Identifying a miRNA signature for predicting the stage of breast cancer

Exploring prognostic implications of miRNA signatures and telomere maintenance genes in kidney cancer

Kidney cancer, particularly clear cell renal cell carcinoma (KIRC), presents significant challenges in disease-specific survival. This study investigates the prognostic potential of microRNAs (miRNAs) in kidney cancers, including KIRC and kidney papillary cell carcinoma (KIRP), focusing on their interplay with telomere maintenance genes. Utilizing data from The Cancer Genome Atlas, miRNA expression profiles of 166 KIRC and 168 KIRP patients were analyzed. An evolutionary learning-based kidney survival estimator identified robust miRNA signatures predictive of 5-year survival for both cancer types. For KIRC, a 37-miRNA signature showed a correlation coefficient (R) of 0.82 and mean absolute error (MAE) of 0.65 years. Similarly, for KIRP, a 23-miRNA signature exhibited an R of 0.82 and MAE of 0.64 years, demonstrating comparable predictive accuracy. These signatures also displayed diagnostic potential with receiver operating characteristic curve values between 0.70 and 0.94. Bioinformatics analysis revealed targeting of key telomere-associated genes such as TERT, DKC1, CTC1, and RTEL1 by these miRNAs, implicating crucial pathways such as cellular senescence and proteoglycans in cancer. This study highlights the significant link between miRNAs and telomere genes in kidney cancer survival, offering insights for therapeutic targets and improved prognostic markers.

MicroRNA-1307-3p contributes to breast cancer progression through PRM2

BACKGROUND

Despite advances in screening and therapy, breast cancer (BC) remains the predominant cancer in women globally. Dysregulation of microRNAs (miRNAs) is pivotal in carcinogenesis across various cancers, including BC. Evidence indicates that miR-1307-3p is upregulated in BC tumors, yet its target genes are not fully elucidated. This study aimed to explore how miR-1307-3p regulates BC proliferation, migration, invasion, and angiogenesis and to identify potential target genes.

METHODS

Basal miR-1307-3p levels were quantified in BC cell lines MDA-MB-231 and MCF-7, as well as MCF-10A using quantitative real-time reverse transcription-PCR (RT-qPCR). The impact of miR-1307-3p inhibition on BC cell proliferation, migration, invasion, and angiogenesis was assessed. Nine miRNA-target prediction databases identified potential miR-1307-3p targets. Target expression was validated using RT-qPCR, Western blot, and dual-luciferase reporter assays. MiR-1307-3p was overexpressed in MDA-MB-231 and MCF-7 compared to MCF-10A.

RESULTS

Inhibiting miR-1307-3p significantly reduced BC cell proliferation, migration, invasion, and angiogenesis. Bioinformatics analysis identified 17 potential miR-1307-3p targets, with protamine 2 (PRM2) overexpression confirmed via Western blot and dual-luciferase assays.

CONCLUSION

MiR-1307-3p overexpression in BC promotes proliferation, migration, invasion, and angiogenesis. PRM2 emerges as a novel miR-1307-3p target in BC.

A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study

The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resources and therapies. Computational drug repositioning is an effective strategy that redirects authorized drugs to new therapeutic purposes. This strategy holds significant promise for newly emerging diseases, as drug discovery is a lengthy and expensive process. Through this study, we present an ensemble method based on the convolutional neural network integrated with genetic algorithm and deep forest classifier for virus-drug association prediction (CGDVDA). We generated feature vectors by combining drug chemical structure and virus genomic sequence-based similarities, and extracted prominent deep features by applying the convolutional neural network. The convoluted features are optimized using the genetic algorithm and classified using the ensemble deep forest classifier to predict novel virus-drug associations. The proposed method predicts drugs for COVID-19 and other viral diseases in the dataset. The model could achieve ROC-AUC scores of 0.9159 on fivefold cross-validation. We compared the performance of the model with state-of-the-art approaches and classifiers. The experimental results and case studies illustrate the efficacy of CGDVDA in predicting drugs against viral infectious diseases.

Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival

Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.

CRISPR screens in 3D tumourspheres identified miR-4787-3p as a transcriptional start site miRNA essential for breast tumour-initiating cell growth

Our study employs pooled CRISPR screens, integrating 2D and 3D culture models, to identify miRNAs critical in Breast Cancer (BC) tumoursphere formation. These screens combine with RNA-seq experiments allowing identification of miRNA signatures and targets essential for tumoursphere growth. miR-4787-3p exhibits significant up-regulation in BC, particularly in basal-like BCs, suggesting its association with aggressive disease. Surprisingly, despite its location within the 5'UTR of a protein coding gene, which defines DROSHA-independent transcription start site (TSS)-miRNAs, we find it dependant on both DROSHA and DICER1 for maturation. Inhibition of miR-4787-3p hinders tumoursphere formation, highlighting its potential as a therapeutic target in BC. Our study proposes elevated miR-4787-3p expression as a potential prognostic biomarker for adverse outcomes in BC. We find that protein-coding genes positively selected in the CRISPR screens are enriched of miR-4787-3p targets. Of these targets, we select ARHGAP17, FOXO3A, and PDCD4 as known tumour suppressors in cancer and experimentally validate the interaction of miR-4787-3p with their 3'UTRs. Our work illuminates the molecular mechanisms underpinning miR-4787-3p's oncogenic role in BC. These findings advocate for clinical investigations targeting miR-4787-3p and underscore its prognostic significance, offering promising avenues for tailored therapeutic interventions and prognostic assessments in BC.

Identification of Gene Expression in Different Stages of Breast Cancer with Machine Learning

Determining the tumor origin in humans is vital in clinical applications of molecular diagnostics. Metastatic cancer is usually a very aggressive disease with limited diagnostic procedures, despite the fact that many protocols have been evaluated for their effectiveness in prognostication. Research has shown that dysregulation in miRNAs (a class of non-coding, regulatory RNAs) is remarkably involved in oncogenic conditions. This research paper aims to develop a machine learning model that processes an array of miRNAs in 1097 metastatic tissue samples from patients who suffered from various stages of breast cancer. The suggested machine learning model is fed with miRNA quantitative read count data taken from The Cancer Genome Atlas Data Repository. Two main feature-selection techniques have been used, mainly Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance, to identify the most discriminant and relevant miRNAs for their up-regulated and down-regulated states. These miRNAs are then validated as biological identifiers for each of the four cancer stages in breast tumors. Both machine learning algorithms yield performance scores that are significantly higher than the traditional fold-change approach, particularly in earlier stages of cancer, with Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance achieving accuracy scores of up to 0.983 and 0.931, respectively, compared to 0.920 for the FC method. This study underscores the potential of advanced feature-selection methods in enhancing the accuracy of cancer stage identification, paving the way for improved diagnostic and therapeutic strategies in oncology.

Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow long-term risk of metastases

BACKGROUND

Current clinical markers overestimate the recurrence risk in many lymph node negative (LNN) breast cancer (BC) patients such that a majority of these low-risk patients unnecessarily receive systemic treatments. We tested if differential microRNA expression in primary tumors allows reliable identification of indolent LNN BC patients to provide an improved classification tool for overtreatment reduction in this patient group.

METHODS

We collected freshly frozen primary tumors of 80 LNN BC patients with recurrence and 80 recurrence-free patients (mean follow-up: 20.9 years). The study comprises solely systemically untreated patients to exclude that administered treatments confound the metastasis status. Samples were pairwise matched for clinical-pathological characteristics to minimize dependence of current markers. Patients were classified into risk-subgroups according to the differential microRNA expression of their tumors via classification model building with cross-validation using seven classification methods and a voting scheme. The methodology was validated using available data of two independent cohorts (n = 123, n = 339).

RESULTS

Of the 80 indolent patients (who would all likely receive systemic treatments today) our ultralow-risk classifier correctly identified 37 while keeping a sensitivity of 100% in the recurrence group. Multivariable logistic regression analysis confirmed independence of voting results from current clinical markers. Application of the method in two validation cohorts confirmed successful classification of ultralow-risk BC patients with significantly prolonged recurrence-free survival.

CONCLUSION

Profiles of differential microRNAs expression can identify LNN BC patients who could spare systemic treatments demanded by currently applied classifications. However, further validation studies are required for clinical implementation of the applied methodology.

Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer

Breast cancer (BC) is the most common cancer among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. GSE101124 and GSE182471 datasets were obtained from the Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray classification (PAM50). The circRNA-miRNA-mRNA relationship was investigated using the Cancer-Specific CircRNA, miRDB, miRTarBase, and miRWalk databases. The circRNA-miRNA-mRNA regulatory network was annotated using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The protein-protein interaction network was constructed by the STRING database and visualized by the Cytoscape tool. Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The Disease-Free Survival and Overall Survival analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. We conclude that hsa_circ_0000515/miR-486-5p/SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC subgroup of BC.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Developing a practical neurodevelopmental prediction model for targeting high-risk very preterm infants during visit after NICU: a retrospective national longitudinal cohort study

BACKGROUND

Follow-up visits for very preterm infants (VPI) after hospital discharge is crucial for their neurodevelopmental trajectories, but ensuring their attendance before 12 months corrected age (CA) remains a challenge. Current prediction models focus on future outcomes at discharge, but post-discharge data may enhance predictions of neurodevelopmental trajectories due to brain plasticity. Few studies in this field have utilized machine learning models to achieve this potential benefit with transparency, explainability, and transportability.

METHODS

We developed four prediction models for cognitive or motor function at 24 months CA separately at each follow-up visits, two for the 6-month and two for the 12-month CA visits, using hospitalized and follow-up data of VPI from the Taiwan Premature Infant Follow-up Network from 2010 to 2017. Regression models were employed at 6 months CA, defined as a decline in The Bayley Scales of Infant Development 3rd edition (BSIDIII) composite score > 1 SD between 6- and 24-month CA. The delay models were developed at 12 months CA, defined as a BSIDIII composite score < 85 at 24 months CA. We used an evolutionary-derived machine learning method (EL-NDI) to develop models and compared them to those built by lasso regression, random forest, and support vector machine.

RESULTS

One thousand two hundred forty-four VPI were in the developmental set and the two validation cohorts had 763 and 1347 VPI, respectively. EL-NDI used only 4-10 variables, while the others required 29 or more variables to achieve similar performance. For models at 6 months CA, the area under the receiver operating curve (AUC) of EL-NDI were 0.76-0.81(95% CI, 0.73-0.83) for cognitive regress with 4 variables and 0.79-0.83 (95% CI, 0.76-0.86) for motor regress with 4 variables. For models at 12 months CA, the AUC of EL-NDI were 0.75-0.78 (95% CI, 0.72-0.82) for cognitive delay with 10 variables and 0.73-0.82 (95% CI, 0.72-0.85) for motor delay with 4 variables.

CONCLUSIONS

Our EL-NDI demonstrated good performance using simpler, transparent, explainable models for clinical purpose. Implementing these models for VPI during follow-up visits may facilitate more informed discussions between parents and physicians and identify high-risk infants more effectively for early intervention.

MicroRNA Expression Profile in Early-Stage Breast Cancers

BACKGROUND

Breast cancer is one of the leading causes of cancer deaths in women. Early diagnosis offers the best hope for a cure. Ductal carcinoma in situ is considered a precursor of invasive ductal carcinoma of the breast. In this study, we carried out microRNA sequencing from 7 ductal carcinoma in situ (DCIS), 6 infiltrating ductal carcinomas (IDC Stage IIA) with paired normal, and 5 unpaired normal breast tissue samples.

METHODS

We have deployed miRge for microRNA analysis, DESeq for differential expression analysis, and Cytoscape for competing endogenous RNA network investigation.

RESULTS

Here, we identified 76 miRNAs that were differentially expressed in DCIS and IDC. Additionally, we provide preliminary evidence of miR-365b-3p and miR-7-1-3p being overexpressed, and miR-6507-5p, miR-487b-3p, and miR-654-3p being downregulated in DCIS relative to normal breast tissue. We also identified a miRNA miR-766-3p that was overexpressed in earlystage IDCs. The overexpression of miR-301a-3p in DCIS and IDC was confirmed in 32 independent breast cancer tissue samples.

