Prognostic Molecular Signatures Research Articles

Retraction Note: Special transcriptome landscape and molecular prognostic signature of non-smoking head and neck cancer patients.

Retraction Note: Special transcriptome landscape and molecular prognostic signature of non-smoking head and neck cancer patients.

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities.

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

Abstract 6440: Novel N-myristoyltransferase immunohistochemistry-based predictive and prognostic tests for breast cancer

Abstract Breast cancer (BC) is the most common cancer worldwide, surpassing lung cancer incidence for the first time in 2020, and is the most common cancer diagnosed in American women. Despite the relative success of endocrine therapies in treating hormone receptor-positive (HR+) BC, de novo and developed resistances to these therapies (endocrine resistance) are still significant concerns. Furthermore, triple-negative breast cancer has a poor prognosis and remains one of the most challenging cancers to treat. We have discovered that the expression and localization of N-myristoyltransferase 2 (NMT2) could be prognostic and predictive markers for metastatic BC. We determined the expression patterns of NMT2 by immunohistochemical (IHC) analyses on primary tumor samples from treatment naïve BC patients. This retrospective study was blinded, and the tumor tissues in duplicate were stained with NMT2 monoclonal/polyclonal antibodies on an autostainer (Leica Bond). Stained slides were scored, and an "H" score representing the intensity and percent positive cells was given to each sample. In a combined cohort of BC represented by ER+, Triple Positive, and Triple Negative (n= 598) cases, we determined the expression and localization of NMT2. The localization pattern of NMT2 turned out to be a significant prognostic and predictive molecular signature. The nuclear localization serves as both prognostic and predictive markers. In a cohort of 447 ER+ BC cases that included 18 triple positive cases, the majority of the cases showed negative staining for nuclear NMT2 (n=394) and positive nuclear staining with differential expression in 53 cases. Whereas, in a cohort of 151 TNBC cases, nuclear NMT2 staining was positive in most cases (n= 116) and negative in 20 cases (undetermined; n= 15). The most important finding was the dichotomy of the nuclear NMT2 staining in predicting treatment response and prognosis. Both high cytoplasmic H score (>180) and positive nuclear staining (>0) predicted significant association with worse clinical outcomes for OS (HR = 1.72, P = .0284, 95% CI 1.06 to 2.81 for cytoplasmic NMT2 and HR = 1.74, P = 0.0006, CI 1.27 to 2.39 for the nuclear NMT2). Nuclear NMT2 status alone predicted a significant association with worse clinical outcomes to RFS (HR = 1.56, P = 0.0027, CI 1.17 to 2.09). The data from our study indicate that NMT2 could be a potential marker for predicting the likelihood of recurrence of BC and become part of routine first-line prognostic and predictive tests. Citation Format: Anuraag Shrivastav, Dean Reddick, Abinash Meher, Shailly Varma Shrivastav, Sahil Mittal, Vijayakrishna K. Gadi, Leigh Murphy. Novel N-myristoyltransferase immunohistochemistry-based predictive and prognostic tests for breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6440.

Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer

High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list. Such gene lists can be obtained as a set of features (genes) that are important for the decisions of a Machine Learning (ML) method applied to high-dimensional gene expression data. Several studies have identified predictive gene lists for patient prognosis in breast cancer, but these lists are unstable and have only a few genes in common. Instability of feature selection impedes biological interpretability: genes that are relevant for cancer pathology should be members of any predictive gene list obtained for the same clinical type of patients. Stability and interpretability of selected features can be improved by including information on molecular networks in ML methods. Graph Convolutional Neural Network (GCNN) is a contemporary deep learning approach applicable to gene expression data structured by a prior knowledge molecular network. Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanations (SHAP) are methods to explain individual decisions of deep learning models. We used both GCNN+LRP and GCNN+SHAP techniques to construct feature sets by aggregating individual explanations. We suggest a methodology to systematically and quantitatively analyze the stability, the impact on the classification performance, and the interpretability of the selected feature sets. We used this methodology to compare GCNN+LRP to GCNN+SHAP and to more classical ML-based feature selection approaches. Utilizing a large breast cancer gene expression dataset we show that, while feature selection with SHAP is useful in applications where selected features have to be impactful for classification performance, among all studied methods GCNN+LRP delivers the most stable (reproducible) and interpretable gene lists.

