CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer.

MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising diagnostic and prognostic biomarkers for human cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive biomarkers for early cancer detection. Novel and user-friendly tools are desperately needed to facilitate data mining of the vast amount of miRNA expression data from The Cancer Genome Atlas (TCGA) and large-scale circulating miRNA profiling studies. To fill this void, we developed CancerMIRNome, a comprehensive database for the interactive analysis and visualization of miRNA expression profiles based on 10 554 samples from 33 TCGA projects and 28 633 samples from 40 public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and machine learning algorithms have been packaged in CancerMIRNome, allowing for the pan-cancer analysis of a miRNA of interest across multiple cancer types and the comprehensive analysis of miRNome profiles to identify dysregulated miRNAs and develop diagnostic or prognostic signatures. The data analysis and visualization modules will greatly facilitate the exploit of the valuable resources and promote translational application of miRNA biomarkers in cancer. The CancerMIRNome database is publicly available at http://bioinfo.jialab-ucr.org/CancerMIRNome.

Evaluation of a 55-gene classifier as a prognostic biomarker for adjuvant chemotherapy in stage III colon cancer patients.

BACKGROUND: Adjuvant chemotherapy reduces the risk of recurrence of stage III colon cancer (CC). However, more effective prognostic and predictive biomarkers are needed for better treatment stratification of affected patients. Here, we constructed a 55-gene classifier (55GC) and investigated its utility for classifying patients with stage III CC. METHODS: We retrospectively identified patients aged 20-79 years, with stage III CC, who received adjuvant chemotherapy with or without oxaliplatin, between the years 2009 and 2012. RESULTS: Among 938 eligible patients, 203 and 201 patients who received adjuvant chemotherapy with and without oxaliplatin, respectively, were selected by propensity score matching. Of these, 95 patients from each group were analyzed, and their 5-year relapse-free survival (RFS) rates with and without oxaliplatin were 73.7 and 77.1%, respectively. The hazard ratios for 5-year RFS following adjuvant chemotherapy (fluoropyrimidine), with and without oxaliplatin, were 1.241 (95% CI, 0.465-3.308; P = 0.67) and 0.791 (95% CI, 0.329-1.901; P = 0.60), respectively. Stratification using the 55GC revealed that 52 (27.3%), 78 (41.1%), and 60 (31.6%) patients had microsatellite instability (MSI)-like, chromosomal instability (CIN)-like, and stromal subtypes, respectively. The 5-year RFS rates were 84.3 and 72.0% in patients treated with and without oxaliplatin, respectively, for the MSI-like subtype (HR, 0.495; 95% CI, 0.145-1.692; P = 0.25). No differences in RFS rates were noted in the CIN-like or stromal subtypes. Stratification by cancer sidedness for each subtype showed improved RFS only in patients with left-sided primary cancer treated with oxaliplatin for the MSI-like subtype (P = 0.007). The 5-year RFS rates of the MSI-like subtype in left-sided cancer patients were 100 and 53.9% with and without oxaliplatin, respectively. CONCLUSIONS: Subclassification using 55GC and tumor sidedness revealed increased RFS in patients within the MSI-like subtype with stage III left-sided CC treated with fluoropyrimidine and oxaliplatin compared to those treated without oxaliplatin. However, the predictive power of 55GC subtyping alone did not reach statistical significance in this cohort, warranting larger prospective studies. TRIAL REGISTRATION: The study protocol was registered in the University Hospital Medical Education Network (UMIN) clinical trial registry (UMIN study ID: 000023879 ).

HIV-Associated Cancer Biomarkers: A Requirement for Early Diagnosis.

Globally, HIV/AIDS and cancer are increasingly public health problems and continue to exist as comorbidities. The sub-Saharan African region has the largest number of HIV infections. Malignancies previously associated with HIV/AIDS, also known as the AIDS-defining cancers (ADCs) have been documented to decrease, while the non-AIDS defining cancer (NADCs) are on the rise. On the other hand, cancer is a highly heterogeneous disease and precision oncology as the most effective cancer therapy is gaining attraction. Among HIV-infected individuals, the increased risk for developing cancer is due to the immune system of the patient being suppressed, frequent coinfection with oncogenic viruses and an increase in risky behavior such as poor lifestyle. The core of personalised medicine for cancer depends on the discovery and the development of biomarkers. Biomarkers are specific and highly sensitive markers that reveal information that aid in leading to the diagnosis, prognosis and therapy of the disease. This review focuses mainly on the risk assessment, diagnostic, prognostic and therapeutic role of various cancer biomarkers in HIV-positive patients. A careful selection of sensitive and specific HIV-associated cancer biomarkers is required to identify patients at most risk of tumour development, thus improving the diagnosis and prognosis of the disease.