CONCLUSION

Higher expression of miR-301a-3p is associated with poor overall survival in The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, indicating that it may be associated with DCIS at high risk of progressing to IDC and warrants deeper investigation.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC.

METHODS

The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression.

RESULTS

HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis.

CONCLUSIONS

Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model

BACKGROUND

Extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.

METHODS

There were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets.

RESULTS

The EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE.

CONCLUSIONS

The EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Survival associated miRNA signature in patients with head and neck carcinomas

Head and neck carcinoma (HNSC) is often diagnosed at advanced stage, incurring poor patient outcome. Despite of advances in chemoradiation and surgery approaches, limited improvements in survival rates of HNSC have been observed over the last decade. Accumulating evidences have demonstrated the importance of microRNAs (miRNAs) in carcinogenesis. In this context, we sought to identify a miRNA signature associated with the survival time in patients with HNSC. This study proposed a survival estimation method called HNSC-Sig that identified a miRNA signature consists of 25 miRNAs associated with the survival in 133 patients with HNSC. HNSC-Sig achieved 10-fold cross validation a mean correlation coefficient and a mean absolute error of 0.85 ± 0.01 and 0.46 ± 0.02 years, respectively, between actual and estimated survival times. The survival analysis revealed that five miRNAs, hsa-miR-3605-3p, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-497-5p, and hsa-miR-374a-5p, were significantly associated with prognosis in patients with HNSC. Comparing the relative expression difference of top 10 prioritized miRNAs, eight miRNAs, hsa-miR-629-3p, hsa-miR-3127-5p, hsa-miR-221-3p, hsa-miR-501-5p, hsa-miR-491-5p, hsa-miR-149-3p, hsa-miR-3934-5p, and hsa-miR-3170, were significantly expressed between cancer and normal groups. In addition, biological relevance, disease association, and target interactions of the miRNA signature were discussed. Our results suggest that identified miRNA signature have potential to serve as biomarker for diagnosis and clinical practice in HNSC.

LncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis in-silico and clinical prospect correlated-to histologic grades-based CRC stratification: A step toward ncRNA Precision

The oncogenic effects of long non-coding RNA (lncRNA) Nicotinamide Nucleotide Transhydrogenase-antisense RNA1 (NNT-AS1) role in colorectal cancer (CRC) hasn't been sufficiently inspected in relation to the Homo sapiens (hsa)-microRNA (miR)- 485-5p/ heat shock protein 90 (HSP90) axis, clinically. qRT-PCR was performed to detect lncRNA NNT-AS1 and hsa-miR-485-5p expression levels in 60 Egyptian patients' sera. HSP90 serum level was quantified using Enzyme-linked immunosorbent assay (ELISA). The relative expression level of the studied non-coding RNAs as well as the HSP90 ELISA concentration were correlated with patients clinicopathological characteristics and correlated to each other. The axis diagnostic utility in comparison with carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) tumor markers (TMs) was studied by receiver operating characteristic (ROC) curve analysis. The relative lncRNA NNT-AS1 expression level fold change 56.7 (13.5-112) and HSP90 protein ELISA level 6.68 (5.14-8.77) (ng/mL) were elevated, while, for hsa-miR-485-5p 0.0474 (0.0236-0.135) expression fold change was repressed in CRC Egyptian patients' cohort sera, being compared to 28 apparently healthy control subjects. LncRNA NNT-AS1 specificity is 96.4% and a sensitivity of 91.7%, hsa-miR-485-5p showed 96.4% specificity, 90% sensitivity, and for HSP90 89.3%, 70% specificity and sensitivity, respectively. Those specificities and sensitivities were superior to the classical CRC TMs. A significant negative correlation was found between hsa-miR-485-5p with lncRNA NNT-AS1 (r = -0.933) expression fold change or with HSP90 protein blood level (r = -0.997), but, significant positive correlation was there between lncRNA NNT-AS1 and HSP90 (r = 0.927). LncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis could be a prospect for CRC development as well as diagnosis. Being correlated and related to CRC histologic grades 1-3, therefore, lncRNA NNT-AS1/hsa-miR-485-5p/HSP90 axis (not individually) expression approved clinically and in silico, could aid treatment precision.

Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

In electronics manufacturing, surface defect detection is very important for product quality control, and defective products can cause severe customer complaints. At the same time, in the manufacturing process, the cycle time of each product is usually very short. Furthermore, high-resolution input images from high-resolution industrial cameras are necessary to meet the requirements for high quality control standards. Hence, how to design an accurate object detector with real-time inference speed that can accept high-resolution input is an important task. In this work, an accurate YOLO-style object detector was designed, ATT-YOLO, which uses only one self-attention module, many-scale feature extraction and integration in the backbone and feature pyramid, and an improved auto-anchor design to address this problem. There are few datasets for surface detection in electronics manufacturing. Hence, we curated a dataset consisting of 14,478 laptop surface defects, on which ATT-YOLO achieved 92.8% mAP0.5 for the binary-class object detection task. We also further verified our design on the COCO benchmark dataset. Considering both computation costs and the performance of object detectors, ATT-YOLO outperforms several state-of-the-art and lightweight object detectors on the COCO dataset. It achieves a 44.9% mAP score and 21.8 GFLOPs, which is better than the compared models including YOLOv8-small (44.9%, 28.6G), YOLOv7-tiny-SiLU (38.7%, 13.8G), YOLOv6-small (43.1%, 44.2G), pp-YOLOE-small (42.7%, 17.4G), YOLOX-small (39.6%, 26.8G), and YOLOv5-small (36.7%, 17.2G). We hope that this work can serve as a useful reference for the utilization of attention-based networks in real-world situations.

