GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer.

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.

An Exosome-based Transcriptomic Signature for Noninvasive, Early Detection of Patients With Pancreatic Ductal Adenocarcinoma: A Multicenter Cohort Study.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) incidence is rising worldwide, and most patients present with an unresectable disease at initial diagnosis. Measurement of carbohydrate antigen 19-9 (CA19-9) levels lacks adequate sensitivity and specificity for early detection; hence, there is an unmet need to develop alternate molecular diagnostic biomarkers for PDAC. Emerging evidence suggests that tumor-derived exosomal cargo, particularly micro RNAs (miRNAs), offer an attractive platform for the development of cancer-specific biomarkers. Herein, genomewide profiling in blood specimens was performed to develop an exosome-based transcriptomic signature for noninvasive and early detection of PDAC. METHODS: Small RNA sequencing was undertaken in a cohort of 44 patients with an early-stage PDAC and 57 nondisease controls. Using machine-learning algorithms, a panel of cell-free (cf) and exosomal (exo) miRNAs were prioritized that discriminated patients with PDAC from control subjects. Subsequently, the performance of the biomarkers was trained and validated in independent cohorts (n = 191) using quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays. RESULTS: The sequencing analysis initially identified a panel of 30 overexpressed miRNAs in PDAC. Subsequently using qRT-PCR assays, the panel was reduced to 13 markers (5 cf- and 8 exo-miRNAs), which successfully identified patients with all stages of PDAC (area under the curve [AUC] = 0.98 training cohort; AUC = 0.93 validation cohort); but more importantly, was equally robust for the identification of early-stage PDAC (stages I and II; AUC = 0.93). Furthermore, this transcriptomic signature successfully identified CA19-9 negative cases (<37 U/mL; AUC = 0.96), when analyzed in combination with CA19-9 levels, significantly improved the overall diagnostic accuracy (AUC = 0.99 vs AUC = 0.86 for CA19-9 alone). CONCLUSIONS: In this study, an exosome-based liquid biopsy signature for the noninvasive and robust detection of patients with PDAC was developed.

A microRNA-based liquid biopsy signature for the early detection of esophageal squamous cell carcinoma: a retrospective, prospective and multicenter study.

BACKGROUND: Currently, there is no clinically relevant non-invasive biomarker for early detection of esophageal squamous cell carcinoma (ESCC). Herein, we established and evaluated a circulating microRNA (miRNA)-based signature for the early detection of ESCC using a systematic genome-wide miRNA expression profiling analysis. METHODS: We performed miRNA candidate discovery using three ESCC tissue miRNA datasets (n = 108, 238, and 216) and the candidate miRNAs were confirmed in tissue specimens (n = 64) by qRT-PCR. Using a serum training cohort (n = 408), we conducted multivariate logistic regression analysis to develop an ESCC circulating miRNA signature and the signature was subsequently validated in two independent retrospective and two prospective cohorts. RESULTS: We identified eighteen initial miRNA candidates from three miRNA expression datasets (n = 108, 238, and 216) and subsequently validated their expression in ESCC tissues. We thereafter confirmed the overexpression of 8 miRNAs (miR-103, miR-106b, miR-151, miR-17, miR-181a, miR-21, miR-25, and miR-93) in serum specimens. Using a serum training cohort, we developed a circulating miRNA signature (AUC:0.83 [95%CI:0.79-0.87]) and the diagnostic performance of the miRNA signature was confirmed in two independent validation cohorts (n = 126, AUC:0.80 [95%CI:0.69-0.91]; and n = 165, AUC:0.89 [95%CI:0.83-0.94]). Finally, we demonstrated the diagnostic performance of the 8-miRNA signature in two prospective cohorts (n = 185, AUC:0.92, [95%CI:0.87-0.96]); and (n = 188, AUC:0.93, [95%CI:0.88-0.97]). Importantly, the 8-miRNA signature was superior to current clinical serological markers in discriminating early stage ESCC patients from healthy controls (p < 0.001). CONCLUSIONS: We have developed a novel and robust circulating miRNA-based signature for early detection of ESCC, which was successfully validated in multiple retrospective and prospective multinational, multicenter cohorts.

Diagnostic efficacy of circular RNAs as noninvasive, liquid biopsy biomarkers for early detection of gastric cancer.