MicroRNA as a Prognostic and Diagnostic Marker in T-Cell Acute Lymphoblastic Leukemia.

T cell acute lymphoblastic leukemia (T-ALL) is a biologically and genetically heterogeneous disease with a poor prognosis overall and several subtypes. The neoplastic transformation takes place through the accumulation of numerous genetic and epigenetic abnormalities. There are only a few prognostic factors in comparison to B cell precursor acute lymphoblastic leukemia, which is characterized by a lower variability and more homogeneous course. The microarray and next-generation sequencing (NGS) technologies exploring the coding and non-coding part of the genome allow us to reveal the complexity of the genomic and transcriptomic background of T-ALL. miRNAs are a class of non-coding RNAs that are involved in the regulation of cellular functions: cell proliferations, apoptosis, migrations, and many other processes. No miRNA has become a significant prognostic and diagnostic factor in T-ALL to date; therefore, this topic of investigation is extremely important, and T-ALL is the subject of intensive research among scientists. The altered expression of many genes in T-ALL might also be caused by wide miRNA dysregulation. The following review focuses on summarizing and characterizing the microRNAs of pediatric patients with T-ALL diagnosis and their potential future use as predictive factors.

Identification of circulating biomarkers for differentiating patients with papillary thyroid cancers from benign thyroid tumors.

BACKGROUND: This study aimed to identify the potential circulating biomarkers of protein, mRNAs, and long non-coding RNAs (lncRNAs) to differentiate the papillary thyroid cancers from benign thyroid tumors. METHODS: The study population of 100 patients was classified into identification (10 patients with papillary thyroid cancers and 10 patients with benign thyroid tumors) and validation groups (45 patients with papillary thyroid cancers and 35 patients with benign thyroid tumors). The Sengenics Immunome Protein Array-combined data mining approach using the Open Targets Platform was used to identify the putative protein biomarkers, and their expression validated using the enzyme-linked immunosorbent assay. Next-generation sequencing by Illumina HiSeq was used for the detection of dysregulated mRNAs and lncRNAs. The website Timer v2.0 helped identify the putative mRNA biomarkers, which were significantly over-expressed in papillary thyroid cancers than in adjacent normal thyroid tissue. The mRNA and lncRNA biomarker expression was validated by a real-time polymerase chain reaction. RESULTS: Although putative protein and mRNA biomarkers have been identified, their serum expression could not be confirmed in the validation cohorts. In addition, seven lncRNAs (TCONS_00516490, TCONS_00336559, TCONS_00311568, TCONS_00321917, TCONS_00336522, TCONS_00282483, and TCONS_00494326) were identified and validated as significantly downregulated in patients with papillary thyroid cancers compared to those with benign thyroid tumors. These seven lncRNAs showed moderate accuracy based on the area under the curve (AUC = 0.736) of receiver operating characteristic in predicting the occurrence of papillary thyroid cancers. CONCLUSIONS: We identified seven downregulated circulating lncRNAs with the potential for predicting the occurrence of papillary thyroid cancers.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

The Immune Profile of Pituitary Adenomas and a Novel Immune Classification for Predicting Immunotherapy Responsiveness.

CONTEXT: The tumor immune microenvironment is associated with clinical outcomes and immunotherapy responsiveness. OBJECTIVE: To investigate the intratumoral immune profile of pituitary adenomas (PAs) and its clinical relevance and to explore a novel immune classification for predicting immunotherapy responsiveness. DESIGN, PATIENTS, AND METHODS: The transcriptomic data from 259 PAs and 20 normal pituitaries were included for analysis. The ImmuCellAI algorithm was used to estimate the abundance of 24 types of tumor-infiltrating immune cells (TIICs) and the expression of immune checkpoint molecules (ICMs). RESULTS: The distributions of TIICs differed between PAs and normal pituitaries and varied among PA subtypes. T cells dominated the immune microenvironment across all subtypes of PAs. The tumor size and patient age were correlated with the TIIC abundance, and the ubiquitin-specific protease 8 (USP8) mutation in corticotroph adenomas influenced the intratumoral TIIC distributions. Three immune clusters were identified across PAs based on the TIIC distributions. Each cluster of PAs showed unique features of ICM expression that were correlated with distinct pathways related to tumor development and progression. CTLA4/CD86 expression was upregulated in cluster 1, whereas programmed cell death protein 1/programmed cell death 1 ligand 2 (PD1/PD-L2) expression was upregulated in cluster 2. Clusters 1 and 2 exhibited a "hot" immune microenvironment and were predicted to exhibit higher immunotherapy responsiveness than cluster 3, which exhibited an overall "cold" immune microenvironment. CONCLUSIONS: We summarized the immune profile of PAs and identified 3 novel immune clusters. These findings establish a foundation for further immune studies on PAs and provide new insights into immunotherapy strategies for PAs.

