Identification Of Cancer Biomarkers Research Articles

Identification of Prognostic Biomarkers in Papillary Thyroid Cancer and Developing Non-Invasive Diagnostic Models Through Integrated Bioinformatics Analysis.

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid carcinoma, mainly detected in patients with benign thyroid nodules (BTN). Due to the invasiveness of accurate diagnostic tests, there is a need to discover applicable biomarkers for PTC. So, in this study, we aimed to identify the genes associated with prognosis in PTC. Besides, we performed a machine learning tool to develop a non-invasive diagnostic approach for PTC. For the study purposes, the miRNA dataset GSE130512 was downloaded from the GEO database and then analyzed to identify the common differentially expressed miRNAs in patients with non-metastatic PTC (nm-PTC)/metastatic PTC (m-PTC) compared with BTNs. The SVM was also applied to differentiate patients with PTC from those patients with BTN using the common DEMs. A protein-protein interaction network was also constructed based on the targets of the common DEMs. Next, functional analysis was performed, the hub genes were determined, and survival analysis was then executed. A total of three common miRNAs were found to be differentially expressed among patients with nm-PTC/m-PTC compared with BTNs. In addition, it was established that the autophagosome maturation, ciliary basal body-plasma membrane docking, antigen processing as ubiquitination & proteasome degradation, and class I MHC mediated antigen processing & presentation are associated with the pathogenesis of PTC. Furthermore, it was illustrated that RPS6KB1, CCNT1, SP1, and CHD4 might serve as new potential biomarkers for PTC prognosis. RPS6KB1, CCNT1, SP1, and CHD4 may be considered new potential biomarkers used for prognostic aims in PTC. However, performing validation tests is inevitable in the future.

Abstract OT2-07-01: The FLEX real-world data platform explores new gene expression profiles and investigator-initiated protocols in early-stage breast cancer

Abstract Background: Genomic signatures provide prognostic information such as tumor metastatic potential, outperforming information gained from clinicopathologic factors alone, which has transformed personalized treatment of early breast cancer. Together, whole genome expression data can accurately stratify tumors into clinically actionable molecular subtypes. The FLEX study (NCT03053193) is a large, real-world dataset, combining whole genome expression with clinically annotated data, to accelerate research and discovery of subgroups in breast cancer. The goal of FLEX is to discover and develop novel genomic profiles that bring precision oncology to the clinic, and ultimately improve treatment and outcomes of patients with breast cancer. Study Design: The FLEX study is a multicenter, prospective observational trial of patients aged ≥ 18 years with stage I-III breast cancer who receive MammaPrint, with or without BluePrint. Patients consent to the acquisition of clinical data and clinically annotated full transcriptome tumor analysis. The FLEX study creates a unique infrastructure to develop and investigate hypotheses for targeted subset analyses and/or clinical trials. In doing so, the adaptive protocol allows submission of investigator-initiated sub-studies. Upon approval by a Scientific Review Committee, FLEX investigators can query the clinical and genomic data in FLEX to investigate hypotheses for targeted subset analysis. Patients enrolled in the original study who meet all eligibility criteria may be enrolled in sub-studies without additional consent. All necessary clinical data will be collected from patients, starting from diagnosis through 10-year follow-up. Sub-study research categories include: breast cancer and age, optimization of therapeutic strategies, breast cancer tumor types, biomarker identification and quality of care. The FLEX enrollment goal is a minimum of 30,000 patients within 10 years; since April 2017, more than 7,500 patients were enrolled at more than 90 sites. Participating sites include community hospitals and nine National Cancer Institute-designated comprehensive cancer centers to ensure inclusion of diverse populations, particularly patient populations that are underrepresented in traditional clinical trials. Most recently, the FLEX network was expanded globally with additional sites in Greece and Israel. To date, 38 investigator-initiated sub-studies have been approved, resulting in 23 published abstracts at national congresses. Trial contact information: NCT03053193, FLEX@agendia.com Citation Format: Cynthia X. Ma, Robert Maganini, Pat Whitworth, Jennifer A. Crozier, Joyce O'Shaughnessy, Sami Diab, Beth-Ann Lesnikoski, June Lee, Nina D'Abreo, Blanche H. Mavromatis, Pond Kelemen, Darina Pronin, Hiwot Abebe, Michelle L. Bolner, Christine Finn, Lisa Blumencranz, William Audeh, FLEX Investigators Group. The FLEX real-world data platform explores new gene expression profiles and investigator-initiated protocols in early-stage breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT2-07-01.

Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.

Long non-coding RNA and non-coding nucleic acids: Signaling players in the networks of the tumor ecosystem.

Recent findings have revealed that human genome encodes tens of thousands long noncoding RNAs (lncRNAs), which play essential roles in broad spectrum of cellular processes. Emerging evidence has uncovered a new archetype of lncRNAs which functions as key components of cell signaling pathways. In this review, we describe how lncRNAs interact with proteins to regulate cancer intracellular signaling and intercellular signaling in the tumor microenvironment (TME), which enable cancer cells to acquire malignant hallmarks. Moreover, besides lncRNAs, non-coding nucleic acids, such as neutrophil extracellular trap-DNA (NET-DNA), endogenous DNA and RNA, can act as signal molecules to connect cells from distant organs and trigger systemic responses in the macroenvironment of tumor-bearing hosts. Overall, the widely observed dysregulation of non-coding nucleic acids in cancer alters signaling networks in the tumor ecosystem, providing a rich resource for the identification of cancer biomarkers and therapeutic targets.

A multi-objective optimization approach for the identification of cancer biomarkers from RNA-seq data

Identification of biomarkers is essential for the diagnosis and prognosis of certain diseases, like cancer. Gene selection purpose is finding the minimum number of genes that can classify a (e.g. normal or tumour) sample with a high accuracy. Therefore, the selected genes can be studied as potential cancer biomarkers. In this article, a new method for gene selection is proposed in two steps. The first step is a filtering of the most relevant genes of a gene expression dataset. In this step, three feature selection methods have been combined. Since gene selection is a two-objective problem (minimizing the number of selected genes while maximizing the classification accuracy), the second step is performed as a multi-objective optimization, using an Artificial Bee Colony based on Dominance (ABCD) algorithm. ABCD algorithm uses internally a support vector machine (SVM) classifier. The method has been tested with five RNA-seq cancer datasets and with a comparative study of the results obtained by the method and by other five methods proposed in the scientific literature by other authors. Finally, in order to check if the genes selected by the proposed method could be studied as biomarkers, the relation between the selected genes and the cancer they belong to is analysed. It can be concluded that the proposed method is effective in gene selection for the identification of cancer biomarkers from RNA-seq data.

Identification of prognostic biomarkers in advanced non-small cell lung cancer with EGFR mutations: Using comprehensive kinase activity profiling

Identification of prognostic biomarkers in advanced non-small cell lung cancer with EGFR mutations: Using comprehensive kinase activity profiling

Explainable machine learning of the breast cancer staging for designing smart biomarker sensors

In medical diagnostics, smart biomarker sensors are used to detect and monitor biomarker thresholds. Recent bariatric research has shown a connection between obesity and an elevated risk of breast cancer in women, with the growth of adipose tissues and malignancy as a disease caused by the secretion of proinflammatory cytokines and adipocytokines. The current article focuses on HOMA, leptin, adiponectin, and resistin, the adipocytokines that have been identified as the primary causes of breast cancer in obese women during the last two decades. In this manuscript, the XAI tool is implemented on the breast cancer data and presents graphical interpretation. The clinical significance and molecular processes behind circulating HOMA, leptin, adiponectin, and breast cancer resistance have been explored, and XAI methods have been used to construct models for the identification of novel breast cancer biomarkers. The premise of this study is based on classifying each adipokine into two groups: low- and high-level concentrations. We examine the correlation between each group and the likelihood of developing breast cancer. The results provided useful evidence to develop accurate treatment interventions for breast cancer patients based on their biomarker levels and body mass index.