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients' prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

Circular RNAs: New layer of complexity evading breast cancer heterogeneity

Advances in high-throughput sequencing techniques and bioinformatic analysis have refuted the "junk" RNA hypothesis that was claimed against non-coding RNAs (ncRNAs). Circular RNAs (circRNAs); a class of single-stranded covalently closed loop RNA molecules have recently emerged as stable epigenetic regulators. Although the exact regulatory role of circRNAs is still to be clarified, it has been proven that circRNAs could exert their functions by interacting with other ncRNAs or proteins in their own physiologically authentic environment, regulating multiple cellular signaling pathways and other classes of ncRNAs. CircRNAs have also been reported to exhibit a tissue-specific expression and have been associated with the malignant transformation process of several hematological and solid malignancies. Along this line of reasoning, this review aims to highlight the importance of circRNAs in Breast Cancer (BC), which is ranked as the most prevalent malignancy among females. Notwithstanding the substantial efforts to develop a suitable anticancer therapeutic regimen against the heterogenous BC, inter- and intra-tumoral heterogeneity have resulted in an arduous challenge for drug development research, which in turn necessitates the investigation of other markers to be therapeutically targeted. Herein, the potential of circRNAs as possible diagnostic and prognostic biomarkers have been highlighted together with their possible application as novel therapeutic targets.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

MiR-211 plays a dual role in cancer development: From tumor suppressor to tumor enhancer

Cancer is a general term for more than 100 unique malignancies in different organs of the body. Each cancer type and subtype has its own unique genetic, epigenetic, and cellular factors accountable for malignant progression and metastasis. Small non-coding RNAs called miRNAs target mRNAs and play a vital part in the pathogenesis of human diseases, specifically cancer. Recent investigations provided knowledge of the deregulation of miR-211 in various cancer types and disclosed that miR-211 has an oncogenic or tumor-suppressive impact on tumourigenesis and cancer development. Moreover, recent discoveries which clarify the essential functions of miR-211 might provide proof for its prognosis, diagnostic and therapeutic impact on cancer. Thereby, this review will discuss recent findings regarding miR-211 expression level, target genes, and mechanisms in different cancers. In addition, the most recent results that propose miR-211 usefulness as a noninvasive biomarker and therapeutic factor for the diagnosis and treatment of cancer will be explained.

Clinical significance of circulating tumor cell (CTC)-specific microRNA (miRNA) in breast cancer

As a noninvasive method, circulating tumor cell (CTC) provides ideal liquid biopsy specimens for early cancer screening and diagnosis. CTCs detection in breast cancer is correlated with patient prognosis such as disease-free survival (DFS) and overall survival (OS). Besides, accumulating evidence supported that CTCs count may be indicator for chemotherapy response as well. The functional roles of microRNA (miRNA) in breast cancer have been well-recognized for the last few years. Due to its stability in circulation, numerous studies have proven that circulating miRNA may serve as promising diagnostic and prognostic biomarkers in breast cancer. The potential ability of miRNAs in disease screening, staging or even molecular subtype classification makes them valuable tools for early breast cancer patients. It would be of great significance to characterize the miRNA expression profile in CTCs, which could provide reliable biological information originated from tumor. However, some issues need to be addressed before the utility of CTC-specific miRNAs in clinical setting. Taken together, we believe that CTC-specific miRNA detection will be trend for early breast cancer screening, diagnosis and treatment monitor in near future.

Association between genome-wide epigenetic and genetic alterations in breast cancer tissue and response to HER2-targeted therapies in HER2-positive breast cancer patients: new findings and a systematic review

Recent evidence suggests that genetic and epigenetic mechanisms might be associated with acquired resistance to cancer therapies. The aim of this study was to assess the association of genome-wide genetic and epigenetic alterations with the response to anti-HER2 agents in HER2-positive breast cancer patients. PubMed was screened for articles published until March 2021 on observational studies investigating the association of genome-wide genetic and epigenetic alterations, measured in breast cancer tissues or blood, with the response to targeted treatment in HER2-positive breast cancer patients. Sixteen studies were included in the review along with ours, in which we compared the genome-wide DNA methylation pattern in breast tumor tissues of patients who acquired resistance to treatment (case group, n = 6) to that of patients who did not develop resistance (control group, n = 6). Among genes identified as differentially methylated between the breast cancer tissue of cases and controls, one of them, PRKACA, was also reported as differentially expressed in two studies included in the review. Although included studies were heterogeneous in terms of methodology and study population, our review suggests that genes of the PI3K pathway may play an important role in developing resistance to anti-HER2 agents in breast cancer patients. Genome-wide genetic and epigenetic alterations measured in breast cancer tissue or blood might be promising markers of resistance to anti-HER2 agents in HER2-positive breast cancer patients. Further studies are needed to confirm these data.

IsomiR-eQTL: A Cancer-Specific Expression Quantitative Trait Loci Database of miRNAs and Their Isoforms

The identification of expression quantitative trait loci (eQTL) is an important component in efforts to understand how genetic variants influence disease risk. MicroRNAs (miRNAs) are short noncoding RNA molecules capable of regulating the expression of several genes simultaneously. Recently, several novel isomers of miRNAs (isomiRs) that differ slightly in length and sequence composition compared to their canonical miRNAs have been reported. Here we present isomiR-eQTL, a user-friendly database designed to help researchers find single nucleotide polymorphisms (SNPs) that can impact miRNA (miR-eQTL) and isomiR expression (isomiR-eQTL) in 30 cancer types. The isomiR-eQTL includes a total of 152,671 miR-eQTLs and 2,390,805 isomiR-eQTLs at a false discovery rate (FDR) of 0.05. It also includes 65,733 miR-eQTLs overlapping known cancer-associated loci identified through genome-wide association studies (GWAS). To the best of our knowledge, this is the first study investigating the impact of SNPs on isomiR expression at the genome-wide level. This database may pave the way for researchers toward finding a model for personalised medicine in which miRNAs, isomiRs, and genotypes are utilised.

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

BACKGROUND

We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer.

METHODS

In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used.

RESULTS

Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66.

CONCLUSIONS

The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.

