Luteolin Inhibits Lung Cancer Cell Migration by Negatively Regulating TWIST1 and MMP2 Through Upregulation of miR-106a-5p.

BACKGROUND: Luteolin, a common dietary flavonoid found in plants, has been shown to have anti-cancer properties. However, its exact mechanisms of action in non-small cell lung cancer (NSCLC) are still not fully understood, particularly its role in regulating broader genomic networks and specific gene targets. In this study, we aimed to elucidate the role of microRNAs (miRNAs) in NSCLC treated with luteolin, using A549 cells as a model system. MATERIALS AND METHODS: miRNA profiling was conducted on luteolin-treated A549 cells using Exiqon microarrays, with validation of selected miRNAs by qRT-PCR. Bioinformatic analysis identified the regulatory roles of miRNAs in biological processes and pathways following luteolin treatment. Computational algorithms were employed to identify potential target genes. A549 cells were transfected with miR-106a-5p mimic and inhibitor or their corresponding controls. The expression levels of 2 genes, twist basic helix-loop-helix transcription factor 1 (TWIST1) and matrix metallopeptidase 2 (MMP2), and cell migration were assessed. RESULTS: miRNA profiling identified 341 miRNAs, with 18 exhibiting significantly altered expression (P < 0.05). Subsequent qRT-PCR analysis confirmed altered expression of 6 selected miRNAs. KEGG and GO analyses revealed significant alterations in pathways and biological processes crucial for tumor biology. TWIST1 and MMP2, which both contain conserved miR-106a-5p binding sites, exhibited an inverse correlation with the expression levels of miR-106a-5p. Dual-luciferase reporter assays confirmed TWIST1 and MMP2 as direct targets of miR-106a-5p. Luteolin treatment led to a reduction in A549 cell migration, and this reduction was further amplified by the overexpression of miR-106a-5p. CONCLUSION: Luteolin inhibits A549 cell migration by modulating the miRNA landscape, shedding light on its mechanisms and laying the foundation for miRNA-based therapeutic approaches for NSCLC.

MicroRNA signature of small-cell lung cancer after treatment failure: impact on oncogenic targets by miR-30a-3p control.

Small-cell lung cancer (SCLC) is associated with a high mortality rate and limited treatment efficacy. We created a microRNA (miRNA) expression signature by RNA sequencing using specimens from patients with SCLC who had failed treatment. Forty-nine miRNAs were downregulated in SCLC tissues and were candidate tumor-suppressive miRNAs. In this signature, both guide and passenger strands were downregulated for five miRNAs (miR-30a, miR-34b, miR-34c, miR-223, and miR-4529). Recent studies have revealed that passenger strands of miRNAs are involved in the molecular pathogenesis of human cancer. Although miR-30a-5p (the guide strand) has been shown to be a tumor-suppressive miRNA in various types of cancers, miR-30a-3p (the passenger strand) function is not well characterized in SCLC cells. We investigated the functional significance of miR-30a-3p and oncogenic genes regulated by miR-30a-3p in SCLC cells. Ectopic expression assays showed that miR-30a-3p expression inhibited cell proliferation and induced cell cycle arrest and apoptosis in two SCLC cell lines. Furthermore, in silico database searches and gene expression assays identified 25 genes as putative targets of miR-30a-3p in SCLC cells. Luciferase reporter assays revealed that downstream neighbor of SON (DONSON) was directly regulated by miR-30a-3p in SCLC cells. Knockdown of DONSON induced cell cycle arrest in SCLC cells and DONSON overexpression were detected in SCLC clinical samples. Analyzing the regulatory networks of tumor-suppressive miRNAs may lead to the identification of therapeutic targets in SCLC.

A microRNA signature for clinical outcomes of pediatric ALL patients treated with TPOG protocols.