Expressão de tetraspanina co029 como biomarcador tumoral para preparação de anticorpos monoclonais: avaliação antigênica em células de câncer colorretal

INTRODUCTION: The identification of innovative cancer biomarkers is a very relevant ongoing quest. Moreover, their role in cancer diagnosis and clinical management has been radically changed in the last few years with the major emphasis on cancer molecular classification, therapeutic target identification, and therapeutic protocol responsivity. tetraspanins are a family of transmembrane proteins correlated with tumor stage, tumor type and patient outcome affecting cell growth, morphology, invasion, and metastasis. METHODS: We expressed the 31kDa transmembrane human tetraspanin co029 antigen in Escherichia coli expression host cells using Gateway® platform. Western blotting and ELISA techniques, together with gene sequencing, confirmed the identity of TSP co029 recombinant protein. Forty clones producing antibodies against TSP co029 were obtained. These antibodies were incubated with human colorectal cancer cells in different conditions. ELISA and immunohistochemistry were also performed. RESULTS: The expressed tetraspanin had an appropriate conformation and antigenic integrity to produce antibodies with affinity to the native TSP co029 biomarker. The affinity of the purified recombinant protein and antibodies were confirmed by western blotting, florescent staining of human colorectal cancer cells in fluorescence and confocal microscopies and by ELISA and immunohistochemistry. CONCLUSION: Our data showed that the recombinant protein and antibodies produced in this study allowed the confirmation of tetraspanin co029 protein presented on the surface of human colorectal cancer cells.

Circulating RNAs in prostate cancer patients.

Growing bodies of evidence have demonstrated that the identification of prostate cancer (PCa) biomarkers in the patients' blood and urine may remarkably improve PCa diagnosis and progression monitoring. Among diverse cancer-derived circulating materials, extracellular RNA molecules (exRNAs) represent a compelling component to investigate cancer-related alterations. Once outside the intracellular environment, exRNAs circulate in biofluids either in association with protein complexes or encapsulated inside extracellular vesicles (EVs). Notably, EV-associated RNAs (EV-RNAs) were used for the development of several assays (such as the FDA-approved Progensa Prostate Cancer Antigen 3 (PCA3 test) aiming at improving early PCa detection. EV-RNAs encompass a mixture of species, including small non-coding RNAs (e.g. miRNA and circRNA), lncRNAs and mRNAs. Several methods have been proposed to isolate EVs and relevant RNAs, and to perform RNA-Seq studies to identify potential cancer biomarkers. However, EVs in the circulation of a cancer patient include a multitude of diverse populations that are released by both cancer and normal cells from different tissues, thereby leading to a heterogeneous EV-RNA-associated transcriptional signal. Decrypting the complexity of such a composite signal is nowadays the major challenge faced in the identification of specific tumor-associated RNAs. Multiple deconvolution algorithms have been proposed so far to infer the enrichment of cancer-specific signals from gene expression data. However, novel strategies for EVs sorting and sequencing of RNA associated to single EVs populations will remarkably facilitate the identification of cancer-related molecules. Altogether, the studies summarized here demonstrate the high potential of using EV-RNA biomarkers in PCa and highlight the urgent need of improving technologies and computational approaches to characterize specific EVs populations and their relevant RNA cargo.

Identification of novel biomarkers in breast cancer via integrated bioinformatics analysis and experimental validation

ABSTRACT Breast cancer (BC), an extremely aggressive malignant tumor, causes a large number of deaths worldwide. In this study, we pooled profile datasets from three cohorts to illuminate the underlying key genes and pathways of BC. Expression profiles GSE42568, GSE45827, and GSE124646, including 244 BC tissues and 28 normal breast tissues, were integrated and analyzed. Differentially expressed genes (DEGs) were screened out based on these three datasets. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plugin were utilized to visualize protein protein interaction (PPI) of these DEGs. The module with the highest connectivity of gene interactions was selected for further analysis. All of these hub genes had a significantly worse prognosis in BC by survival analysis. Additionally, four genes (CDK1, CDC20, AURKA, and MCM4) dramatically were enriched in oocyte meiosis and cell cycle pathways through re-analysis of DAVID. Moreover, the mRNA and protein levels of CDK1, CDC20, AURKA, and MCM4 were significantly increased in BC patients. In addition, knockdown of CDK1 and CDC20 by small interfering RNA remarkably suppressed cell migration and invasion in MCF-7 and MDA-MB-231 cells. In conclusion, our results suggested that CDK1, CDC20, AURKA, and MCM4 were reliable biomarkers of BC via bioinformatics analysis and experimental validation and may act as prospective targets for BC diagnosis and treatment.

