Stage-specific treatment of colorectal cancer: A microRNA-nanocomposite approach.
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ABSTRACT:
SUBMITTER: Fadaka AO
PROVIDER: S-EPMC10759263 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
Publications
Stage-specific treatment of colorectal cancer: A microRNA-nanocomposite approach.
Fadaka Adewale Oluwaseun AO Akinsoji Taiwo T Klein Ashwil A Madiehe Abram Madimabe AM Meyer Mervin M Keyster Marshall M Sikhwivhilu Lucky Mashudu LM Sibuyi Nicole Remaliah Samantha NRS
Journal of pharmaceutical analysis 20230717 11
Colorectal cancer (CRC) is among the leading causes of cancer mortality. The lifetime risk of developing CRC is about 5% in adult males and females. CRC is usually diagnosed at an advanced stage, and at this point therapy has a limited impact on cure rates and long-term survival. Novel and/or improved CRC therapeutic options are needed. The involvement of microRNAs (miRNAs) in cancer development has been reported, and their regulation in many oncogenic pathways suggests their potent tumor suppre ...[more]
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Project description:BackgroundIn recent years, many algorithms have been developed for network-based analysis of differential gene expression in complex diseases. These algorithms use protein-protein interaction (PPI) networks as an integrative framework and identify subnetworks that are coordinately dysregulated in the phenotype of interest.MotivationWhile such dysregulated subnetworks have demonstrated significant improvement over individual gene markers for classifying phenotype, the current state-of-the-art in dysregulated subnetwork discovery is almost exclusively limited to binary phenotype classes. However, many clinical applications require identification of molecular markers for multiple classes.ApproachWe consider the problem of discovering groups of genes whose expression signatures can discriminate multiple phenotype classes. We consider two alternate formulations of this problem (i) an all-vs-all approach that aims to discover subnetworks distinguishing all classes, (ii) a one-vs-all approach that aims to discover subnetworks distinguishing each class from the rest of the classes. For the one-vs-all formulation, we develop a set-cover based algorithm, which aims to identify groups of genes such that at least one gene in the group exhibits differential expression in the target class.ResultsWe test the proposed algorithms in the context of predicting stages of colorectal cancer. Our results show that the set-cover based algorithm identifying "stage-specific" subnetworks outperforms the all-vs-all approaches in classification. We also investigate the merits of utilizing PPI networks in the search for multiple markers, and show that, with correct parameter settings, network-guided search improves performance. Furthermore, we show that assessing statistical significance when selecting features greatly improves classification performance.
| S-EPMC3504924 | biostudies-literature
Project description:BackgroundPalliative 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.MethodsSamples 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.ResultsFrom 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.ConclusionsThe 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.
| S-EPMC7571833 | biostudies-literature
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| S-EPMC7655616 | biostudies-literature
Project description:Distant metastasis is the major cause of mortality in colorectal cancer (CRC). We performed a systemic, comprehensive discovery for expression patterns of metastasis-specific microRNAs (miRNAs) by directly comparing primary CRCs (pCRCs) and matched liver metastases (LMs) and evaluated the feasibility of their clinical application as metastasis-specific biomarkers. CRC metastasis-specific miRNA profiles were generated by analyzing nine pairs of pCRC and LM tissues, followed by quantitative validation in an independent cohort of 58 pairs of matched pCRC and LM tissues. We evaluated associations between miRNA expression and patient survival and ability to predict metastasis in another 84 patients with CRC. Subsequently, associations were quantitatively validated in 175 CRC tissues and 169 serum samples. Kaplan-Meier, Cox regression, and logistic regression analyses were used. All statistical tests were two-sided. Twenty-three miRNAs were identified that were differentially expressed between pCRC and LM (P < .001; FDR < .5). Four miRNAs downregulated in LM (let-7i, miR-10b, miR-221, and miR-320a) and one upregulated miR (miR-885-5p) were quantitatively validated in pCRC (P < .0001). Low let-7i expression in pCRC tissue predicted worsened prognosis (hazard ratio [HR] = 5.0, 95% confidence interval [CI] = 1.0 to 24.4, P = .0479) as well as distant metastasis (odds ratio [OR] = 5.5, 95% CI = 1.1 to 26.8, P = .0334). High miR-10b expression in pCRC tissue independently predicted distant metastasis (OR = 4.9, 95% CI = 1.2 to 19.7, P = .0248). High serum miR-885-5p expression independently predicted prognosis (HR = 2.9, 95% CI = 1.1 to 7.5, P = .0323), LN metastasis (OR = 3.0, 95% CI = 1.3 to 7.2, P = .0116), and distant metastasis (OR = 3.1, 95% CI = 1.0 to 10.0, P = .0456), whereas tissue miR-885-5p expression did not. Expression patterns of miRNAs were confirmed by in situ hybridization. We discovered a metastasis-specific miRNA signature in pCRCs and discovered novel tissue- and serum-based CRC metastasis-specific miRNA biomarkers through intensive validation. These unique miRNAs may be clinically applicable to predict prognosis and distant metastasis in CRC.