Noncoding RNome as Enabling Biomarkers for Precision Health

Noncoding RNAs (ncRNAs), in the form of structural, catalytic or regulatory RNAs, have emerged to be critical effectors of many biological processes. With the advent of new technologies, we have begun to appreciate how intracellular and circulatory ncRNAs elegantly choreograph the regulation of gene expression and protein function(s) in the cell. Armed with this knowledge, the clinical utility of ncRNAs as biomarkers has been recently tested in a wide range of human diseases. In this review, we examine how critical factors govern the success of interrogating ncRNA biomarker expression in liquid biopsies and tissues to enhance our current clinical management of human diseases, particularly in the context of cancer. We also discuss strategies to overcome key challenges that preclude ncRNAs from becoming standard-of-care clinical biomarkers, including sample pre-analytics standardization, data cross-validation with closer attention to discordant findings, as well as correlation with clinical outcomes. Although harnessing multi-modal information from disease-associated noncoding RNome (ncRNome) in biofluids or in tissues using artificial intelligence or machine learning is at the nascent stage, it will undoubtedly fuel the community adoption of precision population health.

miR-623 Targets Metalloproteinase-1 and Attenuates Extravasation of Brain Metastatic Triple-Negative Breast Cancer Cells

Background

Most breast cancer-related deaths result from metastasis. Understanding the molecular basis of metastasis is needed for the development of effective targeted and preventive strategies. Matrix metalloproteinase-1 (MMP1) plays an important role in brain metastasis (BM) of triple-negative breast cancer (TNBC) by promoting extravasation of cancer cells across the brain endothelium (BE). MMP1 expression is controlled by endogenous microRNAs. Preliminary bioinformatics analysis has revealed that miR-623, known to target the 3ʹUTR of MMP1, is significantly downregulated in brain metastatic tumors compared to primary BC tumors. However, the involvement of miR-623 in MMP1 upregulation in breast cancer brain metastatic cells (BCBMC) remains unexplored. Here, we investigated the role of miR-623 in MMP1 regulation and its impact on the extravasation of TNBC cells through the BE in vitro.

Materials and Methods

A loss-and-gain of function method was employed to address the effect of miR-623 modulation on MMP1 expression. MMP1 regulation by miR-623 was investigated by real-time PCR, western blot, luciferase and transwell migration assays using an in vitro human BE model.

Results

Our results confirmed that brain metastatic TNBC cells express lower levels of miR-623 compared with cells having low propensity to spread toward the brain. miR-623 binds to the 3′-untranslated region of MMP1 transcript and downregulates its expression. Restoring miR-623 expression significantly decreased MMP1 expression, preserved the endothelial barrier integrity, and attenuated transmigration of BCBMC through the BE.

Conclusion

Our study elucidates, for the first time, the crucial role of miR-623 as MMP1 direct regulator in BCBMC and sheds light on miR-623 as a novel therapeutic target that can be exploited to predict and prevent brain metastasis in TNBC. Importantly, the presents study helps in unraveling a brain metastasis-specific microRNA signature in TNBC that can be used as a guide to personalized metastasis prediction and preventive approach with better therapeutic outcome.

Visualizing MiRNA Regulation of Apoptosis for Investigating the Feasibility of MiRNA-Targeted Therapy Using a Fluorescent Nanoprobe

MiRNA-targeted therapy is an active research field in precision cancer therapy. Studying the effect of miRNA expression changes on apoptosis is important for evaluating miRNA-targeted therapy and realizing personalized precision therapy for cancer patients. Here, a new fluorescent nanoprobe was designed for the simultaneous imaging of miRNA-21 and apoptotic protein caspase-3 in cancer cells by using gold nanoparticles as the core and polydopamine as the shell. Confocal imaging indicated that the nanoprobe could be successfully applied for in situ monitoring of miRNA regulation of apoptosis. This design strategy is critical for investigating the feasibility of miRNA-targeted therapy, screening new anti-cancer drugs targeting miRNA, and developing personalized treatment plans.

A BIRC5High COD1Low Cancer Tissue Phenotype Indicates Poorer Prognosis of Metastatic Breast Cancer Patients

Extensive data research is helpful to find sensitive biomarkers for prognostic prediction of metastatic breast cancer. Through analyzing multiple GEO datasets, literature retrieval, and verified in GEPIA datasets, we identify BIRC5 (Baculoviral IAP repeat containing 5) and CDO1 (Cysteine dioxygenase type 1) as DEGs (differentially expressed genes) between breast tumor and normal tissue and DEGs between metastatic breast cancer and breast cancer in situ. Then, we performed a series of in silico studies on BIRC5 and CDO1 using online tools including the UALCAN, TIMER, TCGA-BRCA, LinkedOmics Kaplan-Meier Plotter, and an R script for analysis. To verify the association of 2 genes expression and patients’ clinical data, we detected BIRC5 and CDO1 mRNA in the tissue of 48 breast cancer patients. The results showed the tumor with BIRC5^high CDO1^low expression generally indicated patients’ shorter overall (OS) and relapse-free survival (RFS). Specifically, BIRC5 and CDO1 levels significantly affect OS or RFS in patients with Lymph node metastasis and molecular subtypes of TNBC (triple-negative breast cancer) and Luminal A. A BIRC5^high tumor displayed a purer tumor purity and expressed more KIR receptors on NK cells while activating more FOXP3⁺CD25⁺ Treg cells. The CDO1^low tumors infiltrated with more immunocytes leading to less tumor purity. In our verified experiment, BIRC5 mRNA level in patients with stage III and over was significantly higher than in patients with stage 0 to II, but there were no significant differences among molecular subtyping groups; TNBC tissue expressed lower CDO1 mRNA level than HER2⁺ and Luminal type cancer tissue. In conclusion, a BIRC5^high CDO1^low expression type in breast cancer tissue indicates a poorer prognosis of patients. The potential mechanism might be increased BIRC5 expression in cancer tissue is likely to accompany NK cells inhibition, activating more Treg cells, and lacking effective CD8⁺ T cells proliferation. Meanwhile, CDO1 level is positively related to more immunocytes infiltration.

Prognosticating 2-Year Survival Rate of Breast Cancer Patients Through Plasma miRNA-21 and Other Associating Factors

Background

miRNA-21, one of breast cancer (BC) predictive markers, is now gaining cardinal attention from researchers worldwide to evaluate BC patients' survival rate. However, cancer staging, hormonal status, and other BC markers still have to be discussed. We aim to determine the relationship between miRNA-21 and associating factors such as BC staging, other tumor markers, and hormonal status to predict the 2-year survival rate of BC patients.

Methods

We conducted a prospective cohort study on 49 BC patients (26 early stage, 23 advanced stage). Apart from cancer staging, we also examined CEA, Ca15-3, and hormonal status (ER, PR, Her2) and correlated them with miRNA-21 to predict 2-year survival rate. We did bivariate, multivariate, and survival analyses to determine the link between miRNA-21 and those factors to prognosticate on 2-year survival rate.

