HOPS/TMUB1 Enhances Apoptosis in TP53 Mutation-Independent Setting in Human Cancers.

TP53 mutations are prevalent in various cancers, yet the complexity of apoptotic pathway deregulation suggests the involvement of additional factors. HOPS/TMUB1 is known to extend the half-life of p53 under normal and stress conditions, implying a regulatory function. This study investigates, for the first time, the potential modulatory role of the ubiquitin-like-protein HOPS/TMUB1 in p53-mutants. A comprehensive analysis of apoptosis in the most frequent p53-mutants, R175, R248, and R273, in SKBR3, MIA PaCa2, and H1975 cells indicates that the overexpression of HOPS induces apoptosis at least equivalent to that caused by DNA damage. Immunoprecipitation assays confirm HOPS binding to p53-mutant forms. The interaction of HOPS/TMUB1 with p53-mutants strengthens its effect on the apoptotic cascade, showing a context-dependent gain or loss of function. Gene expression analysis of the MYC and TP63 genes shows that H1975 exhibit a gain-of-function profile, while SKBR3 promote apoptosis in a TP63-dependent manner. The TCGA data further corroborate HOPS/TMUB1's positive correlation with apoptotic genes BAX, BBC3, and NOXA1, underscoring its relevance in patient samples. Notably, singular TP53 mutations inadequately explain pathway dysregulation, emphasizing the need to explore additional contributing factors. These findings illuminate the intricate interplay among TP53 mutations, HOPS/TMUB1, and apoptotic pathways, providing valuable insights for targeted cancer interventions.

rPAC: Route based pathway analysis for cohorts of gene expression data sets.

Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, "Proportion of Significance" (PS) and "Average Route Score" (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC's scoring competency, we first used a large amount of simulated data and compared the method's performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.

Identification of Core Genes Involved in the Progression of Cervical Cancer Using an Integrative mRNA Analysis.

In spite of being a preventable disease, cervical cancer (CC) remains at high incidence, and it has a significant mortality rate. Although hijacking of the host cellular pathway is fundamental for developing a better understanding of the human papillomavirus (HPV) pathogenesis, a major obstacle is identifying the central molecular targets involved in HPV-driven CC. The aim of this study is to investigate transcriptomic patterns of HPV-infected and normal tissues to identify novel prognostic markers. Analyses of functional enrichment and interaction networks reveal that altered genes are mainly involved in cell cycle, DNA damage, and regulated cell-to-cell signaling. Analysis of The Cancer Genome Atlas (TCGA) data has suggested that patients with unfavorable prognostics are more likely to have DNA repair defects attributed, in most cases, to the presence of HPV. However, further studies are needed to fully unravel the molecular mechanisms of such genes involved in CC.

Selection of three miRNA signatures with prognostic value in non-M3 acute myeloid leukemia.

BACKGROUND: MiRNAs that are potential biomarkers for predicting prognosis for acute myeloid leukemia (AML) have been identified. However, comprehensive analyses investigating the association between miRNA expression profiles and AML survival remain relatively deficient. METHOD: In the present study, we performed multivariate Cox's analysis and principal component analysis (PCA) using data from The Cancer Genome Atlas (TCGA) to identify potential molecular signatures for predicting non-M3 AML prognosis. RESULT: We found that patients who were still living were significantly younger at diagnosis than those who had died (P = 0.001). In addition, there was a marked difference in living status among different risk category groups (P = 0.022). A multivariate Cox model suggested that three miRNAs were potential biomarkers of non-M3 AML prognosis, including miR-181a-2, miR-25 and miR-362. Subsequently, PCA analyses were conducted to comprehensively represent the expression levels of these three miRNAs in each patient with a PCA value. According to the log-rank test, AML outcome for patients with lower PCA values was significantly different from those with higher PCA values (P < 0.001). Further bioinformatic analysis revealed the biological functions of the selected miRNAs. CONCLUSION: We conducted a comprehensive analysis of TCGA non-M3 AML data, identifying three miRNAs that are significantly correlated with AML survival. PCA values for the identified miRNAs are valuable for predicting AML prognosis.

Upregulation of the adipokine genes ADIPOR1 and SPP1 is related to poor survival outcomes in colorectal cancer.

