.ConclusionThe present study provided seven prognosis-associated genes in TC and revealed several significant pathways, which contributed to elucidate the pathogenesis of TC.

Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes.

BackgroundThe pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.MethodsFrom the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.ResultsTwo molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.ConclusionThis research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity.

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer.

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.

Construction of a prognostic risk model of colorectal adenocarcinoma through integrated analysis of RNA-binding proteins.

BackgroundRNA binding proteins (RBPs) play an important role in a variety of cancers. However, their mechanisms in cancer progression are still limited especially in colorectal adenocarcinoma (COAD). Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment.MethodsThe gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients.ResultsA total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model.ConclusionsWe constructed a COAD prognostic model through bioinformatics analysis and the nomogram can effectively predict the 1-year, 2-year, and 3-year survival rate for COAD patients.

NEIL3 may act as a potential prognostic biomarker for lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related death. This study aimed to develop and validate reliable prognostic biomarkers and signature.MethodsDifferentially expressed genes were identified based on three Gene Expression Omnibus (GEO) datasets. Based on 1052 samples’ data from our cohort, GEO and The Cancer Genome Atlas, we explored the relationship of clinicopathological features and NEIL3 expression to determine clinical effect of NEIL3 in LUAD. Western blotting (22 pairs of tumor and normal tissues), Real-time quantitative PCR (19 pairs of tumor and normal tissues), and immunohistochemical analyses (406-tumor tissues subjected to microarray) were conducted. TIMER and ImmuCellAI analyzed relationship between NEIL3 expression and the abundance of tumor-infiltrating immune cells in LUAD. The co-expressed-gene prognostic signature was established based on the Cox regression analysis.ResultsThis study identified 502 common differentially expressed genes and confirmed that NEIL3 was significantly overexpressed in LUAD samples (P < 0.001). Increased NEIL3 expression was related to advanced stage, larger tumor size and poor overall survival (p < 0.001) in three LUAD cohorts. The proportions of natural T regulatory cells and induced T regulatory cells increased in the high NEIL3 group, whereas those of B cells, Th17 cells and dendritic cells decreased. Gene set enrichment analysis indicated that NEIL3 may activate cell cycle progression and P53 signaling pathway, leading to poor outcomes. We identified nine prognosis-associated hub genes among 370 genes co-expressed with NEIL3. A 10-gene prognostic signature including NEIL3 and nine key co-expressed genes was constructed. Higher risk-score was correlated with more advanced stage, larger tumor size and worse outcome (p < 0.05). Finally, the signature was verified in test cohort (GSE50081) with superior diagnostic accuracy.ConclusionsThis study suggested that NEIL3 has the potential to be an immune-related therapeutic target and an independent predictor of LUAD prognosis. We also developed a prognostic signature for LUAD with a precise diagnostic accuracy.

Development and validation of an oxidative stress—associated prognostic risk model for melanoma

BackgroundOxidative stress (OS) is key to various diseases and is implicated in cancer progression and oncogenesis. However, the potential diagnostic value of OS-related genes in skin cutaneous melanoma (SKCM) remains unclear.MethodsWe used data of RNA sequencing from 471 tumor tissues and one healthy tissue acquired from The Cancer Genome Atlas (TCGA)-SKCM cohort. The Genome Tissue Expression database was used to acquire transcriptome data from 812 healthy samples. OS-related genes that were differentially expressed between SKCM and healthy samples were investigated and 16 prognosis-associated OS genes were identified. The prognostic risk model was built using univariate and Cox multivariate regressions. The prognostic value of the hub genes was validated in the GSE65904 cohort, which included 214 SKCM patients.ResultsThe overall survival rate of SKCM patients in the high-risk group was decreased compared to the low-risk group. In both TCGA and GSE65904 cohorts, the ROC curves suggested that our prognostic risk model was more accurate than other clinicopathological characteristics to diagnose SKCM. Moreover, risk score and nomograms associated with the expression of hub genes were developed. These presented reiterated our prognostic risk model. Altogether, this study provides novel insights with regards to the pathogenesis of SKCM. The 16 hub genes identified may help in SKCM prognosis and individualized clinical treatment.

Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis.

IntroductionThis study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis.Materials and methodsGene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene’s protein levels with an immunohistochemical assay to confirm the accuracy of our analysis.ResultsA total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues.ConclusionFOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.