Results

There are significances between advanced and loco-regional stage (p < 0.001); high and low miRNA-21 (p = 0.002) and CA 15-3 (p = 0.001), and low survival rate in patients with ER/PR-Her2- status (p=0.0015). Cox proportional hazard showed miRNA-21 (Adjusted HR 1.41; 95% CI = 1.205-1.632), cancer stage (Adjusted HR 9.5; 95% CI = 1.378-20.683), and CA15-3 (Adjusted HR 4.64; 95% CI = 1.548-13.931) affected patients' mortality within 2 years.

Conclusion

Low two-year survival rate depends on miRNA-21, cancer stage, CA15-3, and ER/PR-Her2-. Cancer stage is robustly associated with miRNA-21 in predicting 2-year survival rate.

The Significance of Tumor Microenvironment Score for Breast Cancer Patients

Purpose

This study was designed to clarify the prognostic value of tumor microenvironment score and abnormal genomic alterations in TME for breast cancer patients.

Method

The TCGA-BRCA data were downloaded from TCGA and analyzed with R software. The results from analyses were further validated using the dataset from GSE96058, GSE124647, and GSE25066.

Results

After analyzing the TCGA data and verifying it with the GEO data, we developed a TMEscore model based on the TME infiltration pattern and validated it in 3273 breast cancer patients. The results suggested that our TMEscore model has high prognostic value. TME features with the TMEscore model can help to predict breast cancer patients' response to immunotherapy and provide new strategies for breast cancer treatment. Signature 24 was first found in breast cancer. In focal SCNAs, a total of 95 amplified genes and 169 deletion genes in the TMEscore high group were found to be significantly related to the prognosis of breast cancer patients, while 61 amplified genes and 174 deletion genes in the TMEscore low group were identified. LRRC48, CFAP69, and cg25726128 were first discovered and reported to be related to the survival of breast cancer patients. We identified specific mutation signatures that correlate with TMEscore and prognosis.

Conclusion

TMEscore model has high predictive value regarding prognosis and patients' response to immunotherapy. Signature 24 was first found in breast cancer. Specific mutation signatures that correlate with TMEscore and prognosis might be used for providing additional indicators for disease evaluation.

The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer

Breast cancer continues to be a major global problem with significant mortality associated with advanced stage and metastases at clinical presentation. However, several findings suggest that metastasis is indeed an early occurrence. The standard diagnostic techniques such as invasive core needle biopsy, serological protein marker assays, and non-invasive radiological imaging do not provide information about the presence and molecular profile of small fractions of early metastatic tumor cells which are prematurely dispersed in the circulatory system. These circulating tumor cells (CTCs) diverge from the primary tumors as clusters with a defined secretome comprised of circulating cell-free nucleic acids and small microRNAs (miRNAs). These circulatory biomarkers provide a blueprint of the mutational profile of the tumor burden and tumor associated alterations in the molecular signaling pathways involved in oncogenesis. Amidst the multitude of circulatory biomarkers, miRNAs serve as relatively stable and precise biomarkers in the blood for the early detection of CTCs, and promote step-wise disease progression by executing paracrine signaling that transforms the microenvironment to guide the metastatic CTCs to anchor at a conducive new organ. Random sampling of easily accessible patient blood or its serum/plasma derivatives and other bodily fluids collectively known as liquid biopsy (LB), forms an efficient alternative to tissue biopsies. In this review, we discuss in detail the divergence of early metastases as CTCs and the involvement of miRNAs as detectable blood-based diagnostic biomarkers that warrant a timely screening of cancer, serial monitoring of therapeutic response, and the dynamic molecular adaptations induced by miRNAs on CTCs in guiding primary and second-line systemic therapy.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Exosomal-microRNA transcriptome profiling of Parental and CSC-like MDA-MB-231 cells in response to cisplatin treatment

Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype with higher risk of metastasis and cancer reoccurrence. Cisplatin is one of the potential anticancer drugs for treating TNBC, where its effectiveness remains challenged by frequent occurrence of cisplatin resistance. Since acquirement of drug resistance often being associated with presence of cancer stem cells (CSCs), investigation has been conducted, suggesting CSC-like subpopulation to be more resistant to cisplatin than their parental counterpart. On the other hand, plethora evidences showed the transmission of exosomal-miRNAs are capable of promoting drug resistance in breast cancers. In this study, we aim to elucidate the differential expression of exosomal-microRNAs profile and reveal the potential target genes in correlation to cisplatin resistance associated with CSC-like subpopulation by using TNBC cell line (MDA-MB-231). Utilizing next generation sequencing and Nanostring techniques, cisplatin-induced dysregulation of exosomal-miRNAs were evaluated in maximal for CSC-like subpopulation as compared to parental cells. Intriguingly, more oncogenic exosomal-miRNAs profile was detected from treated CSC-like subpopulation, which may correlate to enhancement of drug resistance and maintenance of CSCs. In treated CSC-like subpopulation, unique clusters of exosomal-miRNAs namely miR-221-3p, miR-196a-5p, miR-17-5p and miR-126-3p were predicted to target on six genes (ATXN1, LATS1, GSK3β, ITGA6, JAG1 and MYC), aligned with previous finding which demonstrated dysregulation of these genes in treated CSC-like subpopulation. Our results highlight the potential correlation of exosomal-miRNAs and their target genes as well as novel perspectives of the corresponding pathways that may be essential to contribute to the attenuated cytotoxicity of cisplatin in CSC-like subpopulation.

