The AP2M1 gene expression is a promising biomarker for predicting survival of patients with hepatocellular carcinoma

The AP-2 complex interacts with γ-TuRC and regulates the proliferative capacity of neural progenitors

Centrosomes are organelles that nucleate microtubules via the activity of gamma-tubulin ring complexes (γ-TuRC). In the developing brain, centrosome integrity is central to the progression of the neural progenitor cell cycle, and its loss leads to microcephaly. We show that NPCs maintain centrosome integrity via the endocytic adaptor protein complex-2 (AP-2). NPCs lacking AP-2 exhibit defects in centrosome formation and mitotic progression, accompanied by DNA damage and accumulation of p53. This function of AP-2 in regulating the proliferative capacity of NPCs is independent of its role in clathrin-mediated endocytosis and is coupled to its association with the GCP2, GCP3, and GCP4 components of γ-TuRC. We find that AP-2 maintains γ-TuRC organization and regulates centrosome function at the level of MT nucleation. Taken together, our data reveal a novel, noncanonical function of AP-2 in regulating the proliferative capacity of NPCs and open new avenues for the identification of novel therapeutic strategies for the treatment of neurodevelopmental and neurodegenerative disorders with AP-2 complex dysfunction.

Endocytosis in cancer and cancer therapy

Endocytosis is a complex process whereby cell surface proteins, lipids and fluid from the extracellular environment are packaged, sorted and internalized into cells. Endocytosis is also a mechanism of drug internalization into cells. There are multiple routes of endocytosis that determine the fate of molecules, from degradation in the lysosomes to recycling back to the plasma membrane. The overall rates of endocytosis and temporal regulation of molecules transiting through endocytic pathways are also intricately linked with signalling outcomes. This process relies on an array of factors, such as intrinsic amino acid motifs and post-translational modifications. Endocytosis is frequently disrupted in cancer. These disruptions lead to inappropriate retention of receptor tyrosine kinases on the tumour cell membrane, changes in the recycling of oncogenic molecules, defective signalling feedback loops and loss of cell polarity. In the past decade, endocytosis has emerged as a pivotal regulator of nutrient scavenging, response to and regulation of immune surveillance and tumour immune evasion, tumour metastasis and therapeutic drug delivery. This Review summarizes and integrates these advances into the understanding of endocytosis in cancer. The potential to regulate these pathways in the clinic to improve cancer therapy is also discussed.

The Impact of YRNAs on HNSCC and HPV Infection

HPV infection is one of the most important risk factors for head and neck squamous cell carcinoma among younger patients. YRNAs are short non-coding RNAs involved in DNA replication. YRNAs have been found to be dysregulated in many cancers, including head and neck squamous cell carcinoma (HNSCC). In this study, we investigated the role of YRNAs in HPV-positive HNSCC using publicly available gene expression datasets from HNSCC tissue, where expression patterns of YRNAs in HPV(+) and HPV(−) HNSCC samples significantly differed. Additionally, HNSCC cell lines were treated with YRNA1-overexpressing plasmid and RNA derived from these cell lines was used to perform a NGS analysis. Additionally, a deconvolution analysis was performed to determine YRNA1’s impact on immune cells. YRNA expression levels varied according to cancer pathological and clinical stages, and correlated with more aggressive subtypes. YRNAs were mostly associated with more advanced cancer stages in the HPV(+) group, and YRNA3 and YRNA1 expression levels were found to be correlated with more advanced clinical stages despite HPV infection status, showing that they may function as potential biomarkers of more advanced stages of the disease. YRNA5 was associated with less-advanced cancer stages in the HPV(−) group. Overall survival and progression-free survival analyses showed opposite results between the HPV groups. The expression of YRNAs, especially YRNA1, correlated with a vast number of proteins and cellular processes associated with viral infections and immunologic responses to viruses. HNSCC-derived cell lines overexpressing YRNA1 were then used to determine the correlation of YRNA1 and the expression of genes associated with HPV infections. Taken together, our results highlight the potential of YRNAs as possible HNSCC biomarkers and new molecular targets.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

High expression of AP2M1 correlates with worse prognosis by regulating immune microenvironment and drug resistance to R-CHOP in diffuse large B cell lymphoma

BACKGROUND

First-line treatment with R-CHOP has cured 50%-60% patients of diffuse large B cell lymphoma (DLBCL), and more than one-third patients will eventually progressed to relapsed/refractory disease with dismal outcomes. Adaptor Related Protein Complex 2 Subunit Mu 1 (AP2M1) is required for the activity of a vacuolar ATPase and may also play an important role in regulating the intracellular trafficking and function of CTLA-4 protein. Herein, using both public databases and our own tumor samples, we aimed to demonstrate the prognostic role of AP2M1 and the potential tumor-promoting mechanisms in DLBCL.

METHOD

Using public datasets of DLBCL from both GEO and TCGA databases, we analyzed the role of AP2M1 in mediating chemoresistance to R-CHOP and its correlation with various clinical parameters and prognosis. By using various R packages, we evaluated the role of AP2M1 on regulating tumor immune microenvironment. Moreover, tumor samples of DLBCL from Beijing TongRen Hospital were used to validate our findings by immunohistochemistry staining.

