Combination of a six microRNA expression profile with four clinicopathological factors for response prediction of systemic treatment in patients with advanced colorectal cancer

Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases

Objectives

This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients.

Methods

We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a training cohort (n=112) and a test cohort (n=38). We manually delineated three-dimensional tumor volumes, selecting the largest liver metastasis for measurement, using pretreatment portal-phase CT images and extracted 107 radiomics features. Treatment response was classified as responder (complete or partial response) or non-responder (stable or progressive disease), based on the best overall response according to RECIST criteria, version 1.1. Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. Radiomics signatures were developed and validated in the training cohort using five-fold cross-validation, and performance was assessed using the area under the curve (AUC).

Results

Among the patients, 91 (61%) were responders and 59 (39%) were non-responders. Variable selection with Boruta revealed three key parameters ("DependenceVariance," "ClusterShade," and "RunVariance"). In the training cohort, individual CT texture parameter AUCs ranged from 0.4 to 0.65, while the machine learning analysis incorporating all valid parameters exhibited a significantly higher AUC of 0.94 (p<0.01). The validation cohort also demonstrated strong predictive accuracy, with an AUC of 0.87 for treatment response.

Conclusions

This study highlights the potential of CT radiomics-driven machine learning in predicting chemotherapy responses among CRLM patients.

MicroRNA-98: the multifaceted regulator in human cancer progression and therapy

MicroRNA-98 (miR-98) stands as an important molecule in the intricate landscape of oncology. As a subset of microRNAs, these small non-coding RNAs have accompanied a new era in cancer research, underpinning their significant roles in tumorigenesis, metastasis, and therapeutic interventions. This review provides a comprehensive insight into the biogenesis, molecular properties, and physiological undertakings of miR-98, highlighting its double-edged role in cancer progression-acting both as a tumor promoter and suppressor. Intriguingly, miR-98 has profound implications for various aspects of cancer progression, modulating key cellular functions, including proliferation, apoptosis, and the cell cycle. Given its expression patterns, the potential of miR-98 as a diagnostic and prognostic biomarker, especially in liquid biopsies and tumor tissues, is explored, emphasizing the hurdles in translating these findings clinically. The review concludes by evaluating therapeutic avenues to modulate miR-98 expression, addressing the challenges in therapy resistance, and assessing the efficacy of miR-98 interventions. In conclusion, while miR-98's involvement in cancer showcases promising diagnostic and therapeutic avenues, future research should pivot towards understanding its role in tumor-stroma interactions, immune modulation, and metabolic regulation, thereby unlocking novel strategies for cancer management.

Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net

Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, for example, groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that this marginal likelihood approximates that of elastic net models. The ridge group penalties are then transformed to elastic net group penalties by matching the ridge prior variance to the elastic net prior variance as function of the group penalties. The method allows for overlapping groups and unpenalized variables, and is easily extended to other penalties. For a model-based simulation study and two cancer genomics applications we demonstrate a substantially decreased computation time and improved or matching performance compared to other methods. Supplementary materials for this article are available online.

A Systematic Review of Clinical Validated and Potential miRNA Markers Related to the Efficacy of Fluoropyrimidine Drugs

Colorectal cancer (CRC) is becoming increasingly prevalent worldwide. Fluoropyrimidine drugs are the primary chemotherapy regimens in routine clinical practice of CRC. However, the survival rate of patients on fluoropyrimidine-based chemotherapy varies significantly among individuals. Biomarkers of fluoropyrimidine drugs'' efficacy are needed to implement personalized medicine. This review summarized fluoropyrimidine drug-related microRNA (miRNA) by affecting metabolic enzymes or showing the relevance of drug efficacy. We first outlined 42 miRNAs that may affect the metabolism of fluoropyrimidine drugs. Subsequently, we filtered another 41 miRNAs related to the efficacy of fluoropyrimidine drugs based on clinical trials. Bioinformatics analysis showed that most well-established miRNA biomarkers were significantly enriched in the cancer pathways instead of the fluoropyrimidine drug metabolism pathways. The result also suggests that the miRNAs screened from metastasis patients have a more critical role in cancer development than those from non-metastasis patients. There are five miRNAs shared between these two lists. The miR-21, miR-215, and miR-218 can suppress fluoropyrimidine drugs'' catabolism. The miR-326 and miR-328 can reduce the efflux of fluoropyrimidine drugs. These five miRNAs could jointly act by increasing intracellular levels of fluoropyrimidine drugs'' cytotoxic metabolites, leading to better chemotherapy responses. In conclusion, we demonstrated that the dynamic changes in the transcriptional regulation via miRNAs might play significant roles in the efficacy and toxicity of the fluoropyrimidine drug. The reported miRNA biomarkers would help evaluate the efficacy of fluoropyrimidine drug-based chemotherapy and improve the prognosis of colorectal cancer patients.

Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes

Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.

A Three-Genes Signature Predicting Colorectal Cancer Relapse Reveals LEMD1 Promoting CRC Cells Migration by RhoA/ROCK1 Signaling Pathway

Objective

Colorectal cancer (CRC) patients that experience early relapse consistently exhibit poor survival. However, no effective approach has been developed for the diagnosis and prognosis prediction of postoperative relapsed CRC.

Methods

Multiple datasets from the GEO database and TCGA database were utilized for bioinformatics analysis. WGCNA analyses and RRA analysis were performed to identify key genes. The COX/Lasso regression model was used to construct the recurrence model. Subsequent in vitro experiments further validated the potential role of the hub genes in CRC.

Results

A comprehensive analysis was performed on multiple CRC datasets and a CRC recurrence model was constructed containing LEMD1, SERPINE1, and SIAE. After further validation in two independent databases, we selected LEMD1 for in vitro experiments and found that LEMD1 could regulate CRC cell proliferation, migration, invasion, and promote EMT transition. The Rho-GTPase pulldown experiments further indicated that LEMD1 could affect RhoA activity and regulate cytoskeletal dynamics. Finally, we demonstrated that LEMD1 promoted CRC cell migration through the RhoA/ROCK1 signaling pathway.

Conclusions

In this study, a CRC relapse model consisting of LEMD1, SERPINE1, and SIAE was constructed by comprehensive analysis of multiple CRC datasets. LEMD1 could promote CRC cell migration through the RhoA/ROCK signaling pathway.

A comprehensive framework for analysis of microRNA sequencing data in metastatic colorectal cancer

Although microRNAs (miRNAs) contribute to all hallmarks of cancer, miRNA dysregulation in metastasis remains poorly understood. The aim of this work was to reliably identify miRNAs associated with metastatic progression of colorectal cancer (CRC) using novel and previously published next-generation sequencing (NGS) datasets generated from 268 samples of primary (pCRC) and metastatic CRC (mCRC; liver, lung and peritoneal metastases) and tumor adjacent tissues. Differential expression analysis was performed using a meticulous bioinformatics pipeline, including only bona fide miRNAs, and utilizing miRNA-tailored quality control and processing. Five miRNAs were identified as up-regulated at multiple metastatic sites Mir-210_3p, Mir-191_5p, Mir-8-P1b_3p [mir-141–3p], Mir-1307_5p and Mir-155_5p. Several have previously been implicated in metastasis through involvement in epithelial-to-mesenchymal transition and hypoxia, while other identified miRNAs represent novel findings. The use of a publicly available pipeline facilitates reproducibility and allows new datasets to be added as they become available. The set of miRNAs identified here provides a reliable starting-point for further research into the role of miRNAs in metastatic progression.

Flexible co-data learning for high-dimensional prediction

Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co-data, provide information on the covariates, such as genomic location or P-values from external studies. We use multiple and various co-data to define possibly overlapping or hierarchically structured groups of covariates. These are then used to estimate adaptive multi-group ridge penalties for generalized linear and Cox models. Available group adaptive methods primarily target for settings with few groups, and therefore likely overfit for non-informative, correlated or many groups, and do not account for known structure on group level. To handle these issues, our method combines empirical Bayes estimation of the hyperparameters with an extra level of flexible shrinkage. This renders a uniquely flexible framework as any type of shrinkage can be used on the group level. We describe various types of co-data and propose suitable forms of hypershrinkage. The method is very versatile, as it allows for integration and weighting of multiple co-data sets, inclusion of unpenalized covariates and posterior variable selection. For three cancer genomics applications we demonstrate improvements compared to other models in terms of performance, variable selection stability and validation.