BACKGROUND: Majority of gastric cancers (GC) are diagnosed at advanced stages which contributes towards their poor prognosis. In view of this clinical challenge, identification of non-invasive biomarker for early diagnosis is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy based assay by using circular RNAs (circRNAs) as molecular biomarkers for early detection of GC. METHODS: We performed systematic biomarker discovery and validation of the candidate circRNAs in matched tissue specimens of GC and adjacent normal mucosa. Next, we translated the discovered circRNA based biomarker panel into serum samples in a training and validation cohort of GC patients (n = 194) and non-disease controls (n = 94) and evaluated their diagnostic performance. In addition, we measured the expression of circRNAs in serum samples of pre- and post-surgical GC patients and evaluated the specificity of circRNAs biomarker panel with respect to other gastro-intestinal (GI) malignancies. RESULTS: We identified 10-circRNAs in the discovery phase with subsequent validation in a pilot cohort of GC tissue specimens. Using a training cohort of patients, we developed an 8-circRNA based risk-prediction model for the diagnosis of GC. We observed that our biomarker panel robustly discriminated GC patients from non-disease controls with an AUC of 0.87 in the training, and AUC of 0.83 in the validation cohort. Notably, the biomarker panel could robustly identify even early-stage GC patients, regardless of their tumor histology (diffuse vs. intestinal). The decreased expression of circRNAs in post-surgery serum specimens indicated their tumor-specificity and their potential source of origin in the systemic circulation. CONCLUSIONS: We identified a panel of 8-circRNAs as non-invasive, liquid-biopsy biomarkers which might serve as potential diagnostic biomarkers for the early detection of GC.

Circulating miRNA Signature Predicts Response to Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer.

Patients with locally advanced rectal cancer (LARC) are recommended to receive preoperative chemoradiotherapy (PCRT) followed by surgery. Response to PCRT varies widely: 60%-70% of patients with LARC do not derive therapeutic benefit from PCRT, whereas 15%-20% of patients achieve pathologic complete response (pCR). We sought to develop a liquid biopsy assay for identifying response to PCRT in patients with LARC. MATERIALS AND METHODS: We analyzed two genome-wide microRNA (miRNA) expression profiling data sets from tumor tissue samples for in silico discovery (GSE68204) and validation (GSE29298). We prioritized biomarkers in pretreatment plasma specimens from clinical training (n = 41; 15 responders and 26 nonresponders) and validation (n = 65; 29 responders and 36 nonresponders) cohorts of patients with LARC. We developed an integrated miRNA panel and established a risk assessment model, which was combined with the miRNA panel and carcinoembryonic antigen levels. RESULTS: Our comprehensive discovery effort identified an 8-miRNA panel that robustly predicted response to PCRT, with an excellent accuracy in the discovery (area under the curve [AUC] = 0.95) and validation (AUC = 0.92) cohorts. We successfully established a circulating miRNA panel with remarkable diagnostic accuracy in the clinical training (AUC = 0.82) and validation (AUC = 0.81) cohorts. Moreover, the predictive accuracy of the panel was significantly superior to conventional clinical factors in both cohorts (P < .01) and the risk assessment model was superior (AUC = 0.83). Finally, we applied our model to detect patients with pathologic complete response and showed that it was dramatically superior to currently used pathologic features (AUC = 0.92). CONCLUSION: Our novel risk assessment signature for predicting response to PCRT has a potential for clinical translation as a liquid biopsy assay in patients with LARC.

Assessment of the Diagnostic Efficiency of a Liquid Biopsy Assay for Early Detection of Gastric Cancer.

Importance: Noninvasive detection of early-stage disease is a key strategy for reducing gastric cancer (GC)-associated patient mortality. Objective: To establish a novel, noninvasive, microRNA (miRNA)-based signature for the early detection of GC using a comprehensive biomarker discovery approach with retrospective and prospective validation. Design, Setting, and Participants: This diagnostic study was conducted in 4 phases using publicly available genome sequences and tissue samples from patients at an academic medical center in Japan, and validated with retrospective multicenter cohorts of patients with GC. Three tissue miRNA data sets were used to identify a miRNA signature that discriminated GC vs normal tissues. The robustness of this signature was assessed in serum from 2 retrospective cohorts of patients with GC. A risk-scoring model was derived, then the performance of the miRNA signature was evaluated in a prospective cohort of patients with GC. The robustness of the miRNA signature was compared with current blood-based markers, and a cost-effectiveness analysis of the miRNA signature against the current practice of endoscopy was performed. All clinical samples used for this study were collected and data analyzed between April 1997 and March 2018. Main Outcomes and Measures: Assessment of diagnostic efficiency on the basis of area under the curve (AUC), specificity, and sensitivity. Results: The data sets for the genome-wide expression profiling analysis stage included 598 total patient samples (284 [55.4%] from men; mean [SE] patient age, 65.7 [0.5] years). The resulting 10-miRNA signature was validated in 2 retrospective GC serum cohorts (586 patients; 348 [59.4%] men, mean [SE] age, 66.0 [0.7] years), which led to the establishment of a 5-miRNA signature (AUC, 0.90; 95% CI, 0.85-0.94) that also exhibited high levels of diagnostic performance in patients with stage I disease (AUC, 0.89; 95% CI, 0.83-0.94). A risk-scoring model was derived and the assay was optimized to a minimal number of miRNAs. The performance of the resulting 3-miRNA signature was then validated in a prospective cohort of patients with GC (349 patients; 124 [70.5%] men, median [range] age, 66.0 [0.66] years). The final 3-miRNA signature (miR-18a, miR-181b, and miR-335) exhibited high diagnostic accuracy in all stages of patients (AUC, 0.86; 95% CI 0.83-0.90), including in patients with stage I disease (AUC, 0.85; 95% CI, 0.79-0.91). Furthermore, this miRNA signature was superior to currently used blood markers and outperformed the endoscopic screening in a cost-effectiveness analysis (incremental cost-effectiveness ratio, CNY ¥16162.5 per quality-adjusted life-year [USD $2304.80 per quality-adjusted life-year]). Conclusions and Relevance: These results suggest the potential clinical significance of the 3-miRNA signature as a noninvasive, cost-effective, and facile assay for the early detection of GC.

iRGvalid: A Robust in silico Method for Optimal Reference Gene Validation.

