Identification Of Cancer Biomarkers Research Articles

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes1.

Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses.

Background: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognoses, moreover its pathogenesis is not entirely clear. The aim of this study was to identify key genes of IBC, which might serve as diagnostic biomarkers and/or therapeutic targets. Methods: Two microarray datasets, GSE23720 and GSE45581, were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential biological functions of the differentially expressed genes (DEGs). Next, a protein-protein interaction (PPI) network was constructed and visualized by Cytoscape. Functional modules and hub genes were screened using MCODE and cytohubba plug-ins, and the Cancer Genome Atlas survival analysis along with quantitative reverse transcriptional polymerase chain reactions of clinical samples was used to validate the effect that the hub genes have on IBC. Results: A total of 215 DEGs were identified, consisting of 105 upregulated and 110 downregulated genes. GO and KEGG analyses showed that the enriched terms and pathways were mainly associated with cell cycle, proliferation, drug metabolism, and oncogenesis. From the PPI network, we identified six hub genes, including Cell Division Cycle 45 (CDC45), Polo Like Kinase 1 (PLK1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cell Division Cycle 20 (CDC20), Aurora Kinase A (AURKA), and Mitotic Arrest Deficient 2 Like 1 (MAD2L1). The survival analyses and expression validation studies verified the robustness of these hub genes. Conclusion: This study provides new insights into the understanding of the molecular mechanisms of IBC; in addition, the identified hub genes may serve as potential targets for diagnosis and treatment.

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools.

Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1–2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.

Identification of prognostic gene biomarkers for metastatic skin cancer using data mining.

Skin cancer is a common malignant tumor in China and throughout the world, and the rate of recurrence is considerably high, thus endangering the quality of life and health of patients, and increasing the economic burden and pressure to the families of those afflicted. Due to the limitations of traditional drug treatments, it is difficult to achieve the desired therapeutic effect of complete removal. However, targeted gene therapy may be a novel means of treating skin cancer, as the targeted nature of treatment may improve therapeutic outcomes. However, targeted gene therapy requires physicians to select the appropriate gene, which means suitable genetic biomarkers must be identified from complex genetic data. In the present study, the least absolute shrinkage and selection operator regression analysis method was used with 10-fold cross verification to reduce the dimensions of gene data in patients with skin cancer, and subsequently, 20 gene biomarkers were screened. A prognostic model was constructed using these 20 gene biomarkers, and the validity of the model was assessed using a training set and a verification set, which showed that the model performed well. Finally, gene function analysis of these 20 gene biomarkers was determined. Relevant studies were found to show that the genetic biomarkers identified in this paper may possess value for the follow-up clinical treatment of skin cancer.

Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics.

Cellular secreted proteins (secretome), together with cellular membrane proteins, collectively referred to as secretory and membrane proteins (SMPs) are a large potential source of biomarkers as they can be used to indicate cell types and conditions. SMPs have been shown to be ideal candidates for several clinically approved drug regimens including for cancer. This study aimed at performing a functional analysis of SMPs within different cancer subtypes to provide great clinical targets for potential prognostic, diagnostic and the therapeutics use. Using an innovative majority decision-based algorithm and transcriptomic data spanning 5 cancer types and over 3000 samples, we quantified the relative difference in SMPs gene expression compared to normal adjacent tissue. A detailed deep data mining analysis revealed a consistent group of downregulated SMP isoforms, enriched in hematopoietic cell lineages (HCL), in multiple cancer types. HCL-associated genes were frequently downregulated in successive cancer stages and high expression was associated with good patient prognosis. In addition, we suggest a potential mechanism by which cancer cells suppress HCL signaling by reducing the expression of immune-related genes. Our data identified potential biomarkers for the cancer immunotherapy. We conclude that our approach may be applicable for the delineation of other types of cancer and illuminate specific targets for therapeutics and diagnostics.

Proteomic Characterization of Colorectal Cancer Cells versus Normal-Derived Colon Mucosa Cells: Approaching Identification of Novel Diagnostic Protein Biomarkers in Colorectal Cancer.