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Project description:It is well-known that the conversion of normal colon epithelium to adenoma and then to carcinoma stems from acquired molecular changes in the genome. The genetic basis of colorectal cancer has been elucidated to a certain extent, and much remains to be known about the identity of specific cancer genes that are associated with the advancement of colorectal cancer from one stage to the next. Here in this study we attempted to identify novel cancer genes that could underlie the stage-specific progression and metastasis of colorectal cancer. We conducted a stage-based meta-analysis of the voluminous tumor genome-sequencing data and mined using multiple approaches for novel genes driving the progression to stage-II, stage-III and stage-IV colorectal cancer. The consensus of these driver genes seeded the construction of stage-specific networks, which were then analyzed for the centrality of genes, clustering of subnetworks, and enrichment of gene-ontology processes. Our study identified three novel driver genes as hubs for stage-II progression: DYNC1H1, GRIN2A, GRM1. Four novel driver genes were identified as hubs for stage-III progression: IGF1R, CPS1, SPTA1, DSP. Three novel driver genes were identified as hubs for stage-IV progression: GSK3B, GGT1, EIF2B5. We also identified several non-driver genes that appeared to underscore the progression of colorectal cancer. Our study yielded potential diagnostic biomarkers for colorectal cancer as well as novel stage-specific drug targets for rational intervention. Our methodology is extendable to the analysis of other types of cancer to fill the gaps in our knowledge.
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Project description:BackgroundSeveral studies show that the regulatory impact of microRNAs (miRNAs) is an essential contribution to the pathogenesis of colorectal cancer (CRC). The expression levels of diverse miRNAs are associated with specific clinical diagnoses and prognoses of CRC. However, this association reveals very little actionable information with regard to how or whether to treat a CRC patient. To address this problem, we use miRNA expression data along with other molecular information to predict individual response of CRC cell lines and CRC patients.MethodsA strategy has been developed to join four types of information: molecular, kinetic, genetic and treatment data for prediction of individual treatment response of CRC.ResultsInformation on miRNA regulation, including miRNA target regulation and transcriptional regulation of miRNA, in integrated into an in silico molecular model for colon cancer. This molecular model is applied to study responses of seven CRC cell lines from NCI-60 to ten agents targeting signaling pathways. Predictive results of models without and with implemented miRNA information are compared and advantages are shown for the extended model. Finally, the extended model was applied to the data of 22 CRC patients to predict response to treatments of sirolimus and LY294002. The in silico results can also replicate the oncogenic and tumor suppression roles of miRNA on the therapeutic response as reported in the literature.ConclusionsIn summary, the results reveal that detailed molecular events can be combined with individual genetic data, including gene/miRNA expression data, to enhance in silico prediction of therapeutic response of individual CRC tumors. The study demonstrates that miRNA information can be applied as actionable information regarding individual therapeutic response.
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Project description:The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p < 0.05). A stepwise selection procedure was used to identify nine metabolites that were the most predictive of CRC with areas under the curve (AUCs) of >0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670-0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens.
| S-EPMC7345993 | biostudies-literature
Project description:Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.
| S-EPMC8022234 | biostudies-literature
Project description:The endogenous PP2A inhibitor SET Nuclear Proto-Oncogene (SET) has been reported to play oncogenic roles and determines poor outcomes in colorectal cancer (CRC). Our group previously showed that miR-199b is deregulated in metastatic CRC, and reduced the cell viability and enhanced the sensitivity of CRC cells to standard induction chemotherapy drugs, mainly through direct negative SET regulation. Clinically, miR-199b downregulation was identified as the molecular mechanism responsible for SET overexpression in around half of metastatic CRC patients. However, the potential clinical value of miR-199b in early-stage CRC remains totally unknown. Thus, here we explored the expression levels of this microRNA in a cohort of 171 early-stage CRC patients using real-time polymerase chain reactions. MiR-199b downregulation was found in 21.6% of cases (37 out of 171) and was significantly associated with those patients with a worse Eastern Cooperative Oncology Group (ECOG) status (p = 0.045). Moreover, miR-199b downregulation predicted shorter overall (p < 0.001) and progression-free survival (p = 0.015). As expected, we next immunohistochemically analyzed SET, observing that it was significantly associated with miR-199b in our cohort. However, multivariate analyses showed that miR-199b was an independent biomarker of poor outcomes in early-stage CRC with a predictive value stronger than SET. In conclusion, our results highlight the potential clinical usefulness of miR-199b and suggest that it could represent a novel molecular target in this disease.
| S-EPMC7465379 | biostudies-literature