Overexpression of MYEOV predicting poor prognosis in patients with pancreatic ductal adenocarcinoma

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background

Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models.

Methods

To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models.

Results

We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154).

Conclusion

The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

MYEOV with High Frequencies of Mutations in Head and Neck Cancers Facilitates Cancer Cell Malignant Behaviors

Cancer driver genes (CDGs) and the driver mutations disrupt the homeostasis of numerous critical cell activities, thereby playing a critical role in tumor initiation and progression. In this study, integrative bioinformatics analyses were performed based on a series of online databases, aiming to identify driver genes with high frequencies of mutations in head and neck cancers. Higher myeloma overexpressed (MYEOV) genetic variation frequency and expression level were connected to a poorer prognosis in head and neck cancer patients. MYEOV was dramatically upregulated within head and neck tumor samples and cells. Consistently, MYEOV overexpression remarkably enhanced the aggressiveness of head and neck cancer cells by promoting colony formation, cell invasion, and cell migration. Conversely, MYEOV knockdown attenuated cancer cell aggressiveness and inhibited tumor growth and metastasis in the oral orthotopic tumor model. In conclusion, MYEOV is overexpressed in head and neck cancer, with greater mutation frequencies correlating to a poorer prognosis in head and neck cancer patients. MYEOV serves as an oncogene in head and neck cancer through the promotion of tumor cell colony formation, invasion, and migration, as well as promoting tumor growth and metastasis in the oral orthotopic tumor model.

Super-enhancer mediated upregulation of MYEOV suppresses ferroptosis in lung adenocarcinoma

Super-enhancers (SEs) exerted a crucial role in regulating the transcription of oncogenes across various malignancies while the roles of SEs driven genes and the core regulatory elements remain elusive in LUAD. In this study, cancer-specific-SE-genes of lung adenocarcinoma (LUAD) were profiled through H3K27ac ChIP-seq data of cancer cell lines and normal lung tissues, which enriched in in biological processes and pathways integral to the pathophysiology of LUAD. Based on this study, LUAD cells were susceptible to SEs inhibitors, with a reduction of cell proliferation as well as an elevation of apoptosis upon JQ1 or THZ1 intervention. Moreover, the integration of SEs landscapes, CRISPRi, ChIP-PCR, Hi-C data analysis and dual-luciferase reporter assays revealed that myeloma overexpressed gene (MYEOV) was aberrantly overexpressed in LUAD via transcriptional activation by the core SE elements. Functionally, the knockdown of MYEOV undermined cell proliferation in vitro and tumor growth in vivo. In addition, the knockdown of MYEOV generated a prominent ferroptotic phenotype, characterized by elevation of intracellular ferrous iron, reactive oxygen species and lipid peroxidation, together with alteration in marker proteins (SLC7A11, GPX4, FTH1, and ACSL4). Instead, the overexpression of MYEOV accelerated cell proliferation and abrogated ferroptosis. Clinically, the overexpression of MYEOV was observed in LUAD tissues indicating a poor prognosis in patients with LUAD. Mechanistically, SMPD1-induced autophagic degradation of GPX4 assumed a crucial role in the process of ferroptosis triggered by MYEOV knockdown. Serving as an oncogene repressing ferroptosis, promoting proliferation as well as shortening survival in LUAD, SEs-mediated activation of MYEOV might distinguish as a promising therapeutic target.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

PURPOSE

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Genome, Metabolism, or Immunity: Which Is the Primary Decider of Pancreatic Cancer Fate through Non-Apoptotic Cell Death?