MicroRNA signature for estimating the survival time in patients with bladder urothelial carcinoma

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

•

Differences exist in the amino acids within the S protein of diverse host species CoVs

•

We developed SPIKES to identify informative properties of S protein

•

SARS-CoV-2 variants have amino acid changes that alter infection and transmission

•

The SPIKES identified changes in S protein properties from animal to human host CoVs

Identification of novel biomarkers in breast cancer via integrated bioinformatics analysis and experimental validation

Breast cancer (BC), an extremely aggressive malignant tumor, causes a large number of deaths worldwide. In this study, we pooled profile datasets from three cohorts to illuminate the underlying key genes and pathways of BC. Expression profiles GSE42568, GSE45827, and GSE124646, including 244 BC tissues and 28 normal breast tissues, were integrated and analyzed. Differentially expressed genes (DEGs) were screened out based on these three datasets. Functional analysis including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plugin were utilized to visualize protein protein interaction (PPI) of these DEGs. The module with the highest connectivity of gene interactions was selected for further analysis. All of these hub genes had a significantly worse prognosis in BC by survival analysis. Additionally, four genes (CDK1, CDC20, AURKA, and MCM4) dramatically were enriched in oocyte meiosis and cell cycle pathways through re-analysis of DAVID. Moreover, the mRNA and protein levels of CDK1, CDC20, AURKA, and MCM4 were significantly increased in BC patients. In addition, knockdown of CDK1 and CDC20 by small interfering RNA remarkably suppressed cell migration and invasion in MCF-7 and MDA-MB-231 cells. In conclusion, our results suggested that CDK1, CDC20, AURKA, and MCM4 were reliable biomarkers of BC via bioinformatics analysis and experimental validation and may act as prospective targets for BC diagnosis and treatment.

Human microRNA similarity in breast cancer

MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.

Variances in the Expression of mRNAs and miRNAs Related to the Histaminergic System in Endometrioid Endometrial Cancer

Research has indicated higher concentrations of histamine and polyamine in endometrioid tissue in comparison with healthy tissue. The aim of this study was to evaluate changes in the expression patterns of messenger RNA (mRNAs) and microRNA (miRNAs) related to the histaminergic system in endometrial samples and whole blood in women with endometrioid endometrial cancer. The study group consisted of 30 women with endometrioid endometrial cancer qualified for hysterectomy (G1 well-differentiated, 15 cases; G2 moderately differentiated, 8 cases; and G3 poorly differentiated, 7 cases). The control group included 30 women with no neoplastic changes during routine gynecological examinations. The molecular analysis consisted of the microarray analysis of mRNAs and miRNAs related to the histaminergic system, reverse-transcription quantitative polymerase chain reaction (RTqPCR), and enzyme-linked immunosorbent assay (ELISA). Out of 65 mRNAs connected with the histaminergic system, 10 differentiate the samples of tissue and blood obtained from patients with endometrioid endometrial cancer in comparison with the control group (p < 0.05). mRNA histamine receptor 1,3 (HRH1, HRH3), and solute carrier family 22 member 3 (SLC23A2) differentiating samples of endometrioid endometrial cancer independent of either G or control. The highest probability of interaction, based on the target score miRDB, between the selected miRNAs and mRNAs was found for the hybrids hsa-miR-1-3p and endothelin 1 (END1), hsa-miR-27a-5β and SLC23A2. The selected mRNA and miRNA transcripts seem to be promising for molecularly targeted therapies in the context of endometrioid endometrial cancer.

Hydroxyurea-loaded Fe3O4/SiO2/chitosan-g-mPEG2000 nanoparticles; pH-dependent drug release and evaluation of cell cycle arrest and altering p53 and lincRNA-p21 genes expression

Carbohydrate polymers were widely used in pharmaceuticals and drug delivery systems due to their biodegradability and biocompatibility. Among them, chitosan (Cs) has been considered in many new drug delivery systems. Poly(ethylene glycol) as a hydrophilic polymer can increase the solubility and stealth functions of nanocarriers. The Fe₃O₄ nanoparticles functionalized with polymers act as non-toxic drug vehicles for tumor targeting under external magnetic fields. In present study, the Fe₃O₄/SiO₂-NH₂ nanoparticles were prepared and then functionalized with methoxy-PEGylated chitosan (Cs-g-mPEG2000) and the hydroxyurea (HU) was loaded on this nanoparticles. The structure, crystallinity, and morphology of HU/Fe₃O₄/SiO₂/Cs-g-mPEG2000 were determined using spectroscopic and electron microscopy analysis. Encapsulation efficiency of HU and the percentage of loading and release rate at different pH values at 37 °C were examined. Maximum drug release was observed at pH = 7.4. According to TEM results, the nanoparticle sizes were between 18 and 157 nm. The cytotoxicity effect of HU-loaded nanoparticles against MCF-7 human breast cancer cell was evaluated using MTT assay and cell cycle arrest analysis. The inhibitory concentration (IC50) values were 249 and 85 μg/mL on the MCF-7 cell line compared to the control group in 24 h and 96 h, respectively. In addition, the expression of p53 and lincRNA-P21 genes in treated cells and control group was assessed using real-time PCR, and the results showed that the ratio of p53 expression to lincRNA-P21 in MCF-7 cells was significantly increased (P < 0.05). The cell cycle arrested in the S-phase and the population of cells increased 1.3-fold compared to the control group.

Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

BACKGROUND AND AIMS

Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection.

METHODS

Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery.

RESULTS

A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only.

CONCLUSION

The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

SPTBN1 inhibits growth and epithelial-mesenchymal transition in breast cancer by downregulating miR-21

SPTBN1 (spectrin beta, non-erythrocytic 1) has been linked to tumor progression and epithelial-mesenchymal transition (EMT). However, the role of SPTBN1 has yet to be investigated in breast cancer. This study aimed to evaluate the viability, growth, and migration ability of the breast cancer cell line MDA-MB-231 and BT549 using CCK-8 assay, xenograft models, and Transwell assays. The expression of SPTBN1, EMT-related genes, and miRNA21 in breast cancer cells and tissues were assessed by quantitative real-time polymerase chain reaction (qPCR) and Western blot. SPTBN1 staining of breast cancer tissues was analyzed by the Human Protein Atlas databases. Both chromatin immunoprecipitation qPCR and immunofluorescence were performed to detect how SPTBN1 regulates miRNA21. Our results showed that the expression of SPTBN1 in primary breast cancer tumors was dramatically lower than that in normal tissues and that lower levels of SPTBN1 were associated with significantly shorter progression-free survival. We also discovered that the loss of SPTBN1 promotes EMT, the viability of MDA-MB-231 and BT549 in vitro, and the growth of MDA-MB-231 tumor xenografts in vivo by upregulating miR-21 level. Furthermore, loss of SPTBN1-mediated miR-21 upregulation was dependent on the stability and nuclear translocation of NF-κB p65. Therefore, SPTBN1 is a pivotal regulator that inhibits EMT and the growth of breast cancer.