RESULT

Expression of AP2M1 was significantly increased in DLBCL, which was correlated with poor prognosis and a variety of clinical indicators. On the basis of enrichment analysis, it was found that AP2M1 may be related to intracellular receptor signaling pathway. Through immune analysis and drug prediction, we found that the expression of AP2M1 affected the immune environment and drug response of DLBCL, which further revealed the important role of AP2M1 in DLBCL. By analyzing 61 patients treated uniformly with R-CHOP regimen in our center, we validated the above findings that high expression of AP2M1 correlated with inferior survival outcomes and affected sensitivity to R-CHOP treatment.

CONCLUSION

Expression of AP2M1 may affect the prognosis of DLBCL patients probably by affecting the immune environment and the responses to many drugs in treating DLBCL, indicating AP2M1 as a potential therapy target in DLBCL.

Identification and validation of ubiquitin-proteasome system related genes as a prognostic signature for papillary renal cell carcinoma

Dysregulation of the ubiquitin-proteasome system (UPS) pathway greatly affects uncontrolled proliferation, genomic instability, and carcinogenesis, particularly in those with renal papillary cell carcinoma (PRCC). However, there is little information at the molecular level about the full link between changes in the genes involved in ubiquitin-mediated proteolysis and PRCC.

METHODS

The Cancer Genome Atlas (TCGA) and GeneCards databases were utilized to find the clinical data and gene expression patterns of patients with PRCC. Univariate Cox regression analysis and absolute shrinkage and selection operator (LASSO) analyses identified a risk signature formed by ten optimal UPS genes. The predictive value of the risk signature in TCGA-PRCC cohorts was evaluated using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. By utilizing GO enrichment and the KEGG pathway, the interactions of differentially expressed genes connected to ubiquitin-mediated proteolysis were functionally examined. The protein expression of the hub genes was affirmed using the Human Protein Atlas (HPA) database. The effectiveness of particular CDC20 and UBE2C in vitro was confirmed by experimental research.

RESULTS

Ten of the best ubiquitin-mediated proteolysis genes (UBE2C, DDB2, CBLC, BIRC3, PRKN, UBE2O, SIAH1, SKP2, UBC, and CDC20) were detected to create a risk signature. The high-risk score group stratified was associated with advanced tumor status and poor survival of PRCC patients. 10 genes were also found to be associated with the cell cycle pathway and ubiquitin-mediated proteolysis to GO and KEGG analysis. Of these 10 genes, CDC20 and UBE2C are highly expressed in tumor tissue and correlated with cancer immunity founded on the analyses of the expression of human protein atlas and TISIDB. The downregulation of UBE2C facilitated tumor inhibition and the anti-immune effect was confirmed by in vitro experiments.

CONCLUSION

Our results indicate that the risk model created from the ubiquitin-mediated proteolysis genes can be reliably and accurately predict the prognosis of PRCC patients, highlighting its targeted value for PRCC treatment. Particularly, the expression of UBE2C may be crucial for the prognosis and immunological treatment of renal cancer.

Mast cell marker gene signature in head and neck squamous cell carcinoma

BACKGROUND

Mast cells can reshape the tumour immune microenvironment and greatly affect tumour occurrence and development. However, mast cell gene prognostic and predictive value in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study was conducted to identify and establish a prognostic mast cell gene signature (MCS) for assessing the prognosis and immunotherapy response of patients with HNSCC.

METHODS

Mast cell marker genes in HNSCC were identified using single-cell RNA sequencing analysis. A dataset from The Cancer Genome Atlas was divided into a training cohort to construct the MCS model and a testing cohort to validate the model. Fluorescence in-situ hybridisation was used to evaluate the MCS model gene expression in tissue sections from patients with HNSCC who had been treated with programmed cell death-1 inhibitors and further validate the MCS.

RESULTS

A prognostic MCS comprising nine genes (KIT, RAB32, CATSPER1, SMYD3, LINC00996, SOCS1, AP2M1, LAT, and HSP90B1) was generated by comprehensively analysing clinical features and 47 mast cell-related genes. The MCS effectively distinguished survival outcomes across the training, testing, and entire cohorts as an independent prognostic factor. Furthermore, we identified patients with favourable immune cell infiltration status and immunotherapy responses. Fluorescence in-situ hybridisation supported the MCS immunotherapy response of patients with HNSCC prediction, showing increased high-risk gene expression and reduced low-risk gene expression in immunotherapy-insensitive patients.

CONCLUSIONS

Our MCS provides insight into the roles of mast cells in HNSCC prognosis and may have applications as an immunotherapy response predictive indicator in patients with HNSCC and a reference for immunotherapy decision-making.