Pancreatic ductal adenocarcinoma (PDAC) is a solid tumor characterized by poor prognosis and resistance to treatment. Resistance to apoptosis, a cell death process, and anti-apoptotic mechanisms, are some of the hallmarks of cancer. Exploring non-apoptotic cell death mechanisms provides an opportunity to overcome apoptosis resistance in PDAC. Several recent studies evaluated ferroptosis, necroptosis, and pyroptosis as the non-apoptotic cell death processes in PDAC that play a crucial role in the prognosis and treatment of this disease. Ferroptosis, necroptosis, and pyroptosis play a crucial role in PDAC development via several signaling pathways, gene expression, and immunity regulation. This review summarizes the current understanding of how ferroptosis, necroptosis, and pyroptosis interact with signaling pathways, the genome, the immune system, the metabolism, and other factors in the prognosis and treatment of PDAC.

MYEOV overexpression induced by demethylation of its promoter contributes to pancreatic cancer progression via activation of the folate cycle/c-Myc/mTORC1 pathway

BACKGROUND

While molecular targeted drugs and other therapies are being developed for many tumors, pancreatic cancer is still considered to be the malignant tumor with the worst prognosis. We started this study to identify prognostic genes and therapeutic targets of pancreatic cancer.

METHODS

To comprehensively identify prognostic genes in pancreatic cancer, we investigated the correlation between gene expression and cancer-specific prognosis using transcriptome and clinical information datasets from The Cancer Genome Atlas (TCGA). In addition, we examined the effects of the suppression of candidate prognostic genes in pancreatic cancer cell lines.

RESULT

We found that patients with high expression levels of MYEOV, a primate-specific gene with unknown function, had significantly shorter disease-specific survival times than those with low expression levels. Cox proportional hazards analysis revealed that high expression of MYEOV was significantly associated with poor survival and was an independent prognostic factor for disease-specific survival in pancreatic cancer patients. Analysis of multiple cancer samples revealed that the MYEOV promoter region is methylated in noncancer tissues but is demethylated in tumors, causing MYEOV overexpression in tumors. Notably, the knockdown of MYEOV suppressed the expression of MTHFD2 and other folate metabolism-related enzyme genes required for the synthesis of amino acids and nucleic acids and also restored the expression of c-Myc and mTORC1 repressors.

CONCLUSION

There is a significant correlation between elevated MYEOV expression and poor disease-specific survival in pancreatic cancer patients. MYEOV enhances the activation of several oncogenic pathways, resulting in the induction of pancreatic cancer cell proliferation. Overall, MYEOV acts as an oncogene in pancreatic cancer. Furthermore, MYEOV may be a prognostic biomarker and serve as an 'actionable' therapeutic target for pancreatic cancers.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis

Background

As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.

Methods

We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.

Results

We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.

Conclusion

In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.

Comprehensive Analysis of Necroptosis in Pancreatic Cancer for Appealing its Implications in Prognosis, Immunotherapy, and Chemotherapy Responses

Objective: Necroptosis represents a new target for cancer immunotherapy and is considered a form of cell death that overcomes apoptosis resistance and enhances tumor immunogenicity. Herein, we aimed to determine necroptosis subtypes and investigate the roles of necroptosis in pancreatic cancer therapy. Methods: Based on the expression of prognostic necroptosis genes in pancreatic cancer samples from TCGA and ICGC cohorts, a consensus clustering approach was implemented for robustly identifying necroptosis subtypes. Immunogenic features were evaluated according to immune cell infiltrations, immune checkpoints, HLA molecules, and cancer-immunity cycle. The sensitivity to chemotherapy agents was estimated using the pRRophetic package. A necroptosis-relevant risk model was developed with a multivariate Cox regression analysis. Results: Five necroptosis subtypes were determined for pancreatic cancer (C1∼C5) with diverse prognosis, immunogenic features, and chemosensitivity. In particular, C4 and C5 presented favorable prognosis and weakened immunogenicity; C2 had high immunogenicity; C1 had undesirable prognosis and high genetic mutations. C5 was the most sensitive to known chemotherapy agents (cisplatin, gemcitabine, docetaxel, and paclitaxel), while C4 displayed resistance to aforementioned agents. The necroptosis-relevant risk model could accurately predict prognosis, immunogenicity, and chemosensitivity. Conclusion: Our findings provided a conceptual framework for comprehending necroptosis in pancreatic cancer biology. Future work is required for evaluating its relevance in the design of combined therapeutic regimens and guiding the best choice for immuno- and chemotherapy.