In the western world, colorectal cancer (CRC) is the third most common cause of cancer-related deaths. Survival is closely related to the stage of cancer at diagnosis striking the clinical need for biomarkers capable of early detection. To search for possible biological parameters for early diagnosis of CRC we evaluated protein expression for three CREC (acronym: Cab45, reticulocalbin, ERC-55, calumenin) proteins: reticulocalbin, calumenin, and ERC-55 in a cellular model consisting of a normal derived colon mucosa cell line, NCM460, and a primary adenocarcinoma cell line of the colon, SW480. Furthermore, this cellular model was analyzed by a top-down proteomic approach, 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for novel putative diagnostic markers by identification of differentially expressed proteins between the two cell lines. A different colorectal carcinoma cell line, HCT 116, was used in a bottom-up proteomic approach with label-free quantification (LFQ) LC–MS/MS. The two cellular models gave sets of putative diagnostic CRC biomarkers. Various of these novel putative markers were verified with increased expression in CRC patient neoplastic tissue compared to the expression in a non-involved part of the colon, including reticulocalbin, calumenin, S100A6 and protein SET. Characterization of these novel identified biological features for CRC patients may have diagnostic potential and therapeutic relevance in this malignancy characterized by a still unmet clinical need.

MicroRNA expression integrated analysis and identification of novel biomarkers in small cell lung cancer: a meta-analysis.

BackgroundSmall cell lung cancer (SCLC) is an aggressive and recalcitrant cancer. In recent years, studies focused on the abnormal expression of microRNA which has proven valuable in terms of prognosis, diagnosis and treatment in SCLC. To address the limitations of independent studies data, a meta-analysis seems necessary for further exploration of microRNA as biological target and regulatory factor in SCLC.MethodsWe performed comprehensive literature retrieval in GEO database and EBI ArrayExpress database. The microRNA expression data was extracted from 4 related researches (GSE15008, GSE74190, GSE19945, GSE77380), which was obtained from GEO database. In each included study, the R. Affymetrix Expression Console’s Limma package and RMA algorithms were used to screen for raw data for gene chip quality control, standardization, log2 conversion and differential expression of the gene chip, respectively. Significant microRNA meta-signatures were identified by Robust Rank Aggregation method. Subsequently, gene ontology (GO) enrichment analysis and pathway analysis were performed using bioinformatics tools.ResultsWe found a significant microRNA meta-signature of six up-regulated (hsa-miR-182-5p, hsa-miR-96-5p, hsa-miR-7-5p, hsa-miR-301b-3p, hsa-miR-130b-3p, hsa-miR-210-3p) and four down-regulated (hsa-miR-126-3p, hsa-miR-451a, hsa-miR-145-5p, hsa-miR-486-5p) microRNA s in meta-analysis approaches. GO analysis showed that target gene of meta-signatures microRNA was mainly enriched in endosome, chordate embryonic development and transforming growth factor beta receptor. The related functional gene of microRNA meta signature synergistically targeting SCLC signaling pathway was confirmed by enrichment analysis. In particular, neurotrophin and TGF-beta signaling pathway play the most important roles in the pathway network.ConclusionsOur study identified 10 highly significant and consistently dysregulated microRNA s from 4 datasets, which offering convincing molecular targets and regulatory factors in future research of SCLC.

Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.

Background The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

Saliva as a first-line diagnostic tool: A spectral challenge for identification of cancer biomarkers

Abstract The paper concentrates on the role of saliva in the early diagnosis of salivary gland tumor. Due to the still not fully understood etiology of head and neck cancer, the fundamental objective of this study is to find tumor markers of salivary gland tumor progression. This work demonstrates the feasibility of Attenuated Total Reflection Fourier Transform Infrared spectroscopy (ATR-FTIR) to track spectral variations between saliva samples derived from healthy volunteers and from salivary gland tumor (tumor mixus, TM) patients. Furthermore, a Real-Time Polymerase Chain Reaction (RT-PCR) has been used to detect a selected genes expression associated with neoplasm changes. The obtained results imply that spectral signals attributed to the amide I/II (secondary structure of protein), carbohydrates and inorganic phosphates oscillations are the most sensitive to alterations associated with the salivary gland cancer progression. Several spectroscopic biomarkers have been indicated as potential predictors of salivary gland tumor development. Additionally, the RT-PCR results reveal the increased level of Bcl-2 factor in salivary gland tumor patients' samples which may be related to inhibition of apoptosis process and increasing uncontrolled cell proliferation. Such combination of physico-chemical methods is a unique approach towards better understanding the tumor's etiology and early diagnosis problem. Based on the promising findings presented in this article, it could be concluded that saliva fluid has a great potential to be used as a first-line diagnostic tool in patients with suspicion of salivary gland tumor.