A four-gene signature predicts overall survival of patients with esophageal adenocarcinoma.

Esophageal adenocarcinoma (EAC) is an aggressive cancer with poor prognosis. Thus, this study aimed to identify a prognostic molecular signature to predict the overall survival (OS) of patients with EAC. The mRNA microarray data sets GSE13898 and GSE26886 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing profile and clinical data of EAC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) between EAC tissues and adjacent non-cancerous tissues were obtained using R software. DEGs associated with prognosis of OS were assessed by univariate Cox analysis, and a prognostic signature was built using stepwise multivariate Cox analysis. Time-dependent receiver operating characteristic (ROC) analysis and stratification analysis were conducted to evaluate its predictive performance. Functional enrichment analysis was performed for genes co-expressed with the signature to explore its biological functions in EAC. A total of 336 genes were identified to be differentially expressed between EAC tissues and adjacent non-cancerous tissues. After univariate and multivariate Cox regression analysis, four genes (ALAD, ABLIM3, IL17RB and IFI6) were screened out to construct a prognostic signature. According to this signature, patients could be assigned into high-risk and low-risk group with significantly different OS (P=4.92e-05<0.0001). Multivariate Cox regression analysis suggested that the four-gene signature served as an independent factor in OS prediction. In the time-dependent ROC analysis, the areas under the curves (AUCs) were 0.804, 0.792 and 0.695 for 1-, 3- and 5-year survival prediction, respectively, suggesting a good performance. Functional enrichment analysis showed that the signature was mainly clustered in cell proliferation related biological processes or pathways. The four-gene signature identified in the current study may be a potential prognostic factor for predicting OS of EAC patients.

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

Integrative Analysis Developing and Validating Potential Candidate Biomarkers for Cancer Stemness Features of Pan-Renal Cell Carcinoma

In our study, 49 key genes significantly associated with renal cell carcinoma (RCC) stemness were obtained. Next, we developed a molecular prognostic signature associated with stemness features of pan-RCC. The difference in overall survival (OS) between the high- and low-risk groups was statistically significant (p < .05). The area under the receiver operating characteristic curve for 1-year OS, 5-year OS, and 10-year OS was 0.759, 0.712, and 0.918, respectively. The results of validation in The Cancer Genome Atlas cohort and International Cancer Genome Consortium cohort revealed the predictive capability of this signature. Furthermore, we selected three genes and further validation showed that these three hub genes were potential hub biomarkers for pan-RCC stemness features.

MP17-08 CLINICAL VALIDATION OF A NOVEL SIX-GENE RISK-STRATIFICATION SIGNATURE TO SUPPORT TREATMENT DECISIONS FOR MEN DIAGNOSED WITH PROSTATE CANCER