BACKGROUND: Obesity is closely associated with colorectal cancer (CRC), but the underlying mechanism is unclear. We thus evaluated the expression of the adipokine gene family in CRC tissues and its clinicopathological implications. METHODS: Correlations between the mRNA expression levels of the adipokine gene family (ADIPOQ, ADIPOR1/2, LEP, LEPR, RETN, RETNLB, RBP4, SFRP5, NAMPT, and SPP1) in CRC tissue and clinicopathologic factors were analyzed using data from The Cancer Genome Atlas database. RESULTS: Tissue samples from 369 patients were analyzed, and 82 deaths occurred during follow-up (median, 670 days). Overall, mortality was associated with positive venous invasion, higher TNM stage, and increased ADIPOR1 (adiponectin receptor 1 gene) and SPP1 (secreted phosphoprotein gene 1) mRNA expression. Higher ADIPOR1 (odds ratio [OR]: 3.29, 95% confidence interval [CI]: 1.33-8.13) and SPP1 (OR: 2.31, 95%CI: 1.49-3.59) levels were independently associated with increased mortality. A Kaplan-Meier survival analysis showed shorter overall survival times in patients with higher ADIPOR1 (P = 0.006) and SPP1 (P < 0.001) expression. CONCLUSIONS: Upregulation of ADIPOR1 and SPP1, among the adipokine gene family, in cancer tissue is associated with poor survival in CRC, suggesting a potential mechanism linking obesity and CRC. ADIPOR1 and SPP1 expression could become useful prognostic indicators after further validation.

PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas.

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein⁻protein, protein⁻RNA, or protein⁻DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

Pan-Cancer Mutational and Transcriptional Analysis of the Integrator Complex.

The integrator complex has been recently identified as a key regulator of RNA Polymerase II-mediated transcription, with many functions including the processing of small nuclear RNAs, the pause-release and elongation of polymerase during the transcription of protein coding genes, and the biogenesis of enhancer derived transcripts. Moreover, some of its components also play a role in genome maintenance. Thus, it is reasonable to hypothesize that their functional impairment or altered expression can contribute to malignancies. Indeed, several studies have described the mutations or transcriptional alteration of some Integrator genes in different cancers. Here, to draw a comprehensive pan-cancer picture of the genomic and transcriptomic alterations for the members of the complex, we reanalyzed public data from The Cancer Genome Atlas. Somatic mutations affecting Integrator subunit genes and their transcriptional profiles have been investigated in about 11,000 patients and 31 tumor types. A general heterogeneity in the mutation frequencies was observed, mostly depending on tumor type. Despite the fact that we could not establish them as cancer drivers, INTS7 and INTS8 genes were highly mutated in specific cancers. A transcriptome analysis of paired (normal and tumor) samples revealed that the transcription of INTS7, INTS8, and INTS13 is significantly altered in several cancers. Experimental validation performed on primary tumors confirmed these findings.

NCDN is a Potential Biomarker and Therapeutic Target for Glioblastoma.

Background: Glioblastoma (GBM) is a type of central nervous system malignancy. In our study, we determined the effect of NCDN in GBM patients through The Cancer Genome Atlas (TCGA) data analysis, and studied the effects of NCDN on GBM cell function to estimate its potential as a therapeutic target. Methods: Gene expression profiles of glioblastoma cohort were acquired from TCGA database and analyzed to look for central genes that may serve as GBM therapeutic targets. Then the cell function of NCDN in glioblastoma cell was explored through in vitro cell experiments. Results: Through gene ontology (GO) analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis, we identified three key genes (NCDN, PAK1 and SPRYD3) associated with poor prognosis in glioblastoma. In vitro experiments showed impaired cell migration, apoptosis, and cell cycle arrest in NCDN knockdown cells. Conclusion: NCDN affects the progress and prognosis of glioblastoma by promoting cell migration and inhibiting apoptosis.

Uncovering miRNA-mRNA Regulatory Networks Related to Olaparib Resistance and Resensitization of BRCA2MUT Ovarian Cancer PEO1-OR Cells with the ATR/CHK1 Pathway Inhibitors.