Identification of a Four Cancer Stem Cell-Related Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Adenocarcinoma

BACKGROUND

Cancer stem cells (CSCs) are now being considered as the initial component in the development of pancreatic adenocarcinoma (PAAD). Our aim was to develop a CSCrelated signature to assess the prognosis of PAAD patients for the optimization of treatment.

METHODS

Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue in the Cancer Genome Atlas (TCGA) were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the CSC-related gene sets. Then, univariate, Lasso Cox regression analyses and multivariate Cox regression were applied to construct a prognostic signature using the CSC-related genes. Its prognostic performance was validated in TCGA and ICGC cohorts. Furthermore, Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in PAAD, and a prognostic nomogram was established.

RESULTS

The Kaplan-Meier analysis, ROC curve and C-index indicated the good performance of the CSC-related signature at predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression revealed that the CSC-related signature was an independent prognostic factor in PAAD. The nomogram was superior to the risk model and AJCC stage in predicting OS. In terms of mutation and tumor immunity, patients in the high-risk group had higher tumor mutation burden (TMB) scores than patients in the low-risk group, and the immune score and the ESTIMATE score were significantly lower in the high-risk group. Moreover, according to the results of principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA), the low-risk and high-risk groups displayed different stemness statuses based on the risk model.

CONCLUSION

Our study identified four CSC-related gene signatures and established a prognostic nomogram that reliably predicts OS in PAAD. The findings may support new ideas for screening therapeutic targets to inhibit stem characteristics and the development of PAAD.

A prognostic biomarker study in patients with clinical stage I esophageal squamous cell carcinoma: JCOG0502-A1

We conducted genomic analyses of Japanese patients with stage I esophageal squamous cell carcinoma (ESCC) to investigate the frequency of genomic alterations and the association with survival outcomes. Biomarker analysis was conducted for patients with clinical stage T1bN0M0 ESCC enrolled in JCOG0502 (UMIN000000551). Whole-exome sequencing (WES) was performed using DNA extracted from formalin-fixed, paraffin-embedded tissue of ESCC and normal tissue or blood sample. Single nucleotide variants (SNVs), insertions/deletions (indels), and copy number alterations (CNAs) were identified. We then evaluated the associations between each gene alteration with a frequency ≥10% and progression-free survival (PFS) using a Cox regression model. We controlled for family-wise errors at 0.05 using the Bonferroni method. Among the 379 patients who were enrolled in JCOG0502, 127 patients were successfully analyzed using WES. The median patient age was 63 years (IQR, 57-67 years), and 78.0% of the patients ultimately underwent surgery. The 3-year PFS probability was 76.3%. We detected 20 genes with SNVs, indels, or amplifications with a frequency of ≥10%. Genomic alterations in FGF19 showed the strongest association with PFS with a borderline level of statistical significance of p = 0.00252 (Bonferroni-adjusted significance level is 0.0025). Genomic alterations in FGF4, MYEOV, CTTN, and ORAOV1 showed a marginal association with PFS (p < 0.05). These genomic alterations were all CNAs at chromosome 11q13.3. We have identified new genomic alterations associated with the poor efficacy of ESCC (T1bN0M0). These findings open avenues for the development of new potential treatments for patients with ESCC.

Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

BACKGROUND

Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC).

METHODS

Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas-pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan-Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm.

RESULTS

We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan-Meier method (p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart.

CONCLUSIONS

We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

Analysis of Methylation-driven Genes in Pancreatic Ductal Adenocarcinoma for Predicting Prognosis

Purpose: Considerable variations in methylation profile have been found in various cancers to modulate tumorigenesis and affect prognosis. To provide a theoretical basis for early detection, prognosis evaluation and targeted treatment for patients with pancreatic ductal adenocarcinoma: PDAC, this study identified methylation-driven genes in PDAC and explored their prognostic performance.