You have accessJournal of UrologyCME1 Apr 2023MP17-08 CLINICAL VALIDATION OF A NOVEL SIX-GENE RISK-STRATIFICATION SIGNATURE TO SUPPORT TREATMENT DECISIONS FOR MEN DIAGNOSED WITH PROSTATE CANCER Agnieszka Krzyzanowska, Stephen Barron, Debra F. Higgins, Tony Loughman, Katherine M. Sheehan, Angel Chan-Ju Wang, Bozena Fender, Leah M. McGuire, David Galvin, Amanda O'Neil, Joanna Fay, Anthony O'Grady, Desmond O'Leary, R. William. G. Watson, Anders Bjartell, and William M. Gallagher Agnieszka KrzyzanowskaAgnieszka Krzyzanowska More articles by this author , Stephen BarronStephen Barron More articles by this author , Debra F. HigginsDebra F. Higgins More articles by this author , Tony LoughmanTony Loughman More articles by this author , Katherine M. SheehanKatherine M. Sheehan More articles by this author , Angel Chan-Ju WangAngel Chan-Ju Wang More articles by this author , Bozena FenderBozena Fender More articles by this author , Leah M. McGuireLeah M. McGuire More articles by this author , David GalvinDavid Galvin More articles by this author , Amanda O'NeilAmanda O'Neil More articles by this author , Joanna FayJoanna Fay More articles by this author , Anthony O'GradyAnthony O'Grady More articles by this author , Desmond O'LearyDesmond O'Leary More articles by this author , R. William. G. WatsonR. William. G. Watson More articles by this author , Anders BjartellAnders Bjartell More articles by this author , and William M. GallagherWilliam M. Gallagher More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003237.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PCa) is diagnosed in over 1.2 million men worldwide annually. Most men have indolent PCa and can safely defer treatment with active surveillance. However, many men are commonly treated with surgery or radiation therapy which are associated with reduced quality of life. Clinicians are increasingly using molecular information to further understand a patient’s risk of aggressive PCa, and while these signatures have shown promise, there remains a significant opportunity for improving prognostic capacity. METHODS: Expression of a novel six-gene signature was measured by RT-qPCR in prostate samples from an Irish cohort of men diagnosed with PCa and treated with radical prostatectomy (RP) (PCRC, n=426). Cross-validated logistic regression identified a six-gene molecular risk score (MRS) with strong prognostic performance to predict aggressive PCa. The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score to create a molecular plus clinical risk score (MCRS). The MRS and MCRS were clinically validated in an independent Swedish cohort of men diagnosed with PCa and treated by RP (UPCA, n=203 RP and n=100 biopsy samples). RESULTS: We identified a six-gene signature, four prognostic and two reference genes, with improved prognostic value over clinical features. In the clinical biopsy validation study, the AUC of the signature for AP was 0.72 (MRS) and 0.82 (MCRS) versus 0.72 (EAU) and 0.82 (CAPRA), and for high primary Gleason was 0.75 (MRS) and 0.85 (MCRS) versus 0.77 (EAU) and 0.83 (CAPRA). The C index for BCR was 0.62 (MRS) and 0.74 (MCRS) versus 0.65 (EAU) and 0.73 (CAPRA). In bivariable analysis, the six-gene signature added statistically significant (p<0.001) prognostic value to EAU and CAPRA. In the RP clinical validation study, the C index for BCR was 0.74 (MRS) and 0.83 (MCRS) versus 0.69 (EAU) and 0.77 (CAPRA). In bivariable analysis, the six-gene signature added statistically significant (p<0.0001) prognostic value to EAU and CAPRA. CONCLUSIONS: The six-gene molecular prognostic signature has strong performance for AP, high primary Gleason and BCR in independent clinical validation studies when tested on prostate biopsy or RP tissue. MCRS provides improved prognostic stratification and can inform optimal patient management post-diagnosis and post-treatment. Source of Funding: Support is acknowledged from the Swedish Cancer Society, the Swedish Scientific Council and Government Funding of Clinical Research within the Swedish National Health Service (ALF), the Irish Cancer Society, and OncoAssure Ltd © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e215 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Agnieszka Krzyzanowska More articles by this author Stephen Barron More articles by this author Debra F. Higgins More articles by this author Tony Loughman More articles by this author Katherine M. Sheehan More articles by this author Angel Chan-Ju Wang More articles by this author Bozena Fender More articles by this author Leah M. McGuire More articles by this author David Galvin More articles by this author Amanda O'Neil More articles by this author Joanna Fay More articles by this author Anthony O'Grady More articles by this author Desmond O'Leary More articles by this author R. William. G. Watson More articles by this author Anders Bjartell More articles by this author William M. Gallagher More articles by this author Expand All Advertisement PDF downloadLoading ...

Special transcriptome landscape and molecular prognostic signature of non-smoking head and neck cancer patients.