BACKGROUND: Appropriate reference genes are critical to accurately quantifying relative gene expression in research and clinical applications. Numerous efforts have been made to select the most stable reference gene(s), but a consensus has yet to be achieved. In this report, we propose an in silico reference gene validation method, iRGvalid, that can be used as a universal tool to validate the reference genes recommended from different resources so as to identify the best ones without a need for any wet lab validation tests. METHODS: iRGvalid takes advantage of high throughput gene expression data and is built on a double-normalization strategy. First, the expression level of each individual gene is normalized against the total gene expression level of each sample, followed by a target gene normalization to the candidate reference gene(s). Linear regression analysis is then performed between the pre- and post- normalized target gene across the whole sample set to evaluate the stability of the reference gene(s), which is positively associated with the Pearson correlation coefficient, Rt. The higher the Rt value, the more stable the reference gene. We applied iRGvalid to 14 candidate reference genes to validate and identify the most stable reference genes in four cancer types: lung adenocarcinoma, breast cancer, colon adenocarcinoma, and nasopharyngeal cancer. The stability of the reference gene is evaluated both individually and in groups of all possible combinations. RESULTS: Highly stable reference genes resulted in high Rt values regardless of the target gene used. The highest stability was achieved with a specific combination of 3 to 6 reference genes. A few genes were among the best reference genes across the cancer types studied here. CONCLUSION: iRGvalid provides an easy and robust method to validate and identify the most stable reference gene or genes from a pool of candidate reference genes. The inclusivity of large expression data sets as well as the direct comparison of candidate reference genes makes it possible to identify reference genes with universal quality. This method can be used in any other gene expression studies when large cohorts of expression data are available.

Cleavage and Polyadenylation Specific Factor 1 Promotes Tumor Progression via Alternative Polyadenylation and Splicing in Hepatocellular Carcinoma.

Alternative polyadenylation (APA) is an important post-transcriptional regulatory mechanism required for cleavage and polyadenylation (CPA) of the 3' untranslated region (3' UTR) of mRNAs. Several aberrant APA events have been reported in hepatocellular carcinoma (HCC). However, the regulatory mechanisms underlying APA remain unclear. In this study, we found that the expression of cleavage and polyadenylation specific factor 1 (CPSF1), a major component of the CPA complex, was significantly increased in HCC tissues and correlated with unfavorable survival outcomes. Knockdown of CPSF1 inhibited HCC cell proliferation and migration, whereas overexpression of CPSF1 caused the opposite effect. Based on integrative analysis of Iso-Seq and RNA-seq data from HepG2.2.15 cells, we identified a series of transcripts with differential 3' UTR lengths following the knockdown of CPSF1. These transcripts were related to the biological functions of gene transcription, cytoskeleton maintenance, and endomembrane system transportation. Moreover, knockdown of CPSF1 induced an increase in alternative splicing (AS) events in addition to APA. Taken together, this study provides new insights into our understanding of the post-transcriptional regulatory mechanisms in HCC and implies that CPSF1 may be a potential prognostic biomarker and therapeutic target for HCC.

An integrated workflow for biomarker development using microRNAs in extracellular vesicles for cancer precision medicine.

EV-miRNAs are microRNA (miRNA) molecules encapsulated in extracellular vesicles (EVs), which play crucial roles in tumor pathogenesis, progression, and metastasis. Recent studies about EV-miRNAs have gained novel insights into cancer biology and have demonstrated a great potential to develop novel liquid biopsy assays for various applications. Notably, compared to conventional liquid biomarkers, EV-miRNAs are more advantageous in representing host-cell molecular architecture and exhibiting higher stability and specificity. Despite various available techniques for EV-miRNA separation, concentration, profiling, and data analysis, a standardized approach for EV-miRNA biomarker development is yet lacking. In this review, we performed a substantial literature review and distilled an integrated workflow encompassing important steps for EV-miRNA biomarker development, including sample collection and EV isolation, EV-miRNA extraction and quantification, high-throughput data preprocessing, biomarker prioritization and model construction, functional analysis, as well as validation. With the rapid growth of "big data", we highlight the importance of efficient mining of high-throughput data for the discovery of EV-miRNA biomarkers and integrating multiple independent datasets for in silico and experimental validations to increase the robustness and reproducibility. Furthermore, as an efficient strategy in systems biology, network inference provides insights into the regulatory mechanisms and can be used to select functionally important EV-miRNAs to refine the biomarker candidates. Despite the encouraging development in the field, a number of challenges still hinder the clinical translation. We finally summarize several common challenges in various biomarker studies and discuss potential opportunities emerging in the related fields.