Adaptive group-regularized logistic elastic net regression

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p$\end{document}-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical–variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.

Expression levels and clinical values of miR-92b-3p in breast cancer

BACKGROUND

miR-92b is a carcinogenic miRNA that has great potential as a biomarker for disease prognosis, diagnosis, and treatment in the clinic. It is of great significance to analyse the relationship between miR-92b and the clinicopathological characteristics of cancer patients. This paper aimed to investigate the expression levels and clinical values of miR-92b-3p in breast cancer (BC).

METHODS

Altogether, 112 female BC patients who were treated in our hospital were included as a study group, and 108 healthy women who came to our hospital for physical examinations were included as a control group. miR-92b-3p expression in the serum of subjects in both groups was detected by fluorescence quantitative PCR (RT-PCR) to analyse the correlation of this miRNA with the patients' pathological features and prognoses. The diagnostic value of miR-92b-3p expression for BC was analysed by plotting a receiver operating characteristic (ROC) curve.

RESULTS

miR-92b-3p expression was remarkably higher in the study group (P < 0.05), and its area under the curve (AUC) for detecting BC was 0.88. The expression was correlated with the tumour size, degree of differentiation, TNM staging, and lymphatic metastasis (P < 0.05). miR-92b-3p was significantly positively correlated with the TNM staging (r = 0.40, P < 0.05), was significantly negatively correlated with the degree of differentiation of the breast cancer cells (r =  - 0.35, P < 0.05), and was significantly positively correlated with the expression of carbohydrate antigen 125 (CA125) (r = 0.39, P < 0.05). The overall survival rate (OSR) of the 99 patients who had follow-up was 73.74%. The survival status was remarkably better in the low expression group (P < 0.05). miR-92b-3p expression was remarkably higher in the death group (P < 0.05). The AUC of miR-92b-3p alone in the death and survival groups was 0.76.

CONCLUSION

miR-92b-3p expression obviously rises in the serum of BC patients and is closely related to the clinical staging, degree of differentiation, and CA125 in BC, so the detection of this miRNA is of great significance to the diagnosis and prognostic evaluation of BC. This miRNA can be used as a potential biomarker for the diagnosis and prognosis of the disease.

miR-92b-3p Regulates Cell Cycle and Apoptosis by Targeting CDKN1C, Thereby Affecting the Sensitivity of Colorectal Cancer Cells to Chemotherapeutic Drugs

Simple Summary

Multidrug resistance (MDR) limits the effectiveness of colorectal cancer (CRC) treatment and miRNAs play an important role in drug resistance. To search for miRNA targets that may be involved in the CRC MDR phenotype, this study used small RNAomic screens to analyze the expression profiles of miRNAs in CRC HCT8 cell line and its chemoresistant counterpart HCT8/T cell line. It was found that miR-92b-3p was highly expressed in HCT8/T cells and chemotherapeutic drugs could stimulate CRC cells to up-regulate miR-92b-3p expression and conferred cellular resistance to chemotherapeutic drugs. This study revealed a new mechanism of MDR in CRC, elucidating for the first time the direct link between miR-92b-3p/CDKN1C and chemoresistance. In summary, this study suggested that miR-92b-3p could be used as a potential therapeutic target for reversing MDR in chemotherapy and as a candidate biomarker for predicting the efficacy of chemotherapy.