As a well-known behavioral risk factor for human health, smoking is involved in carcinogenesis, tumor progression, and therapeutic interventions of head and neck squamous cell carcinoma (HNSCC). The stratification of disease subtypes according to tobacco use is expressively needed for HNSCC precision therapy. High-throughput transcriptome profiling by RNA sequencing (RNA-seq) from The Cancer Genome Atlas (TCGA) was collected and collated for differential expression analysis and pathway enrichment analysis to characterize the molecular landscape for non-smoking HNSCC patients. Molecular prognostic signatures specific to non-smoking HNSCC patients were identified by the least absolute shrinkage and selection operator (LASSO) analysis and were then verified via internal and external validation cohorts. While proceeding to immune cell infiltration and after drug sensitivity analysis was further carried out, a proprietary nomogram was finally developed for their respective clinical applications. In what it relates to the non-smoking cohort, the enrichment analysis pointed to human papillomavirus (HPV) infection and PI3K-Akt signaling pathway, with the prognostic signature consisting of another ten prognostic genes (COL22A1, ADIPOQ, RAG1, GREM1, APBA2, SPINK9, SPP1, ARMC4, C6, and F2RL2). These signatures showed to be independent factors, and the related nomograms were, thus, constructed for their further and respective clinical applications. While the molecular landscapes and proprietary prognostic signature were characterized based on non-smoking HNSCC patients, a clinical nomogram was constructed to provide better HNSCC patient classification and guide treatment for non-smoking HNSCC patients. Nonetheless, there are still significant challenges in the recognition, diagnosis, treatment, and understanding of the potentially efficient mechanisms of HNSCC with no tobacco use.

Genomic markers of recurrence risk in atypical meningioma following gross total resection.

Meningiomas are the most common primary central nervous system (CNS) tumor in adults and CNS World Health Organization grade 2 (atypical) meningiomas show an intermediate risk of recurrence/progression. Molecular parameters are needed to better inform management following gross total resection (GTR). We performed comprehensive genomic analysis of tumor tissue from 63 patients who underwent radiologically confirmed GTR of a primary grade 2 meningioma, including a CLIA-certified target next-generation sequencing panel (n = 61), chromosomal microarray (n = 63), genome-wide methylation profiling (n = 62), H3K27me3 immunohistochemistry (n = 62), and RNA-sequencing (n = 19). Genomic features were correlated with long-term clinical outcomes (median follow-up: 10 years) using Cox proportional hazards regression modeling and published molecular prognostic signatures were evaluated. The presence of specific copy number variants (CNVs), including -1p, -10q, -7p, and -4p, was the strongest predictor of decreased recurrence-free survival (RFS) within our cohort (P < .05). NF2 mutations were frequent (51%) but did not show a significant association with RFS. DNA methylation-based classification assigned tumors to DKFZ Heidelberg benign (52%) or intermediate (47%) meningioma subclasses and was not associated with RFS. H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 tumors, insufficient for RFS analysis. Application of published integrated histologic/molecular grading systems did not improve prediction of recurrence risk over the presence of -1p or -10q alone. CNVs are strong predictors of RFS in grade 2 meningiomas following GTR. Our study supports incorporation of CNV profiling into clinical evaluation to better guide postoperative patient management, which can be readily implemented using existing, clinically validated technologies.

The Medawar Prize Acceptance Speech 2022.

The Medawar Prize Acceptance Speech 2022.

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology.

The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.

Downregulation of the enhancer of zeste homolog 1 transcriptional factor predicts poor prognosis of triple-negative breast cancer patients.