The problem of axillary staging in breast cancer after neoadjuvant chemotherapy. Role of targeted axillary dissection and types of lymph node markers. / El problema de la estadificación axilar en el cáncer de mama tras quimioterapia neoadyuvante. Papel de la disección axilar dirigida y tipos de marcadores ganglionares.

Targeted axillary dissection (TAD) consists of a new axillary staging technique that combines sentinel lymph node biopsy (SLNB) and clipped lymph node biopsy (CLNB) in the same surgery, in order to re-stage patients with breast cancer and positive axillary lymph nodes undergoing neoadjuvant chemotherapy (NAQT). Prior to the NAQT, the affected lymph node is punctured and a solid marker is left inside echo-guided, in order to biopsy it in the subsequent surgery. There are numerous types of markers: metallic (steel, titanium or polyglycolic acid clips), radioiodine or ferromagnetic seeds, which differ in the method of location (wire, gamma-detection or magnetic probe). The aim of this study is to perform a systematic review about the current status of the TAD, as well as to explain the different techniques and types of axillary marking, based on the current available evidence.

Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset.

MicroRNAs(miRNAs) are maladjusted in multifarious malignant tumor and can be considered as both carcinogens and tumor-inhibiting factor. In the present study, we analyzed the miRNAs expression profiles and clinical information of 481 patients with head and neck squamous cell carcinoma (HNSCC) through the TCGA dataset to identify the prognostic miRNAs signature. A total of 114 significantly differentially expressed miRNAs (SDEMs) were identified, consisting of 60 up-adjusted and 54 down-adjusted miRNAs. The Kaplan-Meier survival method identified the prognostic function of 2 miRNAs (miR-4652-5p and miR-99a-3P). Univariate and multivariate Cox regression analyses indicated that the 2 miRNAs were significant prognostic elements of HNSCC. Furthermore, bioinformatic analysis was conducted by means of 4 online gene predicted toolkits to recognize the target genes, and enrichment analysis was performed on the target genes by DAVID. The outcomes depicted that target genes were correlated with calcium, as well as cell proliferation, circadian entrainment, EGFR, PI3K-Akt-mTOR, and P53 signaling pathways. Finally, the PPI network was conducted in view of STRING database and Cytoscape. Eight hub genes were identified by CytoHubba and MCODE app, respectively, CBL, SKP1, H2AFX, HGF, POLR2F, UBE2I, VAMP2, and GNAI2 genes. As a result, we identified 2 miRNAs signatures, 8 hub genes, and significant signaling pathways for estimating the prognosis of HNSCC. In order to further explore the molecular mechanism of HNSCC occurrence and development, more comprehensive basic and clinical studies are needed.

Molecular Classification of Diffuse Gliomas.

Technological advances in the field of molecular genetics have improved the ability to classify brain tumors into subgroups with distinct clinical features and important therapeutic implications. The World Health Organization's newest update on classification of gliomas (2016) incorporated isocitrate dehydrogenase 1 and 2 mutations, ATRX loss, 1p/19q codeletion status, and TP53 mutations to allow for improved classification of glioblastomas, low-grade and anaplastic gliomas. This paper reviews current advances in the understanding of diffuse glioma classification and the impact of molecular markers and DNA methylation studies on survival of patients with these tumors. We also discuss whether the classification and grading of diffuse gliomas should be based on histological findings, molecular markers, or DNA methylation subgroups in future iterations of the classification system.

Functions of circular RNAs and their potential applications in gastric cancer.