Design of high stability thin-film transistor biosensor for the diagnosis of bladder cancer

Bladder cancer is the most common malignant tumor in the urinary system, with high morbidity, mortality and recurrence after surgery. However, current bladder cancer urine diagnosis methods are limited by the low accuracy and specificity due to the low abundance of bladder cancer biomarkers in the urine with complex biological environments. Herein, we present a high stability indium gallium zinc oxide field effect transistor (IGZO-FET) biosensor for efficient identification of bladder cancer biomarkers from human urine samples. The recognition molecular functionalized IGZO-FET biosensor exhibits stable electronic and sensing performance due to the large-area fabrication of IGZO thin-film FET. Owing to the excellent electrical performance of IGZO-FET, the IGZO-FET biosensor exhibits high sensitivity and extremely low detection limit (2.7 amol/L) towards bladder cancer biomarkers. The IGZO-FET biosensor is also able to directly detect bladder tumor biomarker in human urine with high sensitivity and specificity, and could differentiate bladder cancer patients’ urine samples from healthy donors effectively. These results indicate that our designed high-performance biosensor shows great potential in the application of portable digital bladder cancer diagnosis devices.

FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers.

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

Screening and Identification of Key Biomarkers for Bladder Cancer: A Study Based on TCGA and GEO Data.

Bladder cancer (BLCA) is a common malignant cancer, and it is the most common genitourinary cancer in the world. The recurrence rate is the highest of all cancers, and the treatment of BLCA has only slightly improved over the past 30 years. Genetic and environmental factors play an important role in the development and progression of BLCA. However, the mechanism of cancer development remains to be proven. Therefore, the identification of potential oncogenes is urgent for developing new therapeutic directions and designing novel biomarkers for the diagnosis and prognosis of BLCA. Based on this need, we screened overlapping differentially expressed genes (DEG) from the GSE7476, GSE13507, and TCGA BLCA datasets. To identify the central genes from these DEGs, we performed a protein-protein interaction network analysis. To investigate the role of DEGs and the underlying mechanisms in BLCA, we performed Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) analysis; we identified the hub genes via different evaluation methods in cytoHubba and then selected the target genes by performing survival analysis. Finally, the relationship between these target genes and tumour immunity was analysed to explore the roles of these genes. In summary, our current studies indicate that both cell division cycle 20 (CDC20) and abnormal spindle microtubule assembly (ASPM) genes are potential prognostic biomarkers for BLCA. It may also be a potential immunotherapeutic target with future clinical significance.

Identification of MFI2-AS1, a Novel Pivotal lncRNA for Prognosis of Stage III/IV Colorectal Cancer.

Long noncoding RNAs (lncRNAs) have been shown to play pivotal role in pathogenesis and prognosis of cancers. Identification of novel clinical biomarkers in advanced stage colorectal cancer (CRC) is warranted. To identify potential lncRNAs associated with progression of stage III/IV CRC and illuminate regulatory mechanisms. Differentially expressed lncRNAs, mRNAs and miRNAs (DElncRNAs, DEmRNAs, and DEmiRNAs) were extracted between stage III/IV CRC and normal tissues. We used DEGs to construct a ceRNA network and analyzed correlations between key lncRNAs and overall survivals (OS) of stage III/IV CRC patients. Weighted gene co-expression network analysis (WGCNA) was applied to a pivotal lncRNA. We conducted functional enrichment analysis on target genes and constructed lncRNA-TF-mRNA network by overlapping mRNAs co-expressed with the key lncRNA and target genes of transcriptional factors (TFs). A total of 26 DElncRNAs, 398 DEmiRNAs, 2155 DEmRNAs were identified. A ceRNA network was constructed with 16 lncRNAs, 20 miRNAs, and 59 mRNAs, in which MFI2-AS1 exhibited promising diagnostic efficiency. (AUC was 0.938.) MFI2-AS1 was negatively correlated to OS of stage III/IV CRC patients (P value < 0.05). KEGG analysis showed potential mRNA targets of MFI2-AS1 mainly involved in cell cycle and cytokine-cytokine receptor interaction. We identified 17 potential TFs of MFI2-AS1 and built a lncRNA-TF-mRNA network. Our study provides novel insights into lncRNAs associated regulatory networks and reveals a promising lncRNA biomarker, MFI2-AS1, as an independent prognostic indicator and potential therapeutic target for CRC.

Silencing of synaptotagmin 13 inhibits tumor growth through suppressing proliferation and promoting apoptosis of colorectal cancer cells.