Systematic Construction and Validation of an Immune-Related Gene-Based Model to Predict Prognosis for Ovarian Cancer

Ovarian cancer (OC) is a malignancy with poor prognosis, stubborn resistance, and frequent recurrence. Recently, it has been widely recognized that immune-related genes (IRGs) have demonstrated their indispensable importance in the occurrence and progression of OC. Given this, this study aimed to identify IRGs with predictive value and build a prognostic model for a more accurate assessment. First, we obtained transcriptome and clinical information of ovarian samples from both TCGA and GTEx databases. After integration, we figured out 10 genes as immune-related prognostic genes (IRPGs) by performing the univariate Cox regression analysis. Subsequently, we established a TF-associated network to investigate its internal mechanism. The prognosis model consisting of 5 IRPGs was constructed later by lasso regression analysis. The comparison of the score with the clinical factors validated its independence and superiority in OC's prognosis. Moreover, the association between the signature and immune cell infiltration demonstrated its ability to image the immune situation of the tumor microenvironment. Finally, the reliability of the risk model was confirmed by the GEO cohort. Together, our study has constructed an independent prognostic model for OC, which may deepen the understanding of the immune microenvironment and help present novel biomarkers or ideas for targeted therapy.

Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Based on BLCA data retrieved from the Cancer Genome Atlas and ARGs list obtained from the Human Autophagy Database website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and the aforementioned PDEARGs-based model. We found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.

Role of adaptin protein complexes in intracellular trafficking and their impact on diseases

Adaptin proteins (APs) play a crucial role in intracellular cell trafficking. The 'classical' role of APs is carried out by AP1‒3, which bind to clathrin, cargo, and accessory proteins. Accordingly, AP1-3 are crucial for both vesicle formation and sorting. All APs consist of four subunits that are indispensable for their functions. In fact, based on studies using cells, model organism knockdown/knock-out, and human variants, each subunit plays crucial roles and contributes to the specificity of each AP. These studies also revealed that the sorting and intracellular trafficking function of AP can exert varying effects on pathology by controlling features such as cell development, signal transduction related to the apoptosis and proliferation pathways in cancer cells, organelle integrity, receptor presentation, and viral infection. Although the roles and functions of AP1‒3 are relatively well studied, the functions of the less abundant and more recently identified APs, AP4 and AP5, are still to be investigated. Further studies on these APs may enable a better understanding and targeting of specific diseases.APs known or suggested locations and functions.

Downregulated Expression of CLEC9A as Novel Biomarkers for Lung Adenocarcinoma

Purpose

Abnormal CLEC9A expression is concerned with carcinogenesis. However, the role of CLEC9A in lung adenocarcinoma (LUAD) remains unknown. The goal of this study was to reveal the role of CLEC9A in LUAD based on bioinformatics and cellular functional experiments.

Materials and methods

Data available from The Cancer Genome Atlas (TCGA) were employed to study CLEC9A expression and mutations in LUAD. Expression and alterations of CLEC9A were analyzed using UALCAN and cBioPortal, respectively. Kaplan-Meier analysis was used to analyze the effect of CLEC9A on the survival of LUAD. Protein-protein interaction (PPI) network was built using GeneMANIA analysis. The similar genes of CLEC9A were obtained using GEPIA analysis, while co-expression genes correlated with CLEC9A were identified using LinkedOmics analysis. The effects of CLEC9A expression on immune cell infiltration was assessed. The effect of CLEC9A on the proliferation, apoptosis, cell cycle distribution, and invasion of human LUAD cells was detected in the LUAD cell line.

Results

CLEC9A was downregulated and the CLEC9A gene was often altered in LUAD. The survival of LUAD patients was correlated with the expression level of CLEC9A. The similar genes of CLEC9A were linked to functional networks involving positive regulation of interleukin-12 production, plasma membrane and CD40 receptor binding, primary immunodeficiency, intestinal immune network for IgA production, and cell adhesion molecules pathways. Cell cycle, apoptosis, EMT, and RAS/MAPK were significantly enriched pathways in positive and negative correlation genes with CLEC9A. A difference in the immune infiltration level of immune cell between the high and low CLEC9A expression groups was observed. Somatic cell copy number alternations (CNAs) of the CLEC9A, including arm-level gain and arm-level deletion, observably changed the infiltration levels of B cells, CD4+ T cells, macrophages, and neutrophils in LUAD. Except for LAG3, the expression of CD274, CTLA4, PDCD1, and TIGIT was positively correlated with the expression level of CLEC9A. After transfection, overexpression and knockdown of CLEC9A could affect the proliferation, apoptosis, cell cycle distribution, and invasion of LUAD cells.

Conclusion

CLEC9A is associated with prognosis and tumor immune microenvironment of LUAD, suggesting that CLEC9A may be considered as a novel biomarker for LUAD.

Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

Background

Prostate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.

Methods

Data were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.

Results

The weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.

Conclusion

In summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

A New Prognostic Risk Signature of Eight Ferroptosis-Related Genes in the Clear Cell Renal Cell Carcinoma

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

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) is the most common malignancy in the urinary system. Despite substantial improvements in available treatment options, the survival outcome of advanced RCC is unsatisfactory. Identifying novel biomarkers to assist in early diagnosis and to screen patients who are sensitive to immunotherapy would be beneficial. CD248 is a promising candidate that deserves to be investigated.