Introduction: In recent years, circular RNAs (circRNAs) have emerged in the field of RNA research and their biological functions are now being gradually identified. circRNAs are divided into three categories: exonic circular RNAs (ecircRNAs), exon-intron circular RNAs (EIciRNAs), and intronic circular RNAs (ciRNAs). The circular structure of circRNAs confers unique biological characteristics upon them, such as enhanced stability over linear RNAs.Areas covered: circRNAs function to competitively bind with microRNAs (miRNAs) and proteins, participate in protein coding, regulate transcription, and form pseudogenes after reverse transcription. In gastric cancer, the circRNA-miRNA-mRNA axis is the most studied mechanisms underlying gastric cancer occurrence and development. Some specific and sensitive circRNAs, such as hsa_circ_102958, hsa_circ_0000520, and hsa_circ_0001017 may have potential diagnostic potential in early-stage gastric cancer. Abnormal expression of some circRNAs, including circ-LMTK2, circ-PSMC3, and circ-DLST are associated with the development of gastric cancer. Other circRNAs, such as hsa_circ_0001368, circ-ZFR, and circ-ERBB2, may also play important roles in gastric cancer treatment.Expert opinion: Exploring the roles of circRNAs in gastric cancer occurrence and development will help us to elucidate the functions of circRNAs and develop potential tools for early diagnosis and effective treatment of gastric cancer.

A pilot investigation of a urinary metabolic biomarker discovery in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is the most common and lethal malignancy of the kidney. Distinguishing RCC from benign renal tumors and healthy controls is still a clinical challenge. Urine metabolomics has been used to identify biomarkers of clinical diseases. METHODS: In the present study, we explored the urine metabolomes of a cohort of 61 patients with renal tumors (39 RCC and 22 benign renal tumors) and 68 healthy controls using liquid chromatography coupled with high-resolution mass spectrometry (LC-MS). RESULTS: Metabolic profiling of urine could significantly differentiate RCC from healthy controls and benign renal tumors. Metabolic pathways, including lysine metabolism and phenylalanine metabolism, were found to be disturbed in the RCC group. Steroid hormone biosynthesis was significantly different between the benign tumor group and the RCC group. RCC biomarkers were further explored. A metabolite panel consisting of cortolone, testosterone and L-2-aminoadipate adenylate was discovered to have good ability of distinguishing RCC from benign tumors, with an AUC of 0.868 for tenfold cross-validation and 0.873 for the validation group. In addition, the panel of aminoadipic acid, 2-(formamido)-N1-(5-phospho-D-ribosyl) acetamidine and alpha-N-phenylacetyl-L-glutamine could distinguish the RCC group from the healthy control group, with an AUC of 0.841 for tenfold cross-validation and 0.894 for the validation group. CONCLUSIONS: This pilot study suggests that urine metabolomics may be useful in differentiating RCC from healthy controls and benign renal tumors.

Intratumoral Cytotoxic T-Lymphocyte Density and PD-L1 Expression Are Prognostic Biomarkers for Patients with Colorectal Cancer.

Background and objectives: Cytotoxic T-lymphocyte (CTL)-mediated inflammatory response to tumors plays a crucial role in preventing the progression of some cancers. Programmed cell death ligand 1 (PD-L1), a cell-surface glycoprotein, has been reported to repress T-cell-mediated immune responses against tumors. However, the clinical significance of PD-L1 in colorectal cancer (CRC) remains unclear. Our aim was to elucidate the prognostic significance of PD-L1 expression and CD8+ CTL density in CRC. Materials and methods: CD8 and PD-L1 immunostaining was conducted on 157 pathologic specimens from patients with CRC. The CD8+ CTL density and PD-L1 expression within the tumor microenvironment were assessed by immunohistochemistry. Results: Tumor invasion (pT) was significantly correlated with intratumoral (p = 0.011) and peritumoral (p = 0.016) CD8+ CTLs density in the tumor microenvironment. In addition, there was a significant difference in the intensity of CD8+ CTLs between patients with and without distant metastases (intratumoral p = 0.007; peritumoral p = 0.037, T-test). Lymph node metastasis (pN) and TNM stage were significantly correlated with PD-L1 expression in CRC cells (p = 0.015, p = 0.029, respectively). Multivariate analysis revealed a statistically significant relationship between the intratumoral CD8+ CTL density and disease-free survival (DFS) (hazard ratio [HR] 2.06; 95% confidence interval [CI]: 1.01-4.23; p = 0.043). The DFS was considerably shorter in patients with a high expression of PD-L1 in cancer cells than those with a low expression (univariate HR 2.55; 95% CI 1.50-4.34; p = 0.001; multivariate HR 0.48; 95% CI 0.28-0.82; p = 0.007). Conversely, patients with high PD-L1 expression in tumor-infiltrating lymphocytes had a longer DFS in both univariate analysis (HR 0.25; 95% CI: 0.14-0.44; p < 0.001) and multivariate analysis (HR 3.42; 95% CI: 1.95-6.01; p < 0.001). Conclusion: The CD8+ CTL density and PD-L1 expression are prognostic biomarkers for the survival of patients with CRC.