Resistance to olaparib is the major obstacle in targeted therapy for ovarian cancer (OC) with poly(ADP-ribose) polymerase inhibitors (PARPis), prompting studies on novel combination therapies to enhance olaparib efficacy. Despite identifying various mechanisms, understanding how OC cells acquire PARPi resistance remains incomplete. This study investigated microRNA (miRNA) expression in olaparib-sensitive (PEO1, PEO4) and previously established olaparib-resistant OC cell lines (PEO1-OR) using high-throughput RT-qPCR and bioinformatic analyses. The role of miRNAs was explored regarding acquired resistance and resensitization with the ATR/CHK1 pathway inhibitors. Differentially expressed miRNAs were used to construct miRNA-mRNA regulatory networks and perform functional enrichment analyses for target genes with miRNet 2.0. TCGA-OV dataset was analyzed to explore the prognostic value of selected miRNAs and target genes in clinical samples. We identified potential processes associated with olaparib resistance, including cell proliferation, migration, cell cycle, and growth factor signaling. Resensitized PEO1-OR cells were enriched in growth factor signaling via PDGF, EGFR, FGFR1, VEGFR2, and TGFßR, regulation of the cell cycle via the G2/M checkpoint, and caspase-mediated apoptosis. Antibody microarray analysis confirmed dysregulated growth factor expression. The addition of the ATR/CHK1 pathway inhibitors to olaparib downregulated FGF4, FGF6, NT-4, PLGF, and TGFß1 exclusively in PEO1-OR cells. Survival and differential expression analyses for serous OC patients revealed prognostic miRNAs likely associated with olaparib resistance (miR-99b-5p, miR-424-3p, and miR-505-5p) and resensitization to olaparib (miR-324-5p and miR-424-3p). Essential miRNA-mRNA interactions were reconstructed based on prognostic miRNAs and target genes. In conclusion, our data highlight distinct miRNA profiles in olaparib-sensitive and olaparib-resistant cells, offering molecular insights into overcoming resistance with the ATR/CHK1 inhibitors in OC. Moreover, some miRNAs might serve as potential predictive signature molecules of resistance and therapeutic response.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery.

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

Anticancer Effect of E26 Transformation-Specific Homologous Factor through the Induction of Senescence and the Inhibition of Epithelial-Mesenchymal Transition in Triple-Negative Breast Cancer Cells.

The aim of the present study was to evaluate the effect of ETS homologous factor (EHF) in malignant breast cancer cells. The overexpression and knockdown of the EHF gene in human and mouse breast cancer cells were performed, and the TCGA dataset and Q-omics were analyzed. We found that the tumor suppressor NDRG2 is correlated with EHF gene expression in triple-negative breast cancer cells, that EHF overexpression results in reduced cell proliferation and that apoptosis is promoted by the chemotherapeutic reagent treatment of EHF-overexpressing cells. By EHF overexpression, senescence-associated ß-galactosidase activity and p21WAF1/CIP1 expression were increased, suggesting that EHF may induce cellular senescence. In addition, the overexpression of EHF reduced the migratory ability and inhibited epithelial-mesenchymal transition (EMT). Furthermore, EHF inhibited the phosphorylation of STAT3. The overexpression of EHF also reduced the tumor size, and lung metastasis in vivo. At the tumor site, ß-galactosidase activity was increased by EHF. Finally, the Kaplan-Meier-plotter analysis showed that TNBC patients with a high expression of EHF had a longer relapse-free survival rate. Our findings demonstrated that EHF inhibits breast tumor progression by inducing senescence and regulating EMT in TNBC cells.

Multi-omics analysis reveals focal adhesion characteristic associated tumor immune microenvironment in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most frequent malignant lesions of the digestive system in humans, with an insidious onset. At the time of diagnosis, most of them have developed to the middle and late stages, and cancer cells have metastasized, and the prognosis is poor. Treatment options for progressive COAD are limited, and despite the promise of immunotherapy, immunotherapy response rates are low. The assembly and disaggregation of focal adhesion are critical for the directional migration of tumor cells to different sites, and it is unclear whether focal adhesion-related genes are involved in the development and prognosis of colon adenocarcinoma. This study aimed to investigate the role of focal adhesion genes in the occurrence and prognosis of COAD. We obtained datasets of COAD patients, including RNA-sequencing data and clinical information, from the TCGA and GEO databases (GSE17538 and GSE39582). Through CNMF clustering, two molecular subtypes with different expression patterns of focal adhesion genes were identified, and it was found that the molecular subtype with low expression of focal adhesion genes had better prognosis. Then the prediction signature was constructed by LASSO-Cox regression model, and the receiver operating characteristic (ROC) curve showed that the 4-gene signature had a good prediction effect on COAD 1-, 2-, and 3-year OS. Gene function enrichment analysis showed that the high-risk group was mainly enriched in immune and adhesion-related signaling pathways, suggesting that focal adhesion genes may affect the development and prognosis of COAD by regulating the immune microenvironment and tumor metastasis. The interaction between focal adhesion genes and immunity during the occurrence of COAD may help improve the response rate of immunotherapy, which also provides new ideas for the molecular mechanism and targeted therapy in COAD.

Assessment of the Mutation Profile of Tonsillar Squamous Cell Carcinomas Using Targeted Next-Generation Sequencing.

Data regarding driver mutation profiles in tonsillar squamous cell carcinomas (TSCCs) remain scarce, limiting the understanding of its pathogenesis and unexpected behavior in the updated staging system. We investigated the incidence of clinically relevant mutations and their contribution in the prognosis of the condition, and their association with human papillomavirus (HPV) infection and adjuvant therapy. We subjected 43 surgically resected TSCC samples to targeted next-generation sequencing, determined their HPV status using polymerase chain reaction, and performed The Cancer Genomic Atlas and Gene Set Enrichment analyses. Thirty-five TSCC samples (81.4%) showed at least one oncogenic/likely oncogenic mutation among twenty-nine cancer-related genes. The top five mutated genes were TP53 (46.5%), PIK3CA (25.6%), PTEN (18.6%), EGFR (16.3%), and SMAD4 (14.0%). The EGFR pathway was the most frequently affected (51.2%), followed by the p53 (48.8%), PI3K (39.5%), and RTK (34.9%) pathways. The gene set enrichment analysis confirmed that the genes involved in signal transduction, such as growth factor receptors and second messengers, EGFR tyrosine kinase inhibitors, and PI3K signaling pathways, were mostly related with TSCCs. TP53 mutation was an independent prognostic factor predicting worse overall survival in the adjuvant therapy group. RTK mutations were related to survival in all patients and in the HPV-positive group, but multivariate analyses showed no significance. In conclusion, oncogenic/likely oncogenic mutations were relatively high in TSCCs, and TP53 and RTK mutations may be candidate predictors for poor prognosis in the adjuvant therapy and HPV-positive groups, respectively, under the updated staging system.

Application of RNA processing factors for predicting clinical outcomes in colon cancer.

Background: Colon cancer is the fifth most common cause of cancer-related death worldwide, and despite significant advances in related treatment, the prognosis of colon cancer patients remains poor. Objective: This study performs systematic bioinformatics analysis of prognostic-associated RNA processing factor genes in colon cancer using the Cancer Related Genome Atlas database to explore their role in colon carcinogenesis and prognosis and excavate potential therapeutic targets. Methods: Data sets of colon cancer patients were obtained from GEO and TCGA databases. Univariate cox analysis was performed on the GSE39582 training set to identify prognosis-associated RNA processing factor genes and constructed a muticox model. The predictive performance of the model was validated by Correlation curve analysis. Similar results were obtained for the test dataset. Functional analyses were performed to explore the underlying mechanisms of colon carcinogenesis and prognosis. Results: A constructed muticox model consisting of ßi and prognosis-related RNA processing factor gene expression levels (Expi) was established to evaluate the risk score of each patient. The subgroup with a higher risk score had lower overall survival (OS), higher risk factor, and mortality. We found that the risk score, age, gender, and TNM Stage were strongly associated with OS, and the 13-gene signature as an independent prognostic factor for colon cancer. The model has good accuracy in predicting patient survival and is superior to traditional pathological staging. Conclusion: This study proposes 13 RNA processing factor genes as a prognostic factor for colon cancer patients, which can independently predict the clinical outcome by risk score. The gene expression profile in this model is closely related to the immune status and prognosis of colon cancer patients. The interaction of the 13 RNA processing factor genes with the immune system during colon carcinogenesis provides new ideas for the molecular mechanisms and targeted therapies for colon cancer.

PTGS2 Over-Expression: A Colorectal Carcinoma Initiator not an Invasive Factor.

BACKGROUND: Cyclooxygenase-2 (COX-2) main product is Prostaglandin E2 (PGE2) which cause mitogenesis and inflammation. COX-2 is the product of prostaglandin-endoperoxide synthase 2 (PTGS2) gene expression. COX-2 dysregulation can cause angiogenesis, differentiation, and promotion of cancer and its suppression related to control of the tumor's size, number, and cell shape. This study focused on the association of COX-2 expression with colorectal carcinoma (CRC) among Iranian patients on mRNA level and in the Cancer Genome Atlas Program (TCGA) colon and rectum RNAseq dataset, and its relation with pathological features. METHODS: PTGS2 expression was assayed by quantitative-PCR method from 90 tissue samples collected from 45 participants. The control samples come from the non-tumor area of the same patients. The data analyzed based on ΔΔCq. The PTGS2-RNAseq data extracted and analyzed by UCSC Xena browser, and its association assessed the occurrence of CRC and invasive-features. RESULTS: PTGS2 showed very significant over-expression in tumor tissues (p< 0.0001) with an N-fold expression of 2.25. But, there was not any significant association between PTGS2 and CRC invasive-pathological features such as Lymphatic, vascular and perineural invasion, the Grades of cancer, and Pathologic-M in both parts of this study. CONCLUSION: The increase in PTGS2 is related to the occurrence of CRC among patient samples. But in both part of this study, PTGS2 is not an invasive factor, and it does not affect the cell differentiation of tumors and metastasis. Based on the high N-fold for patient samples, it can be a strong candidate as a CRC initiator biomarker.

Overexpression of ERCC3 is associated with poor prognosis in patients with pancreatic cancer.

Pancreatic cancer is associated with poor prognosis due to limited therapeutic options. Excision repair cross-complementing 3 (ERCC3) is an important member of nucleotide excision repair (NER) that is overexpressed in some cancers and may be regarded as a poor prognostic factor. Yet, its role in pancreatic cancer remains unclear. This study aimed to investigate the expression and functions of ERCC3 in pancreatic cancer patients and its relation with clinicopathological features. Our data suggested that the protein expression level of ERCC3 was higher in tumor tissues than in adjacent tissues. In addition, the expression of ERCC3 has shown to be associated with the tumor extent (p=0.035). Besides, analysis of the dataset in The Cancer Genome Atlas (TCGA) revealed that high expression of ERCC3 was associated with poor overall survival in pancreatic cancer patients (p=0.0136). In Cox regression analysis, ERCC3 was an independent prognostic factor for overall survival in pancreatic cancer (p<0.001). Furthermore, our in vitro data further suggested that the overexpression of ERCC3 significantly promoted pancreatic cancer (BxPC-3, CFPAC-1, and PANC-1 cells) proliferation, invasion, and migration. Taken together, this study suggested that high expression of ERCC3 might be a poor prognostic factor in human pancreatic cancer and might be used as a promising therapeutic target for pancreatic cancer treatment.

Using The Cancer Genome Atlas data to refine the 8th edition of the American Joint Committee on Cancer staging for papillary thyroid carcinoma.

PURPOSE: The 8th edition of the American Joint Committee on Cancer (AJCC) staging led to a significant downstaging of well differentiated thyroid cancer patients. However, some patients who had been downstaged still experienced death. By using data from the thyroid cancer dataset of The Cancer Genome Atlas (TCGA), we aimed to find molecular features that could improve survival prediction. METHODS: TCGA data were downloaded from cBioPortal. Restaging of cases was performed according to the pathological reports. RESULTS: Out of 496 cases, 204 (41.1%) were downstaged, and the proportion of deaths increased in stages III and IV. TERT promoter mutations were no longer enriched in stage IV only, but significantly redistributed also in stages II and III. TERT mutation was the only alteration predictive of poor survival; however, in this series it was not independent from the AJCC staging. Five proteins (4E-BP1_pT70, Chk1_pS345, Snail, STAT5 alpha and PAI-1) were significantly associated with survival, and their use as a panel refined the risk stratification independently from the AJCC staging, with a hazard ratio for a positive result of 21.2 (95%CI 3.7-122.2, P = 0.0006). CONCLUSIONS: In the TCGA series, the proportion of deaths is in line with the expected survival of the latest AJCC staging, with a neat separation of risk among stages. Nevertheless, the use of protein expression can be useful in refining the stratification. Finally, after the restaging, a considerable number of tumors with TERT mutations will be allocated in lower stages; hence, dedicated studies should define the prognostic usefulness of these mutations in low-stage diseases.

Aberrations of m6A regulators are associated with tumorigenesis and metastasis in head and neck squamous cell carcinoma.