BackgroundTriple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer and lacks effective biomarkers. This study seeks to unravel the expression status and the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC tissue samples. Moreover, another objective of this study is to reveal the prognostic molecular signatures for risk stratification in TNBC patients.MethodsTo determine the expression status of EZH1/EZH2 in TNBC tissue samples, microarray analysis and immunohistochemistry were performed on in house breast cancer tissue samples. External mRNA expression matrices were used to verify its expression patterns. Furthermore, the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC were explored by performing differential expression analysis, co-expression analysis, and chromatin immunoprecipitation sequencing analysis. Kaplan–Meier survival analysis and univariate Cox regression analysis were utilized to detect the prognostic molecular signatures in TNBC patients. Nomogram and time-dependent receiver operating characteristic curves were plotted to predict the risk stratification ability of the prognostic-signatures-based Cox model.ResultsIn-house TMAs (66 TNBC vs. 106 non-TNBC) and external gene microarrays, as well as RNA-seq datasets (1,135 TNBC vs. 6,198 non-TNBC) results, confirmed the downregulation of EZH1 at both the protein and mRNA levels (SMD = −0.59 [−0.80, −0.37]), as is opposite to that of EZH2 (SMD = 0.74 [0.40, 1.08]). The upregulated transcriptional target genes of EZH1 were significantly aggregated in the cell cycle pathway, where CCNA2, CCNB1, MAD2L1, and PKMYT1 were determined as key transcriptional targets. Additionally, the downregulated transcriptional targets of EZH2 were enriched in response to the hormone, where ESR1 was identified as the hub gene. The six-signature-based prognostic model produced an impressive performance in this study, with a training AUC of 0.753, 0.981, and 0.977 at 3-, 5-, and 10-year survival probability, respectively.ConclusionEZH1 downregulation may be a key modulator in the progression of TNBC through negative transcriptional regulation by targeting CCNA2, CCNB1, MAD2L1, and PKMYT1.

ATRT-20. Novel prognostic molecular signatures for improved risk-classification of Atypical Teratoid Rhabdoid Tumours

Abstract Malignant Rhabdoid Tumours (MRT) are aggressive paediatric malignancies seen in the central nervous system (Atypical Teratoid Rhabdoid Tumours (ATRT)), and kidney and other soft tissues (Extra-cranial Rhabdoid Tumours (ECRT)). With current therapies often proving ineffective and a lack of clear prognostic associations with consensus subgroups, we explored the possibility of using prognostic molecular signatures to further identify the biological characteristics of high risk ATRT patients. By employing a cross-validated feature selection method the methylation profiles of 121 MRT patients were analysed with clinical data to obtain meta-CpG signatures associated with prognosis for ATRT, ECRT and MRT. The relationship between these meta-CpG signatures and the consensus subgroups were further explored, along with the correlation of meta-CpGs with gene expression to establish biological significance. By selecting CpGs for their ability to predict survival this method obtained three novel prognostic methylation signatures which predict MRT outcome (ATRT-5, ECRT-14 and MRT-42). These signatures are independent of molecular subgroup and each signature was significantly associated with overall survival (OS) and event free survival (EFS) in their respective cohorts (p&amp;lt;0.001). Both ATRT-5 and MRT-42 maintained their significant association with OS in an independent ATRT cohort (n=64) and each meta-CPG signature is prognostically independent of other major clinical risk factors (e.g. receipt of radiotherapy and presence of metastases). Biologically, individuals with high-risk methylation signatures showed a gene expression profile suggestive of higher proliferative rates and tumours with low-risk scores in ATRT-5 and MRT-42 had an upregulated inflammatory response and increased immune infiltration. Combining these meta-CpGs with other significant clinical risk-factors produced high performing multivariate Cox-models enabling us to propose new stratification models for ATRT and MRT patients. These subgroup-independent prognostic signatures represent a distinct biology in ATRT and, if validated in prospective studies, could progress the use and efficacy of precision-based medicine in this therapeutically challenging disease.

Molecular subtypes of m6A RNA methylation modification patterns and their clinical significance in clear cell renal cell carcinoma.

BackgroundIn this study, we sought to investigate the association between N6-methyladenosine (m6A) RNA methylation-modification patterns and patient prognosis in clear cell renal cell carcinoma (ccRCC) and construct a ccRCC molecular signature according to expressions of m6A-related genes.MethodsFirst, the clinical data and the transcriptomes of 530 patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA). The expression patterns of m6A-related genes were extracted, and the differences in m6A-modification patterns between normal and tumor renal tissues were analyzed. To explore the prognostic role of m6A-modification patterns a in ccRCC, the molecular subtypes of ccRCC were identified based on the expression patterns of the m6A-related genes, and survival rates in patients with the different subtypes were compared. According to expressions of m6A-related genes and clinical prognosis data, a prognostic molecular signature was constructed using least absolute shrinkage and selector operation (LASSO)-Cox regression analysis.ResultsAmong the 13 m6A -related genes identified in this study, 8 (YTHDC1, YTHDF2, HNRNPC, METTL14, ZC3H13, FTO, YTHDC2, and YTHDF1) showed significant expression differences between normal and tumor renal tissues. The molecular subtypes of ccRCC identified according to their expression of the 13 m6A-related genes were associated with differential clinical outcomes.ConclusionsFollowing TCGA data-mining, different molecular subtypes of ccRCC based on m6A RNA methylation patterns were found to have different prognoses. The molecular signature constructed according to the expression patterns of m6A-related genes could predict patient prognosis in ccRCC. We believe m6A RNA methylation modification is a potential therapeutic target and may play a crucial role in ccRCC.