The treatment of colorectal cancer is currently hampered by the lack of early detection technology. The identification of molecular biomarkers for colorectal cancer is crucial for improving prognosis. Synaptotagmin (SYT) 13 has been reported to be associated with several human tumors, but its role in colorectal cancer remains elusive. In the present study, immunohistochemistry was utilized to detect the expression of SYT13 in colorectal cancer tissues and cells. MTT, colony formation, wound healing and Transwell assays were conducted to evaluate the effect of SYT13 knockdown on the biological behavior of RKO and HCT116 cells. Cell apoptosis and cell cycle profiles were detected by FACS. A mouse xenograft model was constructed to investigate the effect of SYT13 on colorectal cancer in vivo. The results indicated that SYT13 was upregulated in colorectal tumor tissues compared with paracancerous tissues. Silencing of SYT13 inhibited the proliferation, colony formation, migration and invasion ability of RKO and HCT116 cells. Moreover, SYT13 knockdown arrested the cell cycle in the G2 phase, thus inducing cell apoptosis. The in vivo experiments also demonstrated the inhibitory effect of SYT13 on tumor growth. In conclusion, the present study demonstrated that SYT13 may act as a promoter in the development and progression of colorectal cancer and, therefore, may be of value as a target for the development of novel treatment strategies.

Plasma proteomics-based identification of novel biomarkers in early gastric cancer

BackgroundIdentification and treatment in the early stage can significantly improve the prognosis of gastric cancer (GC). However, to date, there is still no ideal biomarker that can be used for the screening of early stage GC (EGC). The proteomics supported by mass spectrometry offers more possibilities for discovering tumor biomarkers. The aim of this study was to explore candidate protein biomarkers for EGC screening with mass spectrometry and bioinformatics technology. MethodsPlasma samples were collected from 15 EGC patients and 15 healthy controls. After a selective immune-depletion to remove high abundance proteins, plasma samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with the tandem mass tags (TMT) labeling. ResultsA total of 2040 proteins were identified, and 11 proteins were found to be differentially expressed. The results of the logistic regression model and orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA) model showed that the changed proteins identified by plasma proteomics could help distinguish EGC patients from healthy controls. ConclusionThe proteins identified by plasma proteomics using LC-MS/MS combined with TMT labeling could help distinguish EGC from healthy controls.

Screening and identification of key biomarkers in prostate cancer using bioinformatics.

Prostate cancer (PCa) is the second most common cancer amongst males worldwide. In the current study, microarray datasets GSE3325 and GSE6919 from the Gene Expression Omnibus database were screened to identify candidate genes that are associated with the progression of PCa. A total of 273 differentially expressed genes (DEGs) were identified, which included 173 downregulated genes and 100 upregulated genes, and a protein-protein interaction network was constructed using Search Tool for the Retired of Interacting Genes. The enriched functions and pathways of the identified DEGs included cell adhesion, the negative regulation of cell proliferation, protein binding and focal adhesion. A total of 8 hub genes were identified, of which PDZ binding kinase, Krüppel-like factor 4, collagen type XII α-1 chain, RAP1A and RAP39B were indicated to be associated with the progression and recurrence of PCa. In conclusion, the DEGs and hub genes identified in the present study may aid in determining the molecular mechanisms associated with PCa carcinogenesis and progression.

Screening and Identification of Key Biomarkers in Pancreatic Cancer: Evidence from Bioinformatic Analysis.

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

Identification of Molecular Biomarkers for Thyroid Cancer

Identification of Molecular Biomarkers for Thyroid Cancer

Microtubule-associated tumor suppressors as prognostic biomarkers in breast cancer.

Breast cancer is the most common malignancy in women worldwide. Although important therapeutic progress was achieved over the past decade, this disease remains a public health problem. In light of precision medicine, the identification of new prognostic biomarkers in breast cancer is urgently needed to stratify populations of patients with poor clinical outcome who may benefit from new personalized therapies. The microtubule cytoskeleton plays a pivotal role in essential cellular functions and is an interesting target for cancer therapy. Microtubule assembly and dynamics are regulated by a wide range of microtubule-associated proteins (MAPs), some of which have oncogenic or tumor suppressor effects in breast cancer. This review covers current knowledge on microtubule-associated tumor suppressors (MATS) in breast cancer and their potential value as prognostic biomarkers. We present recent studies showing that combinatorial expression of ATIP3 and EB1, two microtubule-associated biomarkers with tumor suppressor and oncogenic effects, respectively, improves breast cancer prognosis compared to each biomarker alone. These findings are discussed regarding the increasing complexity of protein networks composed of MAPs that coordinate microtubule dynamics and functions. Further studies are warranted to evaluate the prognostic value of combined expression of different MATS and their interacting partners in breast cancer.

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives.

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.