Construction of circRNA-miRNA-mRNA network and identification of novel potential biomarkers for non-small cell lung cancer

BackgroundThe underlying circular RNAs (circRNAs)-related competitive endogenous RNA (ceRNA) mechanisms of pathogenesis and prognosis in non-small cell lung cancer (NSCLC) remain unclear.MethodsDifferentially expressed circRNAs (DECs) in two Gene Expression Omnibus datasets (GSE101684 and GSE112214) were identified by utilizing R package (Limma). Circinteractome and StarBase databases were used to predict circRNA associated-miRNAs and mRNAs, respectively. Then, protein–protein interaction (PPI) network of hub genes and ceRNA network were constructed by STRING and Cytoscape. Also, analyses of functional enrichment, genomic mutation and diagnostic ROC were performed. TIMER database was used to analyze the association between immune infiltration and target genes. Kaplan–Meier analysis, cox regression and the nomogram prediction model were used to evaluate the prognostic value of target genes. Finally, the expression of potential circRNAs and target genes was validated in cell lines and tissues by quantitative real-time PCR (qRT-PCR) and Human Protein Atlas (HPA) database.ResultsIn this study, 15 DECs were identified between NSCLC tissues and adjacent-normal tissues in two GEO datasets. Following the qRT-PCR corroboration, 7 DECs (hsa_circ_0002017, hsa_circ_0069244, hsa_circ_026337, hsa_circ_0002346, hsa_circ_0007386, hsa_circ_0008234, hsa_circ_0006857) were dramatically downregulated in A549 and SK-MES-1 compared with HFL-1 cells. Then, 12 circRNA-sponged miRNAs were screened by Circinteractome and StarBase, especially, hsa-miR-767-3p and hsa-miR-767-5p were significantly up-regulated and relevant to the prognosis. Utilizing the miRDB and Cytoscape, 12 miRNA-target genes were found. Functional enrichment, genomic mutation and diagnostic analyses were also performed. Among them, FNBP1, AKT3, HERC1, COL4A1, TOLLIP, ARRB1, FZD4 and PIK3R1 were related to the immune infiltration via TIMER database. The expression of ARRB1, FNBP1, FZD4, and HERC1 was correlated with poor overall survival (OS) in NSCLC patients by cox regression and nomogram. Furthermore, the hub-mRNAs were validated in cell lines and tissues.ConclusionWe constructed the circRNA-miRNA-mRNA network that might provide novel insights into the pathogenesis of NSCLC and reveal promising immune infiltration and prognostic biomarkers.

Identification of DNA Damage Repair-Associated Prognostic Biomarkers for Prostate Cancer Using Transcriptomic Data Analysis.

In the recent decade, the importance of DNA damage repair (DDR) and its clinical application have been firmly recognized in prostate cancer (PC). For example, olaparib was just approved in May 2020 to treat metastatic castration-resistant PC with homologous recombination repair-mutated genes; however, not all patients can benefit from olaparib, and the treatment response depends on patient-specific mutations. This highlights the need to understand the detailed DDR biology further and develop DDR-based biomarkers. In this study, we establish a four-gene panel of which the expression is significantly associated with overall survival (OS) and progression-free survival (PFS) in PC patients from the TCGA-PRAD database. This panel includes DNTT, EXO1, NEIL3, and EME2 genes. Patients with higher expression of the four identified genes have significantly worse OS and PFS. This significance also exists in a multivariate Cox regression model adjusting for age, PSA, TNM stages, and Gleason scores. Moreover, the expression of the four-gene panel is highly correlated with aggressiveness based on well-known PAM50 and PCS subtyping classifiers. Using publicly available databases, we successfully validate the four-gene panel as having the potential to serve as a prognostic and predictive biomarker for PC specifically based on DDR biology.