Abstract

Colorectal cancer (CRC) is the third most common malignant tumor in the world and the second leading cause of cancer death. Multidrug resistance (MDR) has become a major obstacle in the clinical treatment of CRC. The clear molecular mechanism of MDR is complex, and miRNAs play an important role in drug resistance. This study used small RNAomic screens to analyze the expression profiles of miRNAs in CRC HCT8 cell line and its chemoresistant counterpart HCT8/T cell line. It was found that miR-92b-3p was highly expressed in HCT8/T cells. Knockdown of miR-92b-3p reversed the resistance of MDR HCT8/T cells to chemotherapeutic drugs in vitro and in vivo. Paclitaxel (PTX, a chemotherapy medication) could stimulate CRC cells to up-regulate miR-92b-3p expression and conferred cellular resistance to chemotherapeutic drugs. In studies on downstream molecules, results suggested that miR-92b-3p directly targeted Cyclin Dependent Kinase Inhibitor 1C (CDKN1C, which encodes a cell cycle inhibitor p57Kip2) to inhibit its expression and regulate the sensitivity of CRC cells to chemotherapeutic drugs. Mechanism study revealed that the miR-92b-3p/CDKN1C axis exerted a regulatory effect on the sensitivity of CRC cells via the regulation of cell cycle and apoptosis. In conclusion, these findings showed that miR-92b-3p/CDKN1C was an important regulator in the development of drug resistance in CRC cells, suggesting its potential application in drug resistance prediction and treatment.

miR-122 and miR-197 expressions in hepatic carcinoma patients before and after chemotherapy and their effect on patient prognosis

OBJECTIVE

To quantify the miR-122 and miR-197 expression levels in liver cancer (LC) patients before and after chemotherapy and to determine their prognostic implications.

METHODS

The present study included 169 patients with LC who were admitted to our hospital from January 2005 to December 2010. The miR-122 and miR-197 expression levels in the patients' cancerous and adjacent tissues were quantified, and their peripheral blood levels before and after chemotherapy were analyzed, as well as their prognostic implications.

RESULTS

The miR-122 and miR-197 levels in the LC tissues were lower than they were in the adjacent tissues, and they increased in the peripheral blood after chemotherapy. Higher miR-122 and miR-197 expression levels were observed in the LC tissues of sorafenib-sensitive patients. ROC curves demonstrated that miR-122 and miR-197 are predictive markers for the therapeutic effect of sorafenib. As shown by a K-M survival curve and a log-rank test, low miR-122 and miR-197 levels are responsible for low 5-year patient survival rates. Moreover, a univariate Cox analysis uncovered the association between the 5-year survival and the miR-122 and miR-197 expression levels, the size and number of tumors, vascular invasion, and TNM and BCLC staging. Also, a multivariate Cox analysis indicated that the independent risk factors for 5-year survival in LC included the miR-122 and miR-197 levels, the number of tumors, vascular invasion, and TNM and BCLC staging.

CONCLUSION

miR-122 and miR-197 expression levels can predict LC patient responses to sorafenib chemotherapy, and their levels increase after chemotherapy. Moreover, decreased miR-122 and miR-197 levels are independent risk factors for LC progression.

Predictive Efficacy of MiR-125b-5p, MiR-17-5p, and MiR-185-5p in Liver Metastasis and Chemotherapy Response Among Advanced Stage Colorectal Cancer Patients

MicroRNAs (miRNAs), a class of small non-coding RNAs represent potential biomarkers for colorectal cancer (CRC). The study hypothesized that miRNAs associated with liver metastases may also contribute to assessing treatment response when associated to plasma exosomes. In this study, we used two sets of biological samples, a collection of tumor tissues harvested from patients with CRC with and without liver metastases, and a collection of plasma from CRC patients with and without response to FOLFOX4/FOLFIRI regimens. We investigated 10 target miRNAs in the tissue of 28 CRC patients and identified miR-125b-5p, miR-17-5p, and miR-185-5p to be associated with liver metastasis. Further, we investigated the three miRNAs at the exosomal level in a plasma collection to test their association with chemotherapy response. Our data suggest that the elevated plasma levels of miR-17-5p and miR-185-5p could be predictive of treatment response. Overexpression of miR-17-5p and underexpression of miR-125b-5p and miR-185-5p in CRC tissue seem to be associated with metastatic potential. On the other hand, an increased expression of miR-125b-5p in plasma exosomes was potentially correlated with a more aggressive CRC phenotype.