Methods: The Cancer Genome Atlas (TCGA) data set and clinical specimens were adopted to analyze the expression of CD248 between normal and tumor tissues. Univariate and multivariate Cox regression analyses were employed to identify independent prognostic factors and construct a CD248-based prognostic signature. The correlation among the present signature, tumor-infiltrating immune cells (TIICs), the tumor mutation burden (TMB), and immunomodulatory molecules was evaluated. The weighted gene co-expression network analysis (WGCNA), the enrichment analysis, and the miRNA correlation analysis were performed to explore the underlying mechanism of CD248 in the progression of RCC.

Results: The overexpression of CD248 in RCC was related to a poor prognosis, and a CD248-based prognostic signature could precisely stratify patients with RCC with different survival outcomes regardless of the training or testing cohort. The present signature could reflect the immunosuppressive landscape of RCC (i.e., increased infiltration of regulatory T cells and upregulated immune checkpoints), accompanied by deteriorated clinicopathologic indexes. The TMB and immunostimulatory molecules expression also increased with the risk score generated from the present signature. CD248 co-expressed gene sets were identified through the WGCNA algorithm, and several immunosuppressive Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched. The result of CD248-correlated miRNA further emphasized the importance of CD248 in RCC.

Conclusion: CD248 is a valuable biomarker to improve the diagnostic and therapeutic efficiency of RCC. The immunosuppressive effect of CD248 co-expressed genes may provide insight for the present study, and miRNA would help to reveal the mechanism of the expressive regulation of CD248.

Dysregulation of miR-138-5p/RPS6KA1-AP2M1 Is Associated With Poor Prognosis in AML

Acute myeloid leukemia (AML) is a malignant disease of hematopoietic stem/progenitor cells, and most AML patients are in a severe state. Internal tandem duplication mutations in FLT3 gene (FLT3-ITD) detected in AML stem cells account for 20–30 percent of AML patients. In this study, we attempted to study the impact of the interaction of FLT3-ITD mutation and the CXCL12/CXCR4 axis in AML, and the possible mechanisms caused by the impact by bioinformatics. Gene set variation analysis (GSVA) revealed that the PI3K-Akt-mTOR pathway positively correlated with the status of FLT3-ITD mutation. Multiple survival analyses were performed on TCGA-AML to screen the prognostic-related genes, and RPS6KA1 and AP2M1 are powerful prognostic candidates for overall survival in AML. WGCNA, KEGG/GO analysis, and the functional roles of RPS6KA1 and AP2M1 in AML were clarified by correlation analysis. We found that the expression levels of RPS6KA1 and AP2M1 were significantly associated with chemoresistance of AML, and the CXCL12/CXCR4 axis would regulate RPS6KA1/AP2M1 expression. Besides, miR-138-5p, regulated by the CXCL12/CXCR4 axis, was the common miRNA target of RPS6KA1 and AP2M1. Taken together, the interaction of FLT3-ITD mutation and the CXCL12/CXCR4 axis activated the PI3K-Akt-mTOR pathway, and the increased expression of RPS6KA1 and AP2M1 caused by hsa-miR-138-5p downregulation regulates the multi-resistance gene expression leading to drug indications.

Identification and validation of an autophagy-related long non-coding RNA signature as a prognostic biomarker for patients with lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most predominant pathological subtype of lung cancer, accounting for 40–70% of all lung cancer cases. Although significant improvements have been made in the screening, diagnosis, and precise management in recent years, the prognosis of LUAD remains bleak. This study aimed to investigate the prognostic significance of autophagy-related long non-coding RNAs (lncRNAs) and construct an autophagy-related lncRNA prognostic model in LUAD.

Methods

The gene expression data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. All autophagy-related genes were downloaded from the Human Autophagy Database (HADb). Spearman’s correlation test was exploited to identify potential autophagy-related lncRNAs. The multivariate Cox regression analysis was used to construct the prognostic signature, which divided LUAD patients into high-risk and low-risk groups. Subsequently, the receiver operating characteristic (ROC) curves were generated to assess the predictive ability of this prognostic model for overall survival (OS) in these individuals. Then, the Gene set enrichment analysis (GSEA) was conducted to execute pathway enrichment analysis. Finally, a multidimensional validation was exploited to verify our findings.

Results

A total of 1,144 autophagy-related lncRNAs were identified to construct the co-expression network via Spearman’s correlation test (|R²| >0.4 and P≤0.001). Ultimately, a 16 autophagy-related lncRNAs prognostic model was constructed, and the area under the ROC curve (AUC) was 0.775. The results of GSEA enrichment analysis showed that the genes in the high-risk group were mainly enriched in cell cycle and p53 signaling pathways. The results of the multidimensional database validation indicated that the expression level of BIRC5 was significantly correlated with the expression level of TMPO-AS1. Furthermore, both TMPO-AS1 and BIRC5 had a higher expression level in LUAD samples. LUAD patients with high expression levels of TMPO-AS1 and BIRC5 were correlated with advanced disease stage and poor OS.

Conclusions

In summary, our results suggested that the prognostic signature of the 16 autophagy-related lncRNAs has significant prognostic value for LUAD patients. Furthermore, TMPO-AS1 and BIRC5 are potential predictors and therapeutic targets in these individuals.