Methods: The methylation, expression and clinical data of PDAC patients were extracted from TCGA database. Based on the β-mixture model of the MethylMix R package, the differential methylation status and connection between methylation and expression degree were examined to screen out methylation-driven genes in PDAC. COX analyses and lasso regressions were applied to construct a linear risk model based on methylation-driven genes. Univariate and multivariate analyses were performed to ensure the risk model was an independent prognostic factor. Joint survival analyses of methylation and gene expression were conducted to explore the prognostic value of component genes. The methylation sites in the key genes were also investigated.

Results: A total of 118 methylation-driven genes in PDAC were identified, and two genes (FOXI2, MYEOV) constituted the risk model whose AUC was 0.722 at one year of overall survival rate, displaying a better performance on survival prediction than other clinical features. Further survival analyses demonstrated that the expression of MYEOV and combined methylation and expression levels of the genes MYEOV and FOXI2 can be potential biomarkers for survival prediction and targets of drug manipulation of PDAC patients. Close relationships were discovered between two sites in MYEOV and one site in FOXI2 and the prognosis of PDAC patients.

Conclusion: Concentrating on DNA methylation, our study identified potential biomarkers and developed a reliable short-term predictive model for prognosis of PDAC patients.

High expression of LAMA3/AC245041.2 gene pair associated with KRAS mutation and poor survival in pancreatic adenocarcinoma: a comprehensive TCGA analysis

Background

Pancreatic adenocarcinoma (PAAD) is one of the most challenging cancers with high morbidity and mortality. KRAS mutations could occur as an early event in PAAD. The present study aimed to identify the differentially expressed lncRNAs (DE-lncRNAs) and differentially expressed mRNAs (DE-mRNAs) in KRAS-mutant PAAD to explore the pathogenesis and the underlying molecular mechanism of PAAD development.

Methods

Clinical data of TCGA–PAAD patients were downloaded from the TCGA database and subjected to survival analysis along with the KRAS mutation information data. Weighted gene correlation network analysis (WGCNA) and univariate Cox regression analysis were conducted to construct prognostic risk models to identify the hub DE-mRNAs and DE-lncRNAs associated with PAAD prognosis. GO and KEGG enrichment analyses of the identified hub DE-mRNAs were performed. Multivariate cox regression analysis was performed to analyze the overall prognosis of age, gender, pathologic_T, and KRAS mutations, following which the differences in the clinical characteristics of risk score1 and risk score2 were analyzed. Finally, the mRNAs–lncRNA–TFs regulatory network was constructed.

Results

Functional enrichment analysis was performed after screening 1671 DE-mRNAs and 324 DE-lncRNAs. It was observed that the associated pathways were enriched mainly in the modulation of chemical synaptic transmission, synaptic membrane, ion-gated channel activity, ligand−receptor interactions that stimulate neural tissue, among others. The univariate Cox regression analysis screened 117 mRNAs and 36 lncRNAs, and the risk ratio models of the mRNAs and lncRNAs were constructed. LAMA3 (mRNA) and AC245041.2 (lncRNA) exhibited a strong expression correlation in the respective two risk models. The genes in the samples with a high expression of these two genes were enriched in several pathways associated with transcription factors (TFs), among which the TFs ATF5, CSHL1, NR1I2, SIPA1, HOXC13, HSF2, and HOXA10 were shared by the two groups. The core enrichment genes in the common TF pathways were collated, and the mRNAs–lncRNAs–TFs regulatory network was constructed.

Conclusion

In the present study, novel prognostic mRNAs and lncRNAs were identified, and their respective prognostic models and nomograms were constructed to guide clinical practice. An mRNAs–lncRNAs–TFs regulatory network was also constructed, which could assist further research in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00322-2.