MicroRNA-92a-3p enhances functional recovery and suppresses apoptosis after spinal cord injury via targeting phosphatase and tensin homolog

Spinal cord injury (SCI) is a neurological disease commonly caused by traumatic events on spinal cords. MiRNA-92a-3p is reported to be down-regulated after SCI. Our study investigated the effects of up-regulated miR-92a-3p on SCI and the underlying mechanisms. SCI mice model was established to evaluate the functional recovery of hindlimbs of mice through open-field locomotion and scored by Basso, Beattie, and Bresnahan (BBB) locomotion scale. Apoptosis of spinal cord cells was determined by flow cytometry. The effects of miR-92a-3p on SCI were detected by intrathecally injecting miR-92a-3p agomiR (agomiR-92) into the mice prior to the establishment of SCI. Phosphatase and tensin homolog (PTEN) was predicted as a target of miR-29a-3p by TargetScan. We further assessed the effects of agomiR-92 or/and overexpressed PTEN on apoptosis rates and apoptotic protein expressions in SCI mice. Moreover, the activation of protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling was determined by Western blot. The results showed that compared with the sham-operated mice, SCI mice had much lower BBB scores, and theapoptosis rate of spinal cord cells was significantly increased. After SCI, the expression of miR-92a-3p was down-regulated, and increased expression of miR-92a-3p induced by agomiR-92 further significantly increased the BBB score and decreased apoptosis. PTEN was specifically targeted by miR-92a-3p. In addition, the phosphorylation levels of Akt and mTOR were up-regulated under the treatment of agomiR-92. Our data demonstrated that the neuroprotective effects of miR-92a-3p on spinal cord safter SCI were highly associated with the activation of the PTEN/AKT/mTOR pathway.

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA-mRNA regulatory network involved in LUAD has not been fully elucidated.

METHODS

Differentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA-mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein-protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA).

RESULTS

The miRNA-mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD.

CONCLUSION

This study investigated a miRNA-mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.

OncomiRs miR-106a and miR-17 negatively regulate the nucleoside-derived drug transporter hCNT1

High-affinity uptake of natural nucleosides as well as nucleoside derivatives used in anticancer therapies is mediated by human concentrative nucleoside transporters (hCNTs). hCNT1, the hCNT family member that specifically transports pyrimidines, is also a transceptor involved in tumor progression. In particular, oncogenesis appears to be associated with hCNT1 downregulation in some cancers, although the underlying mechanisms are largely unknown. Here, we sought to address changes in colorectal and pancreatic ductal adenocarcinoma-both of which are important digestive cancers-in the context of treatment with fluoropyrimidine derivatives. An analysis of cancer samples and matching non-tumoral adjacent tissues revealed downregulation of hCNT1 protein in both types of tumor. Further exploration of the putative regulation of hCNT1 by microRNAs (miRNAs), which are highly deregulated in these cancers, revealed a direct relationship between the oncomiRs miR-106a and miR-17 and the loss of hCNT1. Collectively, our findings provide the first demonstration that hCNT1 inhibition by these oncomiRs could contribute to chemoresistance to fluoropyrimidine-based treatments in colorectal and pancreatic cancer.

A specific microRNA profile as predictive biomarker for systemic treatment in patients with metastatic colorectal cancer

Background

Palliative systemic therapy is currently standard of care for patients with extensive metastatic colorectal cancer (mCRC). A biomarker predicting chemotherapy benefit which prevents toxicity from ineffective treatment is urgently needed. Therefore, a previously developed tissue‐derived microRNA profile to predict clinical benefit from chemotherapy was evaluated in tissue biopsies and serum from patients with mCRC.

Methods

Samples were prospectively collected from patients (N = 132) who were treated with capecitabine or 5‐FU/LV with oxaliplatin ± bevacizumab. Response evaluation was performed according to RECIST 1.1 after three or four cycles, respectively. Baseline tissue and serum miRNAs expression levels of miR‐17‐5p, miR‐20a‐5p, miR‐30a‐5p, miR‐92a‐3p, miR‐92b‐3p, and miR‐98‐5p were quantified with RT‐qPCR and droplet digital PCR, respectively. Combined predictive performance of selected variables was tested using logistic regression analysis.