Ultrasound-Based Method for the Identification of Novel MicroRNA Biomarkers in Prostate Cancer.

The detection of circulating microRNA (miRNA)-based biomarkers represents an innovative, non-invasive method for the early detection of cancer. However, the low concentration of miRNAs released in body fluids and the difficult identification of the tumor site have limited their clinical use as effective cancer biomarkers. To evaluate if ultrasound treatment could amplify the release of extracellular cancer biomarkers, we treated a panel of prostate cancer (PCa) cell lines with an ultrasound-based prototype and profiled the release of miRNAs in the extracellular space, with the aim of identifying novel miRNA-based biomarkers that could be used for PCa diagnosis and the monitoring of tumor evolution. We provide evidence that US-mediated sonoporation amplifies the release of miRNAs from both androgen-dependent (AD) and -independent (AI) PCa cells. We identified four PCa-related miRNAs, whose levels in LNCaP and DU145 supernatants were significantly increased following ultrasound treatment: mir-629-5p, mir-374-5p, mir-194-5p, and let-7d-5p. We further analyzed a publicly available dataset of PCa, showing that the serum expression of these novel miRNAs was upregulated in PCa patients compared to controls, thus confirming their clinical relevance. Our findings highlight the potential of using ultrasound to identify novel cell-free miRNAs released from cancer cells, with the aim of developing new biomarkers with diagnostic and predictive value.

Single-cell RNA sequencing for the identification of early-stage lung cancer biomarkers from circulating blood

Lung cancer accounts for more than half of the new cancers diagnosed world-wide with poor survival rates. Despite the development of chemical, radiological, and immunotherapies, many patients do not benefit from these therapies, as recurrence is common. We performed single-cell RNA-sequencing (scRNA-seq) analysis using Fluidigm C1 systems to characterize human lung cancer transcriptomes at single-cell resolution. Validation of scRNA-seq differentially expressed genes (DEGs) through quantitative real time-polymerase chain reaction (qRT-PCR) found a positive correlation in fold-change values between C-X-C motif chemokine ligand 1 (CXCL1) and 2 (CXCL2) compared with bulk-cell level in 34 primary lung adenocarcinomas (LUADs) from Stage I patients. Furthermore, we discovered an inverse correlation between chemokine mRNAs, miR-532-5p, and miR-1266-3p in early-stage primary LUADs. Specially, miR-532-5p was quantifiable in plasma from the corresponding LUADs. Collectively, we identified markers of early-stage lung cancer that were validated in primary lung tumors and circulating blood.

Identification and validation of tumor microenvironment-related prognostic biomarkers in breast cancer.

BackgroundStromal cells and immune cells in tumor microenvironment (TME) have been reported to have significant value in the diagnosis and prognosis of cancers. We aimed to identify key biomarkers predicting survival in the TME of breast cancer.MethodsCell type enrichment analysis was performed to estimate cell scores using the xCell method with gene expression data from public database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify key signature from the cell scores.ResultsTotally, 50 cells in TME had different scores between 1,078 breast cancer tissues and 112 adjacent normal tissues. We identified a 4-cell signature predicting breast cancer survival, including myocytes, natural killer T cell (NKT), conventional dendritic cell (cDC) and sebocytes, which was validated in the test set. Further analysis showed that cDC score was a key signature predicting prognosis of breast cancer. cDC score was significantly associated with molecular classification and stage of breast cancer, as well as expression level of Ki67. Spearman’s correlation analysis found that cDC score was inversely correlated with the expression level of HER2. High cDC score may predicate better pathological complete response rate. Mechanism analysis indicated high cDC score was associated with elevated immune activity; IL-2 was a key gene associated with high cDC score; and Breast cancer patients with high IL-2 expression had a longer survival time.ConclusionsIn conclusion, cDC score was a key signature predicting prognosis for breast cancer. cDCs may exert antitumor effects by upregulating IL-2.