The Construction and Analysis of Tumor-Infiltrating Immune Cells and ceRNA Networks in Bladder Cancer

Background

Bladder cancer (BLCA) is the 11th most common malignancy worldwide. Although significant improvements have been made in screening, diagnosis, and precise management in recent years, the prognosis of BLCA remains bleak.

Objectives

This study aimed to investigate the prognostic significance of tumor-infiltrating immune cells and construct ceRNA networks in BLCA patients.

Methods

The expression data of BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A competing endogenous RNA (ceRNA) network was constructed to identify the hub genes involved in the prognosis of BLCA. The CIBERSORT algorithm was utilized to investigate the infiltration levels of 22 subsets of immune cells. Ultimately, the nomogram was generated to visualize the survival probability of each patient, with the calibration curve being performed to assess its performance. Furthermore, the Pearson correlation test was used to explore the correlation between the identified hub genes in the ceRNA network and the prognostic-related immune cells.

Results

A total of eight elements in the ceRNA network were considered as key members and correlated with the prognosis of BLCA, including ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p. T cells CD8, T cells follicular helper (Tfh), and neutrophils were identified as independent prognostic factors in BLCA. The co-expression analysis showed that there was a significant correlation between the identified hub genes and immune cells.

Conclusion

Our results suggest that the mechanism of hsa-miR-29c-3p regulates the expression of ELN and DSC2, and the infiltration of Tfh and neutrophils might play pivotal roles in the progression of BLCA.

Tumor Mutation Burden, Immune Cell Infiltration, and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer

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

Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

Pancreatic cancer is known as "the king of cancer," and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

Identification of an Immune-Related Prognostic Predictor in Hepatocellular Carcinoma

Liver hepatocellular carcinoma (LIHC) is the most prevalent primary cancer of the liver, and immune-related genes (IRGs) regulate its development. So far, there is still no precise biomarker that predicts response to immunotherapy in LIHC. Therefore, this research seeks to identify immunogenic prognostic biomarkers and explore potential predictors for the efficacy of anti-PD-1/PD-L1 therapies in LIHC. The clinical data and gene expression profiles of patients diagnosed with LIHC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Moreover, IRGs were obtained from the ImmPort database. We discovered 35 IRGs that were differentially expressed between LIHC tissues and corresponding normal tissues. Through univariate Cox regression analysis, eight prognostic differentially expressed IRGs (PDEIRGs) were identified. Further, three optimal PDEIRGs (BIRC5, LPA, and ROBO1) were identified and used to construct a prognostic risk signature of LIHC patients via multivariate Cox regression analysis. The signature was validated by ROC curves. Subsequently, based on gene set enrichment analysis (GSEA) analysis, two out of the three optimal PDEIRGs (BIRC5 and LPA) were significantly enriched in the mismatch repair (MMR) pathway. Moreover, the two PDEIRGs (BIRC5 and LPA) were significantly correlated with the expression of genes related to mismatch repair (MLH1, MSH2, MSH6, and PMS2). Furthermore, correlations between the two PDEIRGs (BIRC5 and LPA) and immune checkpoints of cancer treatment (such as CTLA4, PD-1, and PD-L1) were demonstrated. Hyperprogressive disease (HPD) is a novel pattern of tumor progression which has a close relationship with immune checkpoint inhibitors (ICIs) utilization. MDM2 family amplification might promote the HPD phenomenon. Finally, we found a positive regulatory relationship between HPD related gene (MDM2) and BIRC5. Notably, MDM2 can either interact directly with BIRC5 or indirectly via downstream transcription factors of BIRC5. Overall, our study uncovered a novel 3-immune-related prognostic genes in LIHC.

Alantolactone inhibits cell autophagy and promotes apoptosis via AP2M1 in acute lymphoblastic leukemia

Background

Acute lymphoblastic leukemia (ALL) is an aggressive hematopoietic malignancy that is most commonly observed in children. Alantolactone (ALT) has been reported to exhibit anti-tumor activity in different types of cancer. The aim of the present study was to investigate the anti-tumor activity and molecular mechanism of ALT in ALL.

Methods

ALL cell lines were treated with 1, 5 and 10 μM ALT, and cell viability was assessed using an MTT assay and RNA sequencing. Flow cytometry, JC-1 staining and immunofluorescence staining assays were used to measure cell apoptosis and autophagy. Additionally, western blot analysis was used to detect expression of apoptosis and autophagy related proteins. Finally, the effects of ALT on tumor growth were assessed in a BV173 xenograft nude mouse model.

Results

ALT inhibited the proliferation of ALL cells in a dose-dependent manner. Additionally, it was demonstrated that ALT inhibited cell proliferation, colony formation, autophagy, induced apoptosis and reduced tumor growth in vivo through upregulating the expression of adaptor related protein complex 2 subunit mu 1 (AP2M1). Moreover, the autophagy activator rapamycin, attenuated the pro-apoptotic effects of ALT on BV173 and NALM6 cell lines. Overexpression of AP2M1 decreased the expression of Beclin1 and the LC3-II/LC3-1 ratio, and increased p62 expression. Knockdown of Beclin1 increased the levels of bax, cleaved caspase 3 and cytochrome C, and decreased bcl-2 expression.