Identification of a Ten-Gene Signature of DNA Damage Response Pathways with Prognostic Value in Esophageal Squamous Cell Carcinoma.

Molecular prognostic signatures are critical for treatment decision-making in esophageal squamous cell cancer (ESCC), but the robustness of these signatures is limited. The aberrant DNA damage response (DDR) pathway may lead to the accumulation of mutations and thus accelerate tumor progression in ESCC. Given this, we applied the LASSO Cox regression to the transcriptomic data of DDR genes, and a prognostic DDR-related gene expression signature (DRGS) consisting of ten genes was constructed, including PARP3, POLB, XRCC5, MLH1, DMC1, GTF2H3, PER1, SMC5, TCEA1, and HERC2. The DRGS was independently associated with overall survival in both training and validation cohorts. The DRGS achieved higher accuracy than six previously reported multigene signatures for the prediction of prognosis in comparable cohorts. Furtherly, a nomogram incorporating DRGS and clinicopathological features showed improved predicting performance. Taken together, the DRGS was identified as a novel, robust, and effective prognostic indicator, which may refine the scheme of risk stratification and management in ESCC patients.

Coiled-coil domain-containing protein-124 (Ccdc124) is a novel RNA binding factor up-regulated in endometrial, ovarian, and urinary bladder cancers.

Coiled-coil domain containing protein-124 (Ccdc124) is a putative mRNA-binding factor associated with cell division, and ribosome biology. Previous reports mentioned an up-regulation of CCDC124 gene in cancer, and listed its mRNA in a molecular prognostic signature in breast cancer. Establishing RNA-binding characteristics of Ccdc124 for a better molecular functional characterization, and carrying-out retrospective studies in order to evaluate its aberrant expression in human cancer samples from various tissue origins. Bioinformatics calculations followed by RIP and RNA-seq experiments were performed to investigate mRNA targets of Ccdc124. Quantitative studies on arrays of cDNAs from different cancers and IHC assays on tissue arrays were used to assess CCDC124 expression levels in cancers. Ccdc124 was characterized as an RNA-binding protein (RBP) interacting with various mRNAs. CCDC124 mRNA levels were high in tumors, with a particular up-regulation in cancers from esophagus, adrenal gland, endometrium, liver, ovary, thyroid, and urinary bladder. IHC assays indicated strong Ccdc124 positivity in endometrial (95.4%), urinary bladder (68.4%), and ovarian cancers (86.8%). Ccdc124 is a cytokinesis related RBP interacting with various mRNAs. CCDC124 mRNA over-expression and an accompanied increase in Ccdc124 protein accumulation was reported in cancers, indicating this RBP as a novel cancer cell marker.

DNA methylation signatures associated with prognosis of gastric cancer

BackgroundFew studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.MethodsBased on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.ResultsWe newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed.ConclusionsWe newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

29P Non-diploidy related prognostic molecular signature (NPMS) predict an “immunologically hot” phenotype in squamous cell lung cancer

Lung squamous cell carcinoma (LUSC) patients suffer from less targetable onco-drivers and potentially acquire clinical benefit from immune checkpoint inhibitors (ICIs). DNA content aberrations contribute to genomic instability and are initial events of tumorigenesis. We established a non-diploidy related prognostic molecular signature (NPMS) based on differentially expressed genes (DEGs) and investigated the immune features of it. We conducted a retrospective analysis based on data downloaded from TCGA database. By integrating CNA and SNV via ABSOLUTE algorithm, we obtained the DNA ploidy status and divided patients into near-diploid and non-diploid group. DEGs were selected and gene functional enrichments were carried out. NPMS was established by all subset multivariate Cox regression to further stratify patients and validated by integrative analysis of GSE73403, GSE41271, GSE4212. Gene sets enrichment analysis was applied to further figure out the mechanism behind NPMS. Non-diploidy coincided with higher TMB and intratumor heterogeneity. Functional enrichment indicated that DEGs mainly participated in genomic instability. NPMS containing HOXB5, TINAGL1, POLR3GL, APOB, FABP6, SCARF1 was established. Patients with low score had better OS than those with high score (HR 0.60, 95%CI 0.45-0.79, p-value=0.000321). This was validated in the GEO cohorts (HR 0.57, 95%CI 0.37-0.88, p-value=0.0121). Dysregulation of DNA repair and cell cycle checkpoints were higher enriched in high score group. The immune phenotype of high score tended to be "immunologically hot", which was characterized as high density but low activity immune cells infiltrated, accompanied by expression of higher immunosuppressive factors, such as PD-1, CTLA4, IDO1. Meanwhile, the upregulation of IFN-γ signaling pathway, lipid alternation and the high correlation between them were observed. High NPMS score corresponded with an "immunologically hot" feature thereby led to shorter OS. The crosstalk of upregulated IFN-γ signaling and aberrations of tumor metabolism mignt participate. It's rational to deduced that patients with high score might be the potential ICIs benefited subpopulation.

Predicted sensitivity to endocrine therapy for stage II-III hormone receptor-positive and HER2-negative (HR+/HER2−) breast cancer before chemo-endocrine therapy

We proposed that a test for sensitivity to the adjuvant endocrine therapy component of treatment for patients with stage II-III breast cancer (SET2,3) should measure transcription related to estrogen and progesterone receptors (SETER/PR index) adjusted for a baseline prognostic index (BPI) combining clinical tumor and nodal stage with molecular subtype by RNA4 (ESR1, PGR, ERBB2, and AURKA). Patients with clinically high-risk, hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative (HR+/HER2-) breast cancer received neoadjuvant taxane-anthracycline chemotherapy, surgery with measurement of residual cancer burden (RCB), and then adjuvant endocrine therapy. SET2,3 was measured from pre-treatment tumor biopsies, evaluated first in an MD Anderson Cancer Center (MDACC) cohort (n= 307, 11 years' follow-up, U133A microarrays), cut point was determined, and then independent, blinded evaluation was carried out in the I-SPY2 trial (n= 268, high-risk MammaPrint result, 3.8 years' follow-up, Agilent-44K microarrays, NCI Clinical Trials ID: NCT01042379). Primary outcome measure was distant relapse-free survival. Multivariate Cox regression models tested prognostic independence of SET2,3 relative to RCB and other molecular prognostic signatures, and whether other prognostic signatures could substitute for SETER/PR or RNA4 components of SET2,3. SET2,3 added independent prognostic information to RCB in the MDACC cohort: SET2,3 [hazard ratio (HR) 0.23, P= 0.004] and RCB (HR 1.77, P < 0.001); and the I-SPY2 trial: SET2,3 (HR 0.27, P= 0.031) and RCB (HR 1.68, P= 0.008). SET2,3 provided similar prognostic information irrespective of whether RCB-II or RCB-III after chemotherapy, and in both luminal subtypes. Conversely, RCB was most strongly prognostic in cancers with low SET2,3 status (MDACC P < 0.001, I-SPY2 P < 0.001). Other molecular signatures were not independently prognostic; they could effectively substitute for RNA4 subtype within the BPI component of SET2,3, but they could not effectively substitute for SETER/PR index. SET2,3 added independent prognostic information to chemotherapy response (RCB) and baseline prognostic score or subtype. Approximately 40% of patients with clinically high-risk HR+/HER2- disease had high SET2,3 and could be considered for clinical trials of neoadjuvant endocrine-based treatment.