Results

From 132 patients, 81 fresh frozen tissue biopsies from metastases and 93 serum samples were available. Based on expression levels of miRNAs in tissue, progressive disease could be predicted with an AUC of 0.85 (95% CI:0.72‐0.91) and response could be predicted with an AUC of 0.70 (95% CI:0.56‐0.80). This did not outperform clinical parameters alone (respectively P = .14 and P = .27). Expression levels of miR‐92a‐3p and miR‐98‐5p in serum significantly improved the predictive value of clinical parameters for response to chemotherapy (AUC 0.74, 95% CI:0.64‐0.84, P = .003) in this cohort.

Conclusions

The additive predictive value to clinical parameters of the tissue‐derived six miRNA profile for clinical benefit could not be validated in patients with mCRC treated with first‐line systemic therapy. Although miR‐92a‐3p and miR‐98‐5p serum levels improved the predictive value of clinical parameters, it remained insufficient for clinical decision‐making.

miRNAs-Based Molecular Signature for KRAS Mutated and Wild Type Colorectal Cancer: An Explorative Study

microRNAs (miRNAs) have been proposed as promising molecular biomarkers for diagnosis, prognosis, and responsive therapeutic targets in different types of cancer, including colorectal cancer (CRC). In this study, we evaluated the expression levels of 84 cancer-associated miRNAs in a cohort of 39 human samples comprising 13 peritumoral and 26 tumoral tissues from surgical specimens of CRC patients. KRAS mutations were detected in 11 tumoral samples. In a first analysis, we found 5 miRNAs (miR-215-5p, miR-9-5p, miR-138-5p, miR378a-3p, and miR-150-5p) that were significantly downregulated and one upregulated (miR-135b-5p) in tumoral tissues compared with the peritumoral tissues. Furthermore, by comparing miRNA profile between KRAS mutated CRC tissues respect to wild type CRC tissues, we found 7 miRNA (miR-27b-3p, miR-191-5p, miR-let7d-5p, miR-15b-5p, miR-98-5p, miR-10a-5p, and miR-149-5p) downregulated in KRAS mutated condition. In conclusion, we have identified a panel of miRNAs that specifically distinguish CRC tissues from peritumoral tissue and a different set of miRNAs specific for CRC with KRAS mutations. These findings may contribute to the discovering of new molecular biomarkers with clinic relevance and might shed light on novel molecular aspects of CRC.

Expression of let-7i and miR-192 is associated with resistance to cisplatin-based chemoradiotherapy in patients with larynx and hypopharynx cancer

OBJECTIVES

The majority of patients with locally advanced larynx or hypopharynx squamous cell carcinoma are treated with organ-preserving chemoradiotherapy (CRT). Clinical outcome following CRT varies greatly. We hypothesized that tumor microRNA (miRNA) expression is predictive for outcome following CRT.

METHODS

Next-generation sequencing (NGS) miRNA profiling was performed on 37 formalin-fixed paraffin-embedded (FFPE) tumor samples. Patients with a recurrence-free survival (RFS) of less than 2 years and patients with late/no recurrence within 2 years were compared by differential expression analysis. Tumor-specific miRNAs were selected based on normal mucosa miRNA expression data from The Cancer Genome Atlas database. A model was constructed to predict outcome using group-regularized penalized logistic ridge regression. Candidate miRNAs were validated by RT-qPCR in the initial sample set as well as in 46 additional samples.

RESULTS

Thirteen miRNAs were differentially expressed (p < 0.05, FDR < 0.1) according to outcome group. Initial class prediction in the NGS cohort (n = 37) resulted in a model combining five miRNAs and disease stage, able to predict CRT outcome with an area under the curve (AUC) of 0.82. In the RT-qPCR cohort (n = 83), 25 patients (30%) experienced early recurrence (median RFS 8 months; median follow-up 42 months). Class prediction resulted in a model combining let-7i-5p, miR-192-5p and disease stage, able to discriminate patients with good versus poor clinical outcome (AUC:0.80).

CONCLUSION

The combined miRNA expression and disease stage prediction model for CRT outcome is superior to using either factor alone. This study indicates NGS miRNA profiling using FFPE specimens is feasible, resulting in clinically relevant biomarkers.