Identification of Potential Biomarkers for Liver Cancer Through Gene Mutation and Clinical Characteristics.

Liver cancer is a common malignant tumor worldwide, which is a serious threat to the health of people. We try to investigate some mutations and clinical indicators as candidate markers for the development of liver cancer through targeted region capture technology combined with next-generation sequencing. We collected peripheral blood and liver cancer tissue samples from 32 liver patients concurrently. The SeqCap EZ Prime Choice Probe was used to perform the targeted enrichment; this probe captures 1,000 known cancer-associated genes. We calculated the tumor mutation burden (TMB) for each patient. The high-frequency mutations and these relative genes were identified. Eventually, survival analysis was performed based on the mutations and clinical indicators. In 32 liver patients, a total of 29 high-frequency mutations were investigated. They were located in 25 genes, which were enriched in 9 cellular components (CCs), 6 molecular functions (MFs), and 21 biological processes (BPs). Among them, EZH2 c.1544A>G and CCND1 c.839A>T had the highest mutation frequency (5/32). In the protein–protein interaction (PPI) network, EZH2-DNMT3A, NOTCH1-CCND1, and ABL1-CCND1 were the top three pairs. The survival analysis showed that there were significant differences in progression-free survival (PFS) and overall survival (OS) between the Karnofsky performance score (KPS) groups. The PFS and OS in the TMB high group were higher than those in the TMB low group. OS and tumor stage had a remarkable relationship. In conclusion, EZH2 c.1544A>G and CCND1 c.839A>T might be potential biomarkers of liver cancer. TMB might be used as a prognosis and survival indicator of liver cancer.

Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers.

A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker.

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer.

Simple SummaryMany studies have identified cancer subtypes based on the cancer driver genes, or the proportion of mutational processes in cancer genomes, however, none of these cancer subtyping methods consider these features together to identify cancer subtypes. Accurate classification of cancer individuals with similar mutational profiles may help clinicians to identify individuals who could receive the same types of treatment. Here, we develop a new statistical pipeline and use a novel concept, “gene-motif”, to identify five pancreatic cancer subtypes, in which for most of them, targeted treatment options are currently available. More importantly, for the first time we provide a system-wide analysis of the enrichment of de novo mutations in a specific motif context of the driver genes in pancreatic cancer. By knowing the genes and motif associated with the mutations, a personalized treatment can be developed that considers the specific nucleotide sequence context of mutations within responsible genes.It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

SEC23A Is an Independent Prognostic Biomarker in Bladder Cancer Correlated With MAPK Signaling.

Clinical data mining and bioinformatics analysis can be employed effectively to elucidate the function and underlying mechanisms of the gene of interest. Here, we have proposed a framework for the identification and validation of independent biomarkers in human cancer and for mechanistic profiling using gene sets enrichment analysis and pathway analysis. This is followed by validation with in vitro experiments. Using this framework to analyze the clinical relevance of SEC23A, we have discovered the prognostic potential of SEC23A in different cancers and identified SEC23A as an independent prognostic factor for poor prognosis in bladder cancer, which implicates SEC23A, for the first time, as an oncogene. Bioinformatic analyses have elucidated an association between SEC23A expression and the upregulation of the MAPK signaling pathway. Using the T24 human bladder cell line, we confirmed that knockdown of SEC23A expression could effectively impact the MAPK signaling pathway. Further, through PCR verification, we showed that MEF2A, one of the key genes of the MAPK signaling pathway, might be a downstream factor of the SEC23A gene.

P-260 Plasma exRNA-based identification of novel biomarkers in early gastric cancer

Since early gastric cancer rarely causes symptoms, there are still many challenges in early detection. So far, there is no ideal biomarker for early Gastric Cancer (EGC) screening. We attempted to use circulating extracellular RNA markers combined with bioinformatics techniques to explore effective and non-invasive biomarkers for detection of early gastric cancer.