OBJECTIVE: N6-Methyladenosine (m6A) is the most common RNA modification in eukaryotic mRNAs and growing evidence suggests that m6A modification and its regulators play crucial roles in human cancers. However, the role of m6A regulators and their molecular mechanisms in head and neck squamous cell carcinoma (HNSCC) remains largely unclear. METHODS: We therefore assessed m6A regulatory genes alterations and their mRNAs expression in HNSCC using openly available data from The Cancer Genome Atlas (TCGA). Further, we have validated the expression level of m6A regulatory gene in HNSCC tissue samples using real-time PCR. In addition, we also analyzed the protein interaction network, and functional enrichment of m6A regulatory genes. RESULTS: Analysis of TCGA data revealed that m6A regulatory genes were altered in many HNSCC patients. Importantly, we found for the first time that m6A "writer" KIAA1429 (VIRMA) was frequently amplified and mutated (8 %), which contributes to the overexpression of KIAA1429 mRNA, and the overexpression of KIAA1429 could be remarkably related to cancer stages, tumor grade, and nodal metastasis (P < 0.05). In addition, the overexpression of KIAA1429 was successfully validated using HNSCC tissue samples. CONCLUSIONS: Our findings suggest that the genetic alterations of m6A regulatory genes are associated with tumorigenesis and metastasis in HNSCC, which may provide clues to identify new therapeutic targets for HNSCC.

NDRG2 Expression in Breast Cancer Cells Downregulates PD-L1 Expression and Restores T Cell Proliferation in Tumor-Coculture.

(1) Background: The aim of the present study was to evaluate the effect of NDRG2 expression in regulating PD-L1 or PD-L2 on malignant breast cancer cells. (2) Methods: Overexpression and knockdown of the NDRG2 gene in human and mouse cancer cells were applied and quantitative real-time PCR and Western blot analysis were performed. T cell proliferation and TCGA analysis were conducted to validate negative correlation of the PD-L1 expression with the NDRG2 expression. (3) Results: We found that NDRG2 overexpression inhibits PD-L1 expression in human breast cancer cells through NF-κB signaling. NDRG2 overexpression in 4T1 mouse breast cancer cells followed by PD-L1 downregulation could block the suppressive activity of cancer cells on T cell proliferation and knockdown of NDRG2 expression enhanced the expression of PD-L1, leading to the inhibition of T cell proliferation by tumor cell coculture. Finally, we confirmed from TCGA data that PD-L1 expression in basal and triple-negative breast cancer patients was negatively correlated with the expression of NDRG2. Intriguingly, linear regression analysis using TNBC cell lines showed that the PD-L1 level was negatively associated with the NDRG2 expression level. (4) Conclusions: Our findings demonstrate that NDRG2 expression is instrumental in suppressing PD-L1 expression and restoring PD-L1-inhibited T cell proliferation activity in TNBC cells.

Development of a prognostic risk model for clear cell renal cell carcinoma by systematic evaluation of DNA methylation markers.

BACKGROUND: Current risk models for renal cell carcinoma (RCC) based on clinicopathological factors are sub-optimal in accurately identifying high-risk patients. Here, we perform a head-to-head comparison of previously published DNA methylation markers and propose a potential prognostic model for clear cell RCC (ccRCC). PATIENTS AND METHODS: Promoter methylation of PCDH8, BNC1, SCUBE3, GREM1, LAD1, NEFH, RASSF1A, GATA5, SFRP1, CDO1, and NEURL was determined by nested methylation-specific PCR. To identify clinically relevant methylated regions, The Cancer Genome Atlas (TCGA) was used to guide primer design. Formalin-fixed paraffin-embedded (FFPE) tissue samples from 336 non-metastatic ccRCC patients from the prospective Netherlands Cohort Study (NLCS) were used to develop a Cox proportional hazards model using stepwise backward elimination and bootstrapping to correct for optimism. For validation purposes, FFPE ccRCC tissue of 64 patients from the University Hospitals Leuven and a series of 232 cases from The Cancer Genome Atlas (TCGA) were used. RESULTS: Methylation of GREM1, GATA5, LAD1, NEFH, NEURL, and SFRP1 was associated with poor ccRCC-specific survival, independent of age, sex, tumor size, TNM stage or tumor grade. Moreover, the association between GREM1, NEFH, and NEURL methylation and outcome was shown to be dependent on the genomic region. A prognostic biomarker model containing GREM1, GATA5, LAD1, NEFH and NEURL methylation in combination with clinicopathological characteristics, performed better compared to the model with clinicopathological characteristics only (clinical model), in both the NLCS and the validation population with a c-statistic of 0.71 versus 0.65 and a c-statistic of 0.95 versus 0.86 consecutively. However, the biomarker model had limited added prognostic value in the TCGA series with a c-statistic of 0.76 versus 0.75 for the clinical model. CONCLUSION: In this study we performed a head-to-head comparison of potential prognostic methylation markers for ccRCC using a novel approach to guide primers design which utilizes the optimal location for measuring DNA methylation. Using this approach, we identified five methylation markers that potentially show prognostic value in addition to currently known clinicopathological factors.