Perception of breast cancer risk factors: Dysregulation of TGF-β/miRNA axis in Pakistani females

Breast cancer poses a serious health risk for women throughout the world. Among the Asian population, Pakistani women have the highest risk of developing breast cancer. One out of nine women is diagnosed with breast cancer in Pakistan. The etiology and the risk factor leading to breast cancer are largely unknown. In the current study the risk factors that are most pertinent to the Pakistani population, the etiology, molecular mechanisms of tumor progression, and therapeutic targets of breast cancer are studied. A correlative, cross-sectional, descriptive, and questionnaire-based study was designed to predict the risk factors in breast cancer patients. Invasive Ductal Carcinoma (90%) and grade-II tumor (73.2%) formation are more common in our patient’s data set. Clinical parameters such as mean age of 47.5 years (SD ± 11.17), disturbed menstrual cycle (> 2), cousin marriages (repeated), and lactation period (< 0.5 Y) along with stress, dietary and environmental factors have an essential role in the development of breast cancer. In addition to this in silico analysis was performed to screen the miRNA regulating the TGF-beta pathway using TargetScanHuman, and correlation was depicted through Mindjet Manager. The information thus obtained was observed in breast cancer clinical samples both in peripheral blood mononuclear cells, and biopsy through quantitative real-time PCR. There was a significant dysregulation (**P>0.001) of the TGF-β1 signaling pathway and the miRNAs (miR-29a, miR-140, and miR-148a) in patients’ biopsy in grade and stage specifically, correlated with expression in blood samples. miRNAs (miR-29a and miR-140, miR-148a) can be an effective diagnostic and prognostic marker as they regulate SMAD4 and SMAD2 expression respectively in breast cancer blood and biopsy samples. Therefore, proactive therapeutic strategies can be devised considering negatively regulated cascade genes and amalgamated miRNAs to control breast cancer better.

Predicting the Risk Genes of Autism Spectrum Disorders

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine–based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

Evaluation of miRNAs 9 and 342 expressions in sera as diagnostic and prognostic biomarkers for breast cancer

OBJECTIVE

Molecular markers for the detection of breast cancer and its different types, grades, and stages lack enough sensitivity and specificity. This study evaluates the expression of miRNAs 9 and 342 in sera of different types, grades, and stages of BC. Moreover, the assessment of their sensitivity, specificity, diagnostic, and prognostic role in detecting different types of BC.

METHODS

Blood was collected from 200 females outpatients, divided into five groups each 40 subjects: control, benign breast tumor, estrogen receptor (ER+)/progesterone receptor (PR+) BC, human epidermal growth factor receptor (HER+) BC, and triple-negative BC. BC subjects were further subdivided according to grade and stage. Expressions of miRNAs 9 and 342 were measured for all subjects by real-time polymerase chain reaction (RT-PCR).

RESULTS

Results showed that serum expression of both miRNAs 9 and 342 can be used for the diagnosis of different types of BC. Their expression can be used to significantly differentiate between different grades and stages of BC. MiRNAs 9 and 342 showed high sensitivity of 92.5% and specificity of (81.2 and 88.7%), respectively, for triple-negative BC.

CONCLUSION

The expressions of miRNAs 9 and 342 provide potential roles as serological biomarkers for the diagnosis and prognosis of different types, grades, and stages of BC.

Differences in plasma microRNA content impair microRNA-based signature for breast cancer diagnosis in cohorts recruited from heterogeneous environmental sites

Circulating microRNAs are non-invasive biomarkers that can be used for breast cancer diagnosis. However, differences in cancer tissue microRNA expression are observed in populations with different genetic/environmental backgrounds. This work aims at checking if a previously identified diagnostic circulating microRNA signature is efficient in other genetic and environmental contexts, and if a universal circulating signature might be possible. Two populations are used: women recruited in Belgium and Rwanda. Breast cancer patients and healthy controls were recruited in both populations (Belgium: 143 primary breast cancers and 136 healthy controls; Rwanda: 82 primary breast cancers and 73 healthy controls; Ntot = 434), and cohorts with matched age and cancer subtypes were compared. Plasmatic microRNA profiling was performed by RT-qPCR. Random Forest was used to (1) evaluate the performances of the previously described breast cancer diagnostic tool identified in Belgian-recruited cohorts on Rwandan-recruited cohorts and vice versa; (2) define new diagnostic signatures common to both recruitment sites; (3) define new diagnostic signatures efficient in the Rwandan population. None of the circulating microRNA signatures identified is accurate enough to be used as a diagnostic test in both populations. However, accurate circulating microRNA signatures can be found for each specific population, when taken separately.

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.

Results

A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.

Conclusions

A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04189-2.

The use of micro RNA in the early detection of cervical intraepithelial neoplasia

An important issue in current oncological research is prevention as well as early detection of cancer. This includes also the difficulty to predict the progression of early or pre-cancerous lesions to invasive cancer. In this context, the characterization and categorization of pre-neoplastic lesions of squamous cell carcinoma [cervical intraepithelial neoplasia (CIN)] are an important task with major clinical impact. Screening programs are worldwide established with the aim to detect and eradicate such lesions with the potential to develop untreated into cervical cancer. From the literature it is known that around 5% of CIN 2 and 12% of CIN 3 cases will progress to cancer. The use of molecular markers extracted from cervical mucus might help to identify these high-risk cases and to exclude unnecessary biopsies or surgical treatment. Here we can show that micro RNA (miRNA) analysis from cervical mucus of 49 patients allowed us to distinguish between healthy patients and patients with CIN 3. The miRNA panel used in combination allowed for highly significant testing (P < 0.0001) of CIN 3 status. In parallel, the human papillomavirus status of the patients, the most important factor for the development of cervical cancer, significantly correlated with the miRNA markers hsa-miR-26b-5p, hsa-miR-191-5p and hsa-miR-143-3p, a subpanel of the original six miRNAs. We provide here a proof-of-concept for cervical mucus-based testing for pre-neoplastic stages of cervical squamous cell carcinoma.