Discovery of Aberrant Alteration of Genome in Colorectal Cancer by Exome Sequencing.

BACKGROUND: This study analyzed multiple parameters including somatic single nucleotide variations (SNVs), Insertion/Deletions, significantly mutated genes (SMGs), copy number variations and frequently altered pathways aims to discover novel aberrances in the tumorigenesis of colorectal cancer (CRC). MATERIALS AND METHODS: Exome sequencing was performed on an Illumina platform to identify novel potential somatic variances in 34 paired tumor and adjacent normal tissues from 17 CRC patients. Results were compared with databases (dbSNP138, 1000 genomes SNP, Hapmap, Catalogue of Somatic Mutation of Cancer and ESP6500) and analyzed. MuSic software was used to identify SMGs. RESULTS: In total, 1,637 somatic SNVs in 17 analyzed tumors were identified. Only 7 SNVs were shared by more than 1 tumor, suggesting that over 99% of the analyzed SNVs were independent events. Mutation of KRAS p. G12D and ZNF717 p. L39V were the most common SNVs. Moreover, 10 SMGs namely KRAS, TP53, SMAD4, ZNF717, FBXW7, APC, ZNF493, CDR1, the Armadillo repeat containing 4 (ARMC4) and sulfate-modifying factor 2 (SUMF2) were found. Among those, ZNF717, ZNF493, CDR1, ARMC4 and SUMF2 were novel frequent genes in CRC. For copy number variations analysis, gains in 10q25.3, 1p31.1, 1q44, 10q23.33, 11p15.4 and 20q13.33, and loss of 3q21.3 and 3q29 were frequent aberrations identified in our results. CONCLUSIONS: We frequently found novel genes ZNF717, ZNF493, CDR1, ARMC4 and SUMF2 and gains in 10q25.3, which may be functional mutation in CRC. The high-frequency private events such as SNVs confirm the highly heterogeneous mutations found in CRCs. The mutated genes sites in different patients may vary significantly, which may also be more challenging for clinical treatment.

Proteomic Advances in Glial Tumors through Mass Spectrometry Approaches.

Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.

The Value of Plasma-Based MicroRNAs as Diagnostic Biomarkers for Ovarian Cancer.

BACKGROUND: Ovarian cancer (OC) is one of the most threatening diseases among women in the world. Plasma microRNAs (miRNAs) may serve as promising diagnostic biomarkers for patients with OC. MATERIALS AND METHODS: Using quantitative reverse transcription polymerase chain reaction (qRT-PCR) based on Exiqon panel, we identified 27 differentially expressed miRNAs from 2 OC pool samples and 1 normal control (NC) pool in the initial screening phase. Then we further validated the identified miRNAs through the training (32 OC vs. 34 NCs) and validation stages (69 OC vs. 66 NCs) using qRT-PCR. The expression levels of the miRNAs were also assessed in tissues and exosomes. RESULTS: Five plasma miRNAs (miR-205-5p, miR-145-5p, miR-10a-5p, miR-346 and miR-328-3p) were significantly overexpressed in OC in comparison with NCs. The areas under the receiver operating characteristic curve of the 5-miRNA panel were 0.788 for the training stage and 0.763 for the validation stage. The level of miR-205-5p has significantly different expression in patients with well-moderate histological grade compared with those with a poor grade (P = 0.012). The expression levels of the 5 miRNAs were also significantly upregulated in the exosomes of OC plasma samples (32 OC vs. 32 NCs). However, the expression of the 4 miRNAs (miR-145-5p, miR-10a-5p, miR-346 and miR-328-3p) was significantly lower in tumor samples than in normal tissues (22 OC vs. 22 NCs). CONCLUSIONS: The 5 plasma miRNAs may be noninvasive diagnostic biomarkers of OC. The plasma miR-205-5p level may reflect the change trend of the histological grade of OC patients.