Conclusions

The present study demonstrated that ALT exerts anti-tumor activity through inducing apoptosis and inhibiting autophagy by upregulating AP2M1 in ALL, highlighting a potential therapeutic strategy for treatment of ALL.

Development of an immune-related prognostic model for pediatric acute lymphoblastic leukemia patients

Background

Acute lymphoblastic leukemia (ALL) is the most common hematological malignancy in pediatrics, and immune‐related genes (IRGs) play crucial role in its development. Our study aimed to identify prognostic immune biomarkers of pediatric ALL and construct a risk assessment model.

Methods

Pediatric ALL patients’ gene expression data were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We screened differentially expressed IRGs (DEIRGs) between the relapse and non‐relapse groups. Cox regression analysis was used to identify optimal prognostic genes, then, a risk model was constructed, and its accuracy was verified in different cohorts.

Results

We screened 130 DEIRGs from 251 pediatric ALL samples. The top three pathways that DEIRGs may influence tumor progression are NABA matrisome‐associated, chemotaxis, and antimicrobial humoral response. A set of 84 prognostic DEIRGs was identified by using univariate Cox analysis. Then, Lasso regression and multivariate Cox regression analysis screened four optimal genes (PRDX2, S100A10, RORB, and SDC1), which were used to construct the prognostic risk model. The risk score was calculated and the survival analysis results showed that high‐risk score was associated with poor overall survival (OS) (p = 3.195 × 10⁻⁷). The time‐dependent survival receiver operating characteristic curves showed good prediction accuracy (Area Under Curves for 3‐year, 5‐year OS were 0.892 and 0.89, respectively). And the predictive performance of our risk model was successfully verified in testing cohort and entire cohort.

Conclusions

Our prognostic risk model can effectively divide pediatric ALL patients into high‐risk and low‐risk groups, which may help predict clinical prognosis and optimize individualized treatment.

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma

Cholangiocarcinoma remained a severe threat to human health. Deciphering the genomic and/or transcriptomic profiles of tumor has been proved to be a promising strategy for exploring the mechanism of tumorigenesis and development, which could also provide valuable insights into Cholangiocarcinoma. However, little knowledge has been obtained regarding to how the alteration among different omics levels is connected. Here, using whole exome sequencing and transcriptome sequencing, we performed a thorough evaluation for the landscape of genome and transcriptome in cholangiocarcinoma and illustrate the alteration of tumor on different biological levels. Meanwhile, we also identified the clonal structure of each included tumor sample and discovered different clonal evolution patterns related to patients’ survival. Furthermore, we extracted subnetworks that were greatly influenced by tumor clonal/subclonal mutations or transcriptome change. The topology relationship between genes affected by genomic/transcriptomic changes in biological interaction networks revealed that alteration of genome and transcriptome was highly correlated, and somatic mutations located on important genes might affect the expression of numerous genes in close range.

Prognostic Role of Zinc Finger Homeobox 4 in Ovarian Serous Cystadenocarcinoma

Introduction: The zinc finger homeobox 4 (ZFHX4) protein is a crucial molecular regulator of tumor-initiating stem cell-like functions. Objective: This study aimed to determine the role of ZFHX4 in the progression of ovarian serous cystadenocarcinoma (OSC). Methods: Differential gene expression ZFHX4 among low-stage (stages I and II), high-stage (stages III and IV), low-grade (grades I and II), and high-grade (grades III and IV) OSC patients was identified using four independent cohorts from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We compared ZFHX4 expression as a prognostic factor using Kaplan-Meier survival curves, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristics at 4 years post diagnosis. Results: ZFHX4 gene expression in high-stage tumors is significantly higher than in low-stage tumors (TCGA, p = 0.007; GSE9891, p = 0.001). A Kaplan-Meier analysis revealed that elevated expression of ZFHX4 was associated with a poor prognosis in OSC patients for all cohorts, regardless of stage and grade (TCGA, p = 1e-04; GSE9891, p = 0.0044; GSE13876, p = 0.00078; GSE26712, p = 0.039). Analysis of C-indices and the area under the receiver operating characteristic curve further supported this result (C-index: TCGA, 0.599; GSE9891, 0.642; GSE13876, 0.585; GSE26712, 0.597). Moreover, univariate and multivariate Cox hazards analyses confirmed the prognostic significance of ZFHX4 levels. Conclusion: Collectively, these findings suggest that ZFHX4 is a prognostic factor for OSC.

Prognostic value of immune-related genes in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma, and immune-related genes (IRGs) are key contributors to its development. In this study, the gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database and the cBioPortal database, respectively. IRGs were obtained from the ImmPort database. We analyzed the expression of IRGs in ccRCC, and discovered 681 that were differentially expressed between ccRCC and normal kidney tissues. Univariate Cox regression analysis was used to identify prognostic differentially expressed IRGs (PDEIRGs). Using Lasso regression and multivariate Cox regression analyses, we detected seven optimal PDEIRGs (PLAU, ISG15, IRF9, ARG2, RNASE2, SEMA3G and UCN) and used them to construct a risk model to predict the prognosis of ccRCC patients. This model accurately stratified patients with different survival outcomes and precisely identified patients with different mutation burdens. Our findings suggest the seven PDEIRGs identified in this study are valuable prognostic predictors in ccRCC patients. These genes could be used to investigate the developmental mechanisms of ccRCC and to design individualized treatments for ccRCC patients.

GABRQ expression is a potential prognostic marker for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Novel biomarkers of ccRCC may provide crucial information on tumor features and prognosis. The present study aimed to determine whether the expression of γ-aminobutyric acid (GABA) A receptor subunit θ (GABRQ) could serve as a novel prognostic marker of ccRCC. GABA is the main inhibitory neurotransmitter in the brain that activates the receptor GABA_A, which is comprised of three subunit isoforms: GABRA3, GABRB3 and GABRQ. A recent study reported that GABRQ is involved in the initiation and progression of hepatocellular carcinoma; however, the role of GABRQ in ccRCC remains unknown. In the present study, clinical and transcriptomic data were obtained from cohorts of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential GABRQ expression levels among early (TI and II), late (TIII and IV), nonmetastatic (M0) and metastatic (M1, primary tumor) stages of ccRCC samples were then identified. Furthermore, the use of GABRQ as a prognostic gene was analyzed using Uno's C-index based on the time-dependent area under the curve (AUC), the AUC of the receiver operating characteristic curve at 5 years, the Kaplan-Meier survival curve and multivariate analysis. The survival curve analysis revealed that low GABRQ mRNA expression was significantly associated with a poor prognosis of ccRCC (P<0.001 and P=0.0012 for TCGA and ICGC data, respectively). In addition, analyses of the C-index and AUC values further supported this discriminatory power. Furthermore, the prognostic value of GABRQ mRNA expression was confirmed by multivariate Cox regression analysis. Taken together, these results suggested that GABRQ mRNA expression may be considered as a novel prognostic biomarker of ccRCC.

Prognostic role of the beta-2 adrenergic receptor in clear cell renal cell carcinoma

The beta-2 adrenergic receptor (ADRB2) regulates the proliferation, apoptosis, angiogenesis, migration, and metastasis of cancer cells. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that ADRB2 can be a novel prognostic factor for patients with ccRCC. The differential expression of ADRB2 in low-stage (stages I and II), high-stage (stages III and IV), low-grade (grades I and II), and high-grade (grades III and IV) ccRCC was identified in cohorts of patients from The Cancer Genome Atlas and the International Cancer Genome Consortium. We evaluated ADRB2 expression as a prognostic factor using the Kaplan-Meier survival curve, multivariate analysis, time-dependent area under the curve (AUC) of Uno’s C-index, and AUC of the receiver operating characteristics (ROC) at five years. Kaplan-Meier analysis revealed that reduced ADRB2 expression is associated with poor prognosis in ccRCC patients. Analysis of C-indices and AUC-ROC further confirmed this result. Moreover, multivariate analysis confirmed the prognostic significance of ADRB2 expression. Collectively, these findings suggest that ADRB2 is a potential prognostic factor for ccRCC.

DYSF expression in clear cell renal cell carcinoma: A retrospective study of 2 independent cohorts

OBJECTIVE

Renal cell carcinoma (RCC) is the most typical type of kidney cancer in adults. Hypercalcemia is a well known paraneoplastic syndrome associated with RCC and recent studies have reported that hypercalcemia is closely related to the poor prognosis of RCC patients. Clear cell RCC (ccRCC) is the most common and aggressive subtype of RCC. Although the histological classification of RCC is important for determination of appropriate treatment strategies, effective biomarkers for predicting prognosis of ccRCC have not yet been identified. Since calcium levels affect the prognosis of RCC patients, we evaluated whether the calcium-sensing genes on the plasma membrane, including those encoding calcium channels, CaSR, GPRC6a, and DYSF, could be used as biomarkers to predict the prognosis of ccRCC patients.

METHODS

Information from 537 patients from The Cancer Genome Atlas (TCGA; n = 446) and International Cancer Genome Consortium (ICGC; n = 91) was used in this study. Among these genes, DYSF was the only gene whose expression correlated with overall survival of both TGCA and ICGC patients.

RESULTS

Although DYSF gene expression was higher in ccRCC tissue than in normal kidney tissue, Kaplan-Meier curves showed that the survival rate of ccRCC patients with high DYSF expression was significantly higher than that of patients with low DYSF expression (TCGA, P < 0.0001; ICGC, P = 0.0011). We also validated the potential of DYSF as a prognostic biomarker for ccRCC by conducting a time-dependent area under the curve (AUC) analysis and 5-years receiver operating characteristic curve analysis. Finally, multivariate regression analysis revealed that the expression of DYSF is independent of other prognostic parameters (TCGA, P = 0.017; ICGC, P = 0.006).

CONCLUSIONS

These results suggested that DYSF may play a suppressive role in the progression of ccRCC and could act as a promising prognostic biomarker for predicting the survival of ccRCC patients.

Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co-expression network analysis

Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co-expression network analysis (WGCNA). Co-expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein-protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan-Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large-scale cohort studies to validate further.

Prognostic Role of TMED3 in Clear Cell Renal Cell Carcinoma: A Retrospective Multi-Cohort Analysis

Transmembrane p24 trafficking protein 3 (TMED3) is a metastatic suppressor in colon cancer and hepatocellular carcinoma. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that TMED3 could be a new prognostic marker for ccRCC. Patient data were extracted from cohorts in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Differential expression of TMED3 was observed between the low stage (Stage I and II) and high stage (Stage III and IV) patients in the TCGA and ICGC cohorts and between the low grade (Grade I and II) and high grade (Grade III and IV) patients in the TCGA cohort. Further, we evaluated TMED3 expression as a prognostic gene using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno’s C-index, and the AUC of the receiver operating characteristics at 5 years. The Kaplan-Meier analysis revealed that TMED3 overexpression was associated with poor prognosis for ccRCC patients. Analysis of the C-indices and area under the receiver operating characteristic curve further supported this. Multivariate analysis confirmed the prognostic significance of TMED3 expression levels (P = 0.005 and 0.006 for TCGA and ICGC, respectively). Taken together, these findings demonstrate that TMED3 is a potential prognostic factor for ccRCC.

Prognostic significance of EIF4G1 in patients with pancreatic ductal adenocarcinoma

Background

Advances in genomics have greatly improved the survival rate in cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early, and its survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients using independent cohort data.

Materials and methods

To develop a novel prognostic biomarker, we used the gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Kaplan-Meier survival curve using median values of genes as cutoff showed that EIF4G1 was the only statistically significant gene in the 3 cohorts. We analyzed the prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate Cox analysis. We also compared EIF4G1 levels between tumors and matched non-tumor tissues.

Results

EIF4G1 is the only prognostic gene in patients with PDAC, which was selected by Kaplan-Meier survival analysis. The survival curve showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with a good discriminative ability in 3 independent cohorts. The risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate Cox regression confirmed its prognostic significance. EIF4G1 expression was significantly higher in PDAC tissues than in the matched normal tissues.

Conclusion

EIF4G1 could be used as a novel prognostic marker for PDAC and to determine suitable treatment options.

Evaluation of the prognostic significances of γ-secretase genes in pancreatic cancer

With the growing requirement for novel prognostic biomarkers for pancreatic cancer, many studies have focused on clinical and/or genomic variables. Although many studies have been performed, carbohydrate antigen 19-9 is the only biomarker in clinical use. Therefore, the present study examined whether γ-secretase genes, including presenilin (PSEN), nicastrin (NCSTN), presenilin enhancer protein 2 (PSENEN), and anterior pharynx-defective 1 (APH1-), could serve as prognostic factors for pancreatic cancer. The cohorts selected included >100 pancreatic cancer patients. Patient data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GSE21501). The prognostic roles of the γ-secretase genes were analyzed by several survival analysis methods. Among the γ-secretase genes, the prognosis tended to be worse in the 2 cohorts with increasing expression of PSEN1, APH1A, and PSENEN, while the remaining genes were the opposite in the 2 cohorts. Notably, although the patient characteristics were quite different, APH1A was statistically significantly associated with prognosis in the 2 cohorts. The hazard ratio of APH1A for overall survival was 1.598 (TCGA) and 2.724 (GSE21501). These results contribute to the study of γ-secretase in pancreatic cancer. We believe that γ-secretase, particularly APH1A, will be a new prognostic biomarker for pancreatic cancer.

VNN3 is a potential novel biomarker for predicting prognosis in clear cell renal cell carcinoma

Although pathological observations provide approximate prognoses, it is difficult to achieve prognosis in patients with existing prognostic factors. Therefore, it is very important to find appropriate biomarkers to achieve accurate cancer prognosis. Renal cell carcinoma (RCC) has several subtypes, the discrimination of which is crucial for proper treatment. Here, we present a novel biomarker, VNN3, which is used to prognose clear cell renal cell carcinoma (ccRCC), the most common and aggressive subtype of kidney cancer. Patient information analyzed in our study was extracted from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. VNN3 expression was considerably higher in stages III and IV than in stages I and II. Moreover, Kaplan–Meier curves associated high VNN3 expression with poor prognoses (TCGA, p < .0001; ICGC, p = .00076), confirming that ccRCC prognosis can be predicted via VNN3 expression patterns. Consistent with all patient results, the prognosis of patients with higher VNN3 expression was worse in both low stage (I and II) and high stage (III and IV) (TCGA, p < 0.0001 in stage I and II; ICGC, p = 0.028 in stage I and II; TCGA, p = 0.005 in stage III and IV). Area under the curve and receiver operating characteristic curves supported our results that highlighted VNN3 expression as a suitable ccRCC biomarker. Multivariate analysis also verified the prognostic performance of VNN3 expression (TCGA, p < .001; ICGC, p = .017). Altogether, we suggest that VNN3 is applicable as a new biomarker to establish prognosis in patients with ccRCC.