MicroRNA (miRNA) expression is reportedly associated with clinical outcomes in childhood acute lymphoblastic leukemia (ALL). Here, we aimed at investigating whether miRNA expression is associated with clinical outcomes in pediatric ALL patients treated with the Taiwan Pediatric Oncology Group (TPOG) protocols. The expression of 397 miRNAs was measured using stem-loop quantitative real-time polymerase chain reaction miRNA arrays in 60 pediatric ALL patients treated with TPOG-ALL-93 or TPOG-ALL-97 VHR (very high-risk) protocols. In order to identify prognosis-related miRNAs, original cohort was randomly split into the training and testing cohort in a 2:1 ratio, and univariate Cox proportional hazards regression was applied to identify associations between event-free survival (EFS) and expressions of miRNAs. Four prognosis-related miRNAs were selected and validated in another independent cohort composed of 103 patients treated with the TPOG-ALL-2002 protocol. Risk score, including the impact of four prognosis-related miRNAs, was calculated for each patients, followed by grouping patients into the high or low risk-score groups. Irrespective of the training, testing, or validation cohort, risk-score group was significantly associated with EFS and overall survival (OS). Risk-score group combining with clinical characteristics including the age onset (≥10 years), white blood cell counts (≥100 × 109/L), cell type (T- or B-cell), sex, and risk groups of the treatment protocols were used as predictors of EFS using the multivariate Cox proportional hazards regression. Results showed that the risk-score group was the strongest predictor. In the validation cohort, hazard ratios (HRs) of the risk-score group were 7.06 (95% CI=1.93-25.84, p-value =0.003) and 14.03 (95% CI=3.34-59.04, p-value =0.003) for EFS and OS, respectively. High risk-score group had higher risk of having poor prognosis and risk of death than that in the low risk group. Accuracy of the prediction model for 5-year EFS could reach 0.76. For the prediction of 5-year OS, accuracy was 0.75. In conclusion, a miRNA signature was associated with clinical outcomes in childhood ALL patients treated with TPOG protocols and might be a suitable prognostic biomarker.

A Cross-Comparison of High-Throughput Platforms for Circulating MicroRNA Quantification, Agreement in Risk Classification, and Biomarker Discovery in Non-Small Cell Lung Cancer.

Background: Circulating microRNAs (ct-miRs) are promising cancer biomarkers. This study focuses on platform comparison to assess performance variability, agreement in the assignment of a miR signature classifier (MSC), and concordance for the identification of cancer-associated miRs in plasma samples from non-small cell lung cancer (NSCLC) patients. Methods: A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miR expression using two sequencing-based and three quantitative reverse transcription PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. The MSC risk levels were compared with those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors, and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. The downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at the tissue level. Results: The intra-platform reproducibility was consistent, whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. The dysregulation and discriminatory power of miR-150-5p and miR-210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison with normal samples. Conclusion: Overall, our studies provide a large performance analysis between five different platforms for miR quantification, indicate the solidity of MSC classifier, and identify two noninvasive biomarkers for NSCLC.

Anti-Migratory Effect of Dipotassium Glycyrrhizinate on Glioblastoma Cell Lines: Microarray Data for the Identification of Key MicroRNA Signatures.

The nuclear factor kappa B (NF-κB) pathway has been reported to be responsible for the aggressive disease phenomenon observed in glioblastoma (GBM). Dipotassium glycyrrhizinate (DPG), a dipotassium salt of glycyrrhizic acid isolated from licorice, has recently demonstrated an anti-tumoral effect on GBM cell lines U87MG and T98G through NF-κB suppression by IRAK2- and TRAF6-mediating microRNA (miR)-16 and miR-146a, respectively. Thus, the present study aimed to evaluate the expression profiles of miRNAs related to NF-κB suppression in T98G GBM cell line after DPG exposure using miRNA microarray (Affymetrix Human miRNA 4.0A), considering only predicted miRNAs as NF-κB regulator genes. Additional assays using U251 and U138MG cells were performed to validate the array results. DPG cytotoxicity was determined by (4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, and cellular apoptosis was quantified by DNA fragmentation and terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. The anti-proliferative effect was observed by cell proliferation and wound-healing assays, and the sphere formation assay examined whether DPG reduced stem cell subpopulation formation. The most over-expressed miRNAs were miR-4443 and miR-3620. The cytotoxic effect of DPG in U251 and U138MG was observed with an IC50 of 32 and 20 mM for 48 h, respectively. The IC50 of each cell line was used in all further assays. DPG treatment-induced apoptosis is observed by DNA fragmentation and increased TUNEL-positive cells. Cell proliferation and wound-healing assays showed an anti-proliferative and anti-migratory effect by DPG on the evaluated cell lines. In addition, DPG treatment led to a 100% reduction in sphere formation. The qPCR results in U251 and U138MG cells showed that DPG increased miR-4443 (2.44 vs. 1.11, p-value = 0.11; 8.27 vs. 1.25, p-value = 0.04) and miR-3620 expression (1.66 vs. 1.00, p-value = 0.03; 8.47 vs. 1.01, p-value = 0.03) and decreased CD209 (0.44 vs. 1.10, p-value = 0.03; 0.49 vs. 1.07, p-value = 0.04) and TNC (0.20 vs. 1.03, p-value = 0.001; 0.39 vs. 1.06, p-value = 0.01) mRNA levels compared to controls. Our results suggest that DPG inhibits cell viability by activating apoptosis and inhibiting cell proliferation and stem cell subpopulation formation through miR-4443 and miR-3620 upregulation. Both miRNAs are responsible for the post-transcriptional inhibition of NF-κB by CD209 and TNC modulation.

A novel immune-related microRNA signature for prognosis of thymoma.

INTRODUCTION: Immune microenvironment and microRNAs serve as common predictors for diagnosis and prognosis of tumors. METHODS: Expression of 122 genes and 126 microRNAs in thymoma was obtained from TCGA database. The proportion of tumor-infiltrating cells was calculated, and IMRS was constructed. TREM2hi score was calculated before functional enrichment analysis on gene sets. RESULTS: IMRS3, TREM2hi score, and CD8+ T lymphocyte abundance were significantly different among WHO classifications. WHO classification, Masaoka staging, and miR-130b-5p, miR-1307-3p, miR-425-5p, CD8, CD68, and CCL18 expression were prognostic factors for relapse-free survival and overall survival. IMRS3 upregulation polarized macrophages into M2, which rejected CD8+ T and other effector lymphocytes to promote thymoma malignant progression. CONCLUSIONS: BRRS may present a novel immune-related microRNA signature for TET prognosis.

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer.

Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (Ktrans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39-0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.

A risk score system based on a six-microRNA signature predicts the overall survival of patients with ovarian cancer.

BACKGROUND: Ovarian cancer (OVC) is a devastating disease worldwide; therefore the identification of prognostic biomarkers is urgently needed. We aimed to determine a robust microRNA signature-based risk score system that could predict the overall survival (OS) of patients with OVC. METHODS: We extracted the microRNA expression profiles and corresponding clinical data of 467 OVC patients from The Cancer Genome Atlas (TCGA) database and further divided this data into training, validation and complete cohorts. The key prognostic microRNAs for OVC were identified and evaluated by robust likelihood-based survival analysis (RLSA) and multivariable Cox regression. Time-dependent receiver operating characteristic (ROC) curves were then constructed to evaluate the prognostic performance of these microRNAs. A total of 172 ovarian cancer samples and 162 normal ovarian tissues were used to verify the credibility and accuracy of the selected markers of the TCGA cohort by quantitative real-time polymerase chain reaction (PCR). RESULTS: We successfully established a risk score system based on a six-microRNA signature (hsa-miR-3074-5p, hsa-miR-758-3p, hsa-miR-877-5p, hsa-miR-760, hsa-miR-342-5p, and hsa-miR-6509-5p). This microRNA based system is able to characterize patients as either high or low risk. The OS of OVC patients, with either high or low risk, was significantly different when compared in the training cohort (p < 0.001), the validation cohort (p < 0.001) and the complete cohort (p < 0.001). Analysis of clinical samples further demonstrated that these microRNAs were aberrantly expressed in OVC tissues. The six-miRNA-based signature was correlated with the prognosis of OVC patients (p < 0.001). CONCLUSIONS: The study established a novel risk score system that is predictive of patient prognosis and is a potentially useful guide for the personalized treatment of OVC patients.

Validation of MiROvaR, a microRNA-based predictor of early relapse in early stage epithelial ovarian cancer as a new strategy to optimise patients' prognostic assessment.

AIM: Early-stage epithelial ovarian cancer (eEOC) patients have a generally favorable prognosis but unpredictable recurrence. Accurate prediction of risk of relapse is still a major concern, essentially to avoid overtreatment. Our robust tissue-based miRNA signature named MiROvaR, predicting early EOC recurrence in mostly advanced-stage EOC patients, is here challenged in an independent cohort to extend its classifying ability in the early-stage EOC setting. METHODS: We retrospectively selected patients who underwent comprehensive surgical staging at our institution including stages from IA to IIB. miRNA expression profile was analysed in 89 cases and MiROvaR algorithm was applied using the previously validated cut-off for patients' classification. The primary endpoint was progression-free survival (PFS) at 5 years. Complete follow-up time (median = 112 months) was also considered as secondary analysis. RESULTS: MiROvaR was assessable on 87 cases (19 events of disease progression) and classified 68 (78%) low-risk and 19 (22%) high-risk patients. Recurrence rate at primary end-point was 39% for high-risk patients as compared to 9.5% for low-risk ones. Accordingly, their Kaplan-Meier PFS curves were significantly different at both primary and secondary analysis (p = 0.0006 and p = 0.03, respectively). While none of the prominent clinical variables had prognostic relevance, MiROvaR significantly predicted disease recurrence at the 5-year assessment (primary endpoint analysis; HR:5.43, 95%CI:1.82-16.1, p = 0.0024; AUC = 0.78, 95%CI:0.53-0.82) and at complete follow-up time (HR:2.67, 95%CI:1.04-6.8, p = 0.041; AUC:0.68, 95%CI:0.52-0.82). CONCLUSIONS: We validated MiROvaR performance in identifying at diagnosis eEOC patients' at higher risk of early relapse thus enabling selection of the most effective therapeutic approach.

A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value < 2 e-16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e-4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

A two-microRNA signature predicts the progression of male thyroid cancer.

In various cancers, microRNAs (miRNAs) are abnormally expressed, including thyroid cancer (TC). In recent years, the incidence of TC has increased annually around the world. Compared with female patients, male TC patients are more likely to have a postoperative recurrence and lymph node metastasis, and hence need second treatments. However, the molecular biological processes underlying this phenomenon are not understood. Therefore, we collected data on miRNA expression and clinical information of male TC patients from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs were identified between male TC tissues and matched normal tissues. The Kaplan-Meier method, univariate and multivariate Cox regressions, and receiver operating characteristic curve analyses were performed to assess the association between miRNAs and the disease-free survival of male TC patients. Gene Ontology (GO) and the Kyoto Encyclopaedia of Gene and Genome (KEGG) enrichment analyses were then used to explore the function of miRNA target genes. Furthermore, we evaluated the ability of the miRNA biomarker to predict survival in female TC patients. As a result, a total of 118 differentially expressed miRNAs were identified, including 25 upregulated and 93 downregulated miRNAs. Among them, miR-451a and miR-16-1-3p were confirmed to be independent prognostic factors for the disease-free survival rate. The target genes of miR-451a and miR-16-1-3p were identified, and functional analysis showed that these genes were enriched in 25 Go and KEGG accessions, including cell signal transduction, motor adhesion, phagocytosis, regulation of transcription, cell proliferation, angiogenesis, etc. Neither miR-451a and miR-16-1-3p, nor a prediction model based on both miRNAs effectively predicted survival in female TC patients. In conclusion, both miR-451a and miR-16-1-3p may play important roles in the processes of male TC. The two-miRNA signature involving miR-1258 and miR-193a may serve as a novel prognostic biomarker for male TC patients.

A Five-microRNA Signature as Risk Stratification System in Uterine Corpus Endometrial Carcinoma.

BACKGROUND: MicroRNAs (miRs) have been shown to play important roles in various cancers and may be a reliable prognostic marker. However, its prognostic value in endometrial carcinoma (UCEC) needs to be further explored. OBJECTIVES: The aim of this study was to create a miR-based signature to effectively predict the prognosis for patients with uterine corpus endometrial carcinoma (UCEC). METHODS: Using UCEC data set in TCGA, we identified differentially expressed miRs between UCEC and healthy endometrial tissues. The LASSO method was used to construct a miR-based signature prognosis index for predicting prognosis in the training cohort. The miR-based signature prognosis index was validated in an independent test cohort. MiRNet tool was applied to perform functional enrichment analysis of this miR-based signature. RESULTS: A total of 208 miRs were differentially expressed between UCEC and healthy endometrial tissues. Five miRs (miR-652, miR-3170, miR-195, miR-34a, and miR-934) were identified to generate a prognosis index (PI). The five-miR signature is a promising biomarker for predicting the 5-year-survival rate of UCEC with AUC = 0.730. The PI remained an independent prognostic factor adjusted by routine clinicopathologic factors. Using the PI, we could successfully identify the high-risk individuals, furthermore, it still worked in an independent test cohort. The five miRs involved in various pathways associated with cancer. CONCLUSION: We proposed and validated a five-miR signature that could serve as an independent prognostic predictor of UCECs.

Prognostic value of a hypoxia-related microRNA signature in patients with colorectal cancer.

Hypoxia has been particularly associated with poor prognosis in cancer patients. Recent studies have suggested that hypoxia-related miRNAs play a critical role in various cancers, including colorectal cancer (CRC). In the present study, we found 52 differentially expressed miRNAs in HT-29 cells under hypoxic conditions versus normoxic conditions by analyzing the profiles of miRNAs. Using Cox model, we developed a hypoxia-related miRNA signature consisting of four miRNAs, which could successfully discriminate high-risk patients in the Cancer Genome Atlas (TCGA) training cohort (n=381). The prognostic value of this signature was further confirmed in the TCGA testing cohort (n=190) and an independent validation cohort composed of formalin-fixed paraffin-embedded clinical CRC samples (n=220), respectively. Multivariable Cox regression and stratified survival analysis revealed this signature was an independent prognostic factor for CRC patients. Time-dependent receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this signature was significantly larger than that of any other clinical risk factors or single miRNA alone. A nomogram was constructed for clinical use, which incorporated both the miRNA signature and clinical risk factors and performed well in the calibration plots. Collectively, this novel hypoxia-related miRNA signature was an independent prognostic factor, and it possessed a stronger predictive power in identifying high-risk CRC patients than currently used clinicopathological features.

A seven-miRNA expression-based prognostic signature and its corresponding potential competing endogenous RNA network in early pancreatic cancer.

The present study aimed to establish a microRNA (miRNA/miR) signature to predict the prognosis of patients with pancreatic cancer (PC) at the early stage and to investigate the involvement of competing endogenous RNAs (ceRNAs) in PC. Using mature miRNA expression profiles from The Cancer Genome Atlas, differentially expressed miRNAs in tissues derived from patients exhibiting early PC and tissues from healthy individuals were compared. The least absolute shrinkage and selection operator regression method was used to construct a miRNA-based signature for predicting prognosis. The miRNet tool, gene set enrichment analysis (GSEA) and the LncRNADisease database were utilized to explore the mechanistic involvement of ceRNAs. A total of seven downregulated miRNAs in PC (miR-424-5p, miR-139-5p, miR-5586-5p, miR-126-3p, miR-3613-5p, miR-454-3p and miR-1271-5p) were selected to generate a signature. Based on this seven-miRNA signature, it was possible to stratify patients with PC into low- and high-risk groups. The overall survival of the low-risk group was significantly longer than that of the high-risk group (P<0.001). The seven-miRNA signature was able to predict the 2-year-survival rate of patients with early PC with an area under the curve of 0.750. Furthermore, as opposed to routine clinicopathological features, this seven-miRNA signature was an independent prognostic factor according to multivariate Cox regression analysis. GSEA indicated that the extracellular matrix receptor interaction pathway and the transforming growth factor-ß signaling pathway were enriched in the high-risk group. A ceRNA network of the seven-miR signature was constructed. In conclusion, the present study provided a seven-miRNA signature, according to which patients with early PC may be divided into high- and low-risk groups. The ceRNA network of the prognostic signature was preliminarily explored.

Serum-based microRNA signature predicts relapse and therapeutic outcome of adjuvant chemotherapy in colorectal cancer patients.

BACKGROUND: Approximately 60% of patients with colorectal cancer (CRC) undergo either local recurrence or distant metastases after surgery. Current prognostic biomarkers are insufficient to predict recurrence of CRC and provide little forecast information about what patients are likely to receive benefit from the adjuvant chemotherapy. As microRNAs (miRNAs) constantly exist in human serum and being used to predict the prognosis of a various cancers, this study was designed to identify miRNA-based circulating biomarkers to predict clinical outcomes of CRC. METHODS: A serum-focused miRNA expression was used to investigate if miRNA expression profiles could predict the clinical outcomes of patients with CRC. We created miRNA signature profiles associated in the training set (n = 40), and further validated its prediction in two independent testing cohorts. RESULTS: Using Cox regression and risk-score analysis, we identified a four-miRNA signature (miR-652-3p, miR-342-3p, miR-501-3p and miR-328-3p) for the prediction of tumor relapse and the overall survival(OS) of patients with CRC in the training set (n = 40). This miRNA signature was further validated in a testing set (n = 226) and another independent cohort (n = 56). A high-risk signature score was significantly associated with CRC tumor recurrence and poor treatment outcome. Multivariable Cox regression models indicated that the risk score, based on the four-miRNA signature, was an independent prognostic classifier for patients with CRC. CONCLUSIONS: The serum miRNA signature may serve as a minimally invasive predictor for tumor relapse and treatment outcome in patients with CRC and provide a useful reference for treatment selection.

A two-microRNA signature as a diagnostic and prognostic marker of pancreatic adenocarcinoma.

BACKGROUND AND AIM: Some cancer-specific miRNAs are dysregulated in pancreatic adenocarcinoma (PAAD) and involved in cell autophagy, differentiation, proliferation, migration, invasion, and malignant transformation. The aim of our study was to determine a panel of new diagnostic and prognostic biomarkers for PAAD. METHODS: We conducted a comprehensive analysis of global miRNA-expression profiles and corresponding prognosis information of 168 PAAD patients from the Cancer Genome Atlas data set. A total of 16 differentially expressed miRNAs were identified as aberrantly expressed in PAAD, and six of these were evaluated for use as diagnostic markers for PAAD. Next, we confirmed a two-miRNA signature significantly associated with PAAD patient diagnosis and outcome prediction. RESULTS: The panel of two miRNAs showed outstanding diagnostic performance, with sensitivity of 100% and specificity of 87.5%. Finally, we divided the PAAD patients into high-risk and low-risk groups based on the expression profile of the two miRNAs. Kaplan-Meier analysis demonstrated that patients in the high-risk group had significantly worse prognosis than patients in the low-risk group. Univariate and multivariate Cox regression analysis showed that the two-miRNA signature was an independent prognostic factor for the overall survival of PAAD patients. CONCLUSION: Taken together, the two-miRNA signature may serve as an accurate and sensitive biomarker for diagnosis and PAAD-outcome prediction, facilitating the diagnosis and potentially improving treatment outcome of PAAD.

A microRNA signature associated with pathological complete response to novel neoadjuvant therapy regimen in triple-negative breast cancer.

Neoadjuvant chemotherapy aims to improve the outcome of breast cancer patients, but only few would benefit from this treatment. Pathological complete response has been proposed as a surrogate marker for the prediction of long-term clinical benefits; however, 50%-85% patients have an unfavorable pathological complete response to chemotherapy. MicroRNAs are known biomarkers of breast cancer progression; nevertheless, their potential to identify patients with pathological complete response remains poorly understood. Here, we investigated whether a microRNA profile could be associated with pathological complete response in triple-negative breast cancer patients receiving 5-fluorouracil, adriamycin, cyclophosphamide-cisplatin/paclitaxel as a novel neoadjuvant chemotherapy. In the discovery cohort, the expression of 754 microRNAs was examined in tumors from 10 triple-negative breast cancer patients who achieved pathological complete response and 8 without pathological complete response using TaqMan Low-Density Arrays. Unsupervised hierarchical cluster analysis identified 11 microRNAs with significant differences between responder and no-responder patients (fold change ≥ 1.5; p < 0.05). The differential expression of miR-30a, miR-9-3p, miR-770, and miR-143-5p was validated in an independent group of 17 patients with or without pathological complete response. Moreover, Kaplan-Meier analysis showed that expression of these four microRNAs was associated with an increased disease-free survival. Gene ontology classification of predicted microRNA targets indicated that numerous genes are involved in pathways related to chemoresistance, such as vascular endothelial growth factor, focal adhesion kinase, WNT, ERbB, phosphoinositide 3-kinase, and AKT signaling. In summary, we identified a novel microRNA expression signature associated with pathological complete response in breast cancer. We propose that the four validated microRNAs could be used as molecular biomarkers of clinical response in triple-negative breast cancer patients with pathological complete response to neoadjuvant therapy.

A novel microRNAs expression signature for hepatocellular carcinoma diagnosis and prognosis.

This study aims to identify prognostic microRNAs (miRNAs) biomarkers for diagnosis and survival of hepatocellular carcinoma (HCC) based on large patients cohort analysis. HCC patient cohort data were downloaded from The Cancer Genome Atlas, including paired HCC and adjacent non-cancer tissues. Receiver operating characteristic curve method was used to classify cancer and non-cancer tissues according to microRNAs expression levels. The aberrant microRNAs expression level were ranked and risked for building a prognostic miRNAs signature model. Kaplan-Meier survival was used to analyze the differences among various risk factors in accordance with miRNAs ranking scores. The study showed 33-miRNA signature, 11 were down-regulated and 22 were up-regulated through comparison between cancer samples and non-cancer samples. The maximum correct classification rate is up to 98.7%. Five microRNAs, hsa-mir-3677, hsa-mir-421, hsa-mir-326, hsa-mir-424 and hsa-mir-511-2, significantly correlated with patient survival. The survival rate and time negatively associated with lowering miRNAs index. In the low risk group, over 70% patients showed 5 years survival, while none patients survived longer than 5 years in the high risk group. MiR-424, miR-326 and miR-511 could be applied for HCC diagnostic biomarkers. These five miRNAs were significantly associated with lysosome pathway and D-Glutamine and D-glutamate metabolism pathway via Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology annotation. Conclusively, the five miRNAs expression signature could be used as HCC prognostic and diagnostic biomarkers.

Identification of microRNA signature and potential pathway targets in prostate cancer.

Prostate cancer (PC) is the most common and the second leading cause of cancer-related death among American men. Early diagnosis is a prerequisite to improving therapeutic benefits. However, the current clinical biomarkers for PC do not reliably decipher indolent PC from other urogenital disorders. Thus, effective clinical intervention necessitates development of new biomarkers for early detection of PC. The present study aimed to identify the miRNA signature in organ-confined (Gleason Score 6) prostate tumors. MicroRNA (miRNA/miR) array analysis identified 118 upregulated and 73 downregulated miRNAs in microdissected tumors in comparison to matched neighboring normal prostate epithelium. The miRs-Plus-A1083, -92b-5p, -18a-3p, -19a-3p, -639, -3622b-3p, -3189-3p, -155-3p, -410, -1179, 548b-5p, and -4469 are predominantly expressed (7-11-fold), whereas miRs-595, 4490, -3120-5p, -1299, -21-5p, -3677-3, -let-7b-5p, -5189, 3-121-5p, -4518, -200a-5p, -3682-5p, -3689d, -3149 represent the most downregulated (12-113-fold) miRNAs in microdissected prostate tumors. The array expression profile of selected miRNA signature and their potential mRNA targets was validated by qRT-PCR analysis in PC cell lines. Integrated in silico and computational prediction analyses demonstrated that the dysregulated miRNA signature map to key regulatory factors involved in tumorigenesis, including cell cycle, apoptosis, and p53 pathways. The newly identified miRNA signature has potential clinical utility as biomarkers, prognostic indicators, and therapeutic targets for early detection of PC. Further studies are needed to assess the functional significance and clinical usefulness of the identified miRNAs. Impact Statement To our knowledge his is the first study of identifying miRNA signatures in microdissected indolent (Gleason score 6) prostate cancer in comparison to matched normal prostate epithelium. By employing in silico and computational prediction analysis, the study provides a landscape of potential miRNA targets and key cellular pathways involved in prostate tumorigenesis. Identification if miRNAs and their relevant targets and pathways pave the way for underpinning their mechanistic role of miRNAs in human prostate tumorigenesis, and possibly other human cancers. Importantly, the outcome of the study has important clinical implications for the management of prostate cancer, including the use of miRNA(s) as biomarkers for early detection of prostate cancer.