Prognostic immunohistochemical biomarkers of chemotherapy efficacy in biliary tract cancer: A systematic review and meta-analysis.

INTRODUCTION: Chemotherapy is the mainstay of systemic treatment of biliary tract cancer (BTC). However, the treatment response to chemotherapy varies between patients. Currently, no prognostic biomarkers for chemotherapy efficacy have been considered for use in clinical practice. A systematic review was conducted to evaluate the prognostic value of immunohistochemical biomarkers for chemotherapy in patients with resected as well as with advanced BTC. METHOD: Medline and EMBASE databases were searched up to March 2017 for studies that evaluated biomarker expression by immunohistochemistry in resected or advanced BTC patients treated with chemotherapy. The primary endpoints were overall survival (OS) and disease or progression free survival (DFS or PFS). RESULT: Twenty-six studies, including a total of 1348 patients and 26 different biomarkers, met the inclusion criteria and were included in this review. The most frequently studied prognostic biomarkers in BTC were the human Equilibrative Nucleoside Transporter 1 (hENT1), Ribonucleotide Reductase M1 (RRM1), and excision repair cross-complementation 1 (ERCC1). In the meta-analysis of patients treated with gemcitabine-based chemotherapy, high hENT1 expression was associated with longer OS (HR 0.43, 95% CI: 0.28 to 0.64) and DFS/PFS (HR 0.45, 95% CI: 0.33 to 0.61). CONCLUSION: hENT1 is a promising prognostic biomarker for gemcitabine-based chemotherapy in resected as well as in advanced BTC and should be further validated for the selection of patients for chemotherapy.

A comparative characterization of the circulating miRNome in whole blood and serum of HCC patients.

miRNAs are considered promising non-invasive biomarkers. Serum represents the major source of biomarkers, being readily accessible for many analytical tests. Recently, whole blood has drawn increasing interest in biomarker studies due to the presence of cancer-interacting cells and circulating cancer cells. Although Hepatocellular Carcinoma (HCC) is the seventh most frequent cancer worldwide, fragmented information exists regarding the miRNome characterization in blood and serum. We profiled the circulatory miRNome of paired serum and blood samples from 20 HCC patients, identifying 274 miRNA expressed in serum and 670 in blood, most of them still uncharacterized. 157 miRNA significantly differ between the two biofluids with 28 exclusively expressed in serum. Six miRNA clusters significantly characterize the two compartments, with the cluster containing miR-4484, miR-1281, miR-3178, miR-3613-3p, miR-4532, miR-4668-5p, miR-1825, miR-4487, miR-455-3p, miR-940 having the highest average expression in serum compared to blood. The ontological analysis revealed a role of these miRNAs in cancer progression, vascular invasion and cancer immune surveillance thought the regulation of DUSP1, PD-L1 and MUC1. Taken together, these results provide the most comprehensive contribution to date towards a complete miRNome profile of blood and serum for HCC patients. We show a consistent portion of circulatory miRNAs being still unknown.

Study of gene expressions' correlation structures in subgroups of Chronic Lymphocytic Leukemia Patients.

In chronic lymphocytic leukemia (CLL) the interaction of leukemic cells with the microenvironment ultimately affects patient outcome. CLL cases can be divided in two subgroups with different clinical course based on the mutational status of the immunoglobulin heavy variable (IGHV) genes: mutated CLL (M-CLL) and unmutated CLL (U-CLL). Since in CLL, the differentiated relation of genes between the two subgroups is of greater importance than the individual gene behavior, this paper investigates the differences between the groups' gene interactions, by comparing their correlation structures. Fisher's test and Zou's confidence intervals are employed to detect differences of correlation coefficients. Afterwards, networks created by the genes participating in most differences are estimated with the use of structural equation models (SEM). The analysis is enhanced with graph modeling in order to visualize the between group differences in the gene structures of the two subgroups. The applied methodology revealed stronger correlations between genes in U-CLL patients, a finding in line with related biomedical literature. Using SEM for multigroup analysis, different gene structures between the two groups of patients were confirmed. The study of correlation structures can facilitate the detection of differential gene expression profiles in CLL subgroups, with potential applications in other diseases. Comparison of correlations can be very useful in understanding the complex internal structural differences which signify the variations of a disease.

iTRAQ-Based Quantitative Proteomic Analysis Strengthens Transcriptomic Subtyping of Triple-Negative Breast Cancer Tumors.

Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis.