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Cyberbullying is one of the crimes that arise rapidly through the daily use of technology by different types of people and, most notably, by sharing one’s opinions or feelings on social media in a harmful manner. It has several negative effects on society such as depression, anxiety, suicide, and so on. At the same time, it reduces productivity, causes psychological damage that can last a lifetime and increases violence among people. To prevent cyberbullying or take necessary steps against the harasser, the first step is to detect cyberbullying. Several works exist to detect and classify cyberbullying but a few works have been carried out to classify cyberbullying in the Bengali Language. As the number of people is increased day by day who communicate on social media using the Bengali language, it is crucial to address this situation and improve both accuracy and robustness to detect and classify cyberbullying. For this purpose, we propose an NLP-based model using machine learning and deep learning algorithms to detect and classify Bengali comments on social media. This research specifies cyberbullying comments using a multiclass classification strategy. Kaggle and Melany are used to collect the dataset to train and evaluate our model. The dataset contains 56308 Bengali comments, consisting of four distinct categories. The categories are not bully, trolls, sexual, and threats. We use different machine learning algorithms such as Support Vector Machine, Logistic Regression, Random Forest, XGBOOST, Multinomial Naïve Bayes, Deep learning algorithm, Recurrent Neural Network (RNN), and two fusion models. Along with that effective preprocessing steps are implemented to get a suitable dataset. In this study, the Recurrent Neural Network gives the best accuracy, which is 86%. The accuracy of our model is good enough to help social media users and encourage them to practice morality.

Abstract Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

Abstract Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2− breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2− breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2− samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Diabetic retinopathy (DR) is a severe global issue causing blindness if untreated, affecting millions worldwide and worsening over time. Addressing this growing concern necessitates early and accurate DR identification. This study introduces a novel approach to DR detection, combining machine learning algorithms with deep feature extraction techniques. A hybrid model is proposed by stacking predictions from diverse classifiers, such as Decision Trees, Random Forests, Support Vector Machines (SVMs), and more. Three deep learning models – MobileNetV2, DenseNet121, and InceptionResNetV2 – are employed as feature extractors from retinal images. Each classifier undergoes hyperparameter tuning for optimal performance. Using the APTOS 2019 Blindness Detection dataset, including preprocessing techniques like data augmentation and standardization, this hybrid model demonstrates promising accuracy in multi-class (95.50%) and binary classification (98.36%). Notably, DenseNet121 outperforms others. The results suggest the effectiveness of this hybrid technique in early diabetic retinopathy detection, holding significant promise for improved medical intervention.

Abstract Background In the competitive health care environment, patient satisfaction and quality of life (QoL) have become the subject of interest to evaluate the efficacy of therapeutic interventions as we experience improved breast cancer survival in modern times. The knowledge of the long-term effects of surgery on the QoL in breast cancer patients is currently limited in the Asian setting. The purpose of this longitudinal study is to evaluate the QoL of early-stage breast cancer patients undergoing mastectomy and breast-conserving surgery (BCS). Methods In this prospective cohort study, the QoL of 208 patients who underwent mastectomy and the BCS treatment were assessed, using the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire. The questionnaire was administered at the baseline, 6 and 12 months following diagnosis. One-way ANCOVA was used for statistical analysis. Results A total of 208 female survivors of Stage 0–II breast cancer were included, among them 47.1% underwent BCS and 52.9% underwent mastectomy. Older (63.3%), Chinese women (63.6%), and patients with primary education (71.7%) were more likely to undergo mastectomy. At baseline, no significant differences were observed for QoL in both treatment groups. At 6 months, patients who underwent BCS had better social functioning scales( P = 0.006) and worse symptom scales for dyspnoea (P = 0.031), compared to mastectomy patients. One year after diagnosis, the role functioning score of the mastectomy group was significantly higher than the BCS group, specifically among patients who had undergone chemotherapy (P = 0.034). Conclusion Patients who underwent BCS had better social functioning and worse dyspnoea symptoms compared to patients undergoing mastectomy at six months. During one year, there were only significant improvements in the role functioning among the mastectomy groups compared to the BCS groups. After further stratification, only mastectomy patients who received chemotherapy exhibited improved role functioning compared to patients those who did not undergo chemotherapy. Providing social and physical support postoperatively and monitoring patients for cancer worry, or other symptoms in the long-term survivorship period would be important to ensure optimal QoL.

Abstract Background Family history, and genetic and non-genetic risk factors can stratify women according to their individual risk of developing breast cancer. The extent of overlap between these risk predictors is not clear. Methods In this case-only analysis involving 7600 Asian breast cancer patients diagnosed between age 30 and 75 years, we examined identification of high-risk patients based on positive family history, the Gail model 5-year absolute risk [5yAR] above 1.3%, breast cancer predisposition genes (protein-truncating variants [PTV] in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53), and polygenic risk score (PRS) 5yAR above 1.3%. Results Correlation between 5yAR (at age of diagnosis) predicted by PRS and the Gail model was low (r=0.27). Fifty-three percent of breast cancer patients (n=4041) were considered high risk by one or more classification criteria. Positive family history, PTV carriership, PRS, or the Gail model identified 1247 (16%), 385 (5%), 2774 (36%), and 1592 (21%) patients who were considered at high risk, respectively. In a subset of 3227 women aged below 50 years, the four models studied identified 470 (15%), 213 (7%), 769 (24%), and 325 (10%) unique patients who were considered at high risk, respectively. For younger women, PRS and PTVs together identified 745 (59% of 1276) high-risk individuals who were not identified by the Gail model or family history. Conclusions Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.

This study explores the application of molecular communication (MC) for the enhancement of the performance of sequential drug delivery in combination therapy—a multi-drug treatment procedure. To achieve high efficacy, it is essential to maintain a delivery time interval (DTI) between consecutive drug administrations. To this end, this study proposes a coordination scheme that enables the control of the release times of a network of drug-carrying nanomachines to ensure the maintenance of the DTI. Particularly, MC is employed to develop a centralized network, wherein the release times of the drugs from the drug-carrying nanomachines are managed by a controller nanomachine. This nanomachine is named the internal controller nanomachine, as it is placed within the human body. The performance of the proposed scheme is evaluated in terms of the DTI error. Furthermore, the analytical expression of the error is derived and its correctness is validated using simulations.

This paper considers simultaneous drug-delivery (SDD) in molecular communication-based (MC-based) targeted drug-delivery systems. In a realistic scenario, the drug-carrying nanomachines are randomly placed close to the infected site. Due to the random propagation delays in the MC channel, the drugs from multiple drug-carrying nanomachines may, therefore, not arrive simultaneously at the infected site, leading to low efficacy and resulting in drug-delivery-time errors. To overcome this error and to administer the drugs simultaneously at the infected site, we use an internal controller nanomachine to control the release times of the drug-carrying nanomachines, with consideration of the propagation delay, to achieve SDD. In this regard, we propose two SDD schemes, namely, the direct trigger estimate SDD (DTE-SDD) scheme and the indirect trigger estimate SDD (iDTE-SDD) scheme. The difference between these schemes is that in the iDTE-SDD scheme, to estimate the propagation delay, the internal controller nanomachine depends on the drug-carrying nanomachines, while in the DTE-SDD scheme, it does not. Furthermore, to study the errors theoretically, we derive the analytical model of delivery-time error, and this is validated with simulation results. We perform intensive evaluations to understand the system’s behavior under different channel conditions, such as the number of molecules released and the distance. The simulation results highlight the proposed scheme’s energy efficiency and robustness to the large propagation delay, reducing the delivery-time error to improve the accuracy of the SDD.

Regular physical activity (PA) after a breast cancer diagnosis is associated with reduced mortality and better quality of life. In this prospective cohort study, we aimed to explore the trends of PA among breast cancer survivors over three years and identify factors associated with low PA. Interviews on 133 breast cancer patients were conducted at baseline, one and three years after the diagnosis of breast cancer at University Malaya Medical Centre in Kuala Lumpur. Physical activity was measured by using the Global Physical Activity Questionnaire. PA was categorised as active (≥ 600 MET-min/week) and inactive (

BACKGROUND: Breast cancer (BC) is the most common cancer in Malaysia, with many diagnosed at late stages. The “Know Your Lemons” (KYL) visual educational tools were developed by KYL Foundation. This study aimed to evaluate participants' confidence levels and perceived knowledge in identifying BC symptoms before and after exposure to KYL tools. MATERIALS AND METHODS: A cross-sectional study was carried out among 788 participants in three KYL health campaigns from 2017 to 2020. Perceived knowledge (a 5-item Likert scale was used, zero means “very poor” and 4 means “excellent knowledge”) and confidence in identifying BC symptoms were studied. A Wilcoxon Matched-Paired Signed-Rank Test was performed to assess the perceived knowledge. RESULTS: There was a significant improvement in the perceived knowledge Mean (±SD) score (2.84 ± 1.02) versus (4.31 ± 0.66) before and after the campaign (P < 0.01). About 95.6% agreed that the language used in KYL materials was clear and understandable, 89.8% agreed it is acceptable in Malaysian culture, and 80% felt more confident in identifying BC symptoms. Therefore, 90.8% had the intention of breast self-examination and 90.8% would consult a doctor if symptomatic. The majority (92.7%) agreed that the KYL tools clarified the BC tests needed. CONCLUSION: The KYL tools enhanced perceived BC symptom recognition knowledge and confidence levels.

In this paper, we consider drug release-time issue in molecular communication-based targeted drug delivery (MC-based TDD) systems. Typically, a trigger source sends out a signal to multiple nanomachines to indicate them when to release the drugs in a simultaneous manner. However, under a practical setting where the nanomachines are located at unequal distances from the source and the propagation delay of an MC channel is proportional to the third power of distance, the trigger signal may arrive at different times causing the nanomachines to release the drugs in a nonsimultaneous manner. This causes release-time errors. Therefore, we propose a simultaneous drug-release scheme to determine the precise time to emit the trigger signal, taking into account the propagation delay information to minimize the release-time errors. Using both analytical and simulation approach, we demonstrate the effectiveness of the proposed scheme in reducing the release-time errors, highlighting its robustness to large propagation delays. Additionally, we show that the analytical model is in good agreement with the simulation.

Gene methylation is one means of controlling tissue gene expression, but it is unknown what pathways influencing Alzheimer’s disease (AD) are controlled this way. We compared normal and AD brain tissue data for gene expression (mRNAs) and gene methylation profiling. We identified methylated differentially expressed genes (MDEGs). Protein-protein interaction (PPI) of the MDEGs showed 18 hypermethylated low-expressed genes (Hyper-LGs) involved in cell signaling and metabolism; also 10 hypomethylated highly expressed (Hypo-HGs) were involved in regulation of transcription and development. Molecular pathways enriched in Hyper-LGs included neuroactive ligand-receptor interaction pathways. Hypo-HGs were notably enriched in pathways including hippo signaling. PPI analysis also identified both Hyper-LGs and Hypo-HGs, as hub proteins. Our analysis of AD datasets identified Hyper-LGs, Hypo-HGs, and transcription factors linked to these genes. These pathways, which may participate in Alzheimer’s disease development, may be affected by treatments that influence gene methylation patterns.

An attempt has been made to evaluate antimicrobial, antioxidant and cytotoxicity properties of extracts from onion (Allium cepa L.), garlic (Allium sativum), leaves of guava (Psidium guajava), papaya (Carica papaya), tea (Camellia sinensis), baen (Avicennia alba) and keora (Sonneratia apetala), respectively to apply as natural preservatives for tomatoes. The air-dried plant materials of the respective plant species were subjected to ethanol-methanol extraction, concentrated and stored at 4 °C before use. The extracts were dissolved in 95% ethanol for analysis of antioxidant and antimicrobial properties. Of the extracts tested, tea extracts showed the highest zone of inhibition against several pathogenic bacteria (E. coli 35.0±3.2 mm; P. aeruginosa 29.3±2.6 mm; S. typhi 28.4±2.1 mm and S. pyogenes 27.7±3.7 mm) using the disc diffusion method. In regard to DPPH free radical scavenging assay, keora and guava extracts showed the highest percentage of radical scavenging activity with the values of 89.64± 0.18 and 89.39± 0.88, respectively, which were in agreement with higher total antioxidant capacity (TAC) of these extracts obtained by the phosphomolybdenum method. Brine shrimp lethality bioassay for cytotoxicity assessment showed LC50 of 132.54 ± 18.99 µg/mL for the leaf extract of keora which was found to be most toxic among all studied extracts. The initial results indicated that the extracts could be used for food preservative applications based on the antimicrobial, antioxidant and cytotoxicity properties of the tested extracts. However, efficacy, stability and safety issues need to be addressed with both in vitro and in vivo studies.

The training achievement was determined based on difference between the score of pre and post evaluation score. The training was found effective in terms of training achievement. The average pre training score was 71.60% and thus, the training was mainly arranged for unemployed youth that had lack of proper knowledge for self employment and development. During training, the participants faced many problems such as lack of modern laboratory, budget comes after training, lack of permanent library, food and accommodation condition of the hostel and poor training allowance (500 Taka per trainee/month). Besides lack of sufficient credit facilities after training is also one of the problems. The trainees were also suggested for probable solutions of the problems faced during their training. [Fundam Appl Agric 2018; 3(1.000): 347-354]

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that is characterized by the death of neurons controlling voluntary muscles. Early diagnosis of ALS is difficult and detection is limited in sensitivity and specificity as well as by cost. Therefore, detecting ALS from blood cell analysis could improve the early diagnosis and treatment of the disease. The present study aimed to identify blood cell transcripts that reflect brain expression levels of factors linked to ALS progression. We analyzed blood cell and brain transcriptomics gene expression datasets (RNA-seq and microarray) in blood and brain. We identified 13 differentially expressed genes (DEG; ALS versus controls) common to blood cells and brain (DNAH6, HLA-DMB, HLA-A, EHD2, CMKLR1, PROS1, GAPT, CCR1, THBS1, CDK2, RAB27A, ITGB3 and C1orf162) that were commonly dysregulated between ALS blood and brain tissues. These data revealed significant neurodegeneration-associated molecular pathways in the signaling systems. Integration of these different analyses revealed dysregulation of a number of transcription factors, namely SP1, MYC, TP3, CTCF and SRF. In addition, we identified microRNAs altered in ALS: miR-29c, miR-21, let-7a, miR-377, miR-103, miR-369-3p, miR-494, miR-204, miR-29a. Thus, we have identified possible new links between pathological processes in the brain and transcripts in blood cells in ALS subjects that may enable the use of blood samples to diagnose and monitor ALS onset and progression. Keywords: Amyotrophic lateral sclerosis, Molecular biomarkers, Blood-brain common gene, Differentially expressed genes, Protein-protein interaction, Transcription factors, microRNAs

Endometrial cancer (EC) arises from the lining of the uterus, and is a common female genital tract malignancy of post-menopausal women. Breast cancer (BC) is one of the risk factors for EC, but the molecular pathways and core genes shared by these two diseases have not yet been elucidated. In this study, we sought to identify the common molecular pathways and prognostic hub proteins in EC and BC that can be used for the prediction of the progression. We employed the statistical method Limma to perform differential analysis of transcriptomes of EC and BC downloaded from the Gene Expression Omnibus. The gene functional annotations were performed through gene ontology (GO) and pathway enrichment analysis. Hub proteins were identified from the protein-protein interactions (PPI) network analysis using the STRING database, and survival analysis was done on the hub proteins to assess the prognostic values using SurvExpress. We analyzed the EC and BC transcriptomics datasets individually, and 57 common differentially expressed genes were identified, shared by both EC and BC. GO and pathway analysis showed that 57 commonly dysregulated genes were involved in several altered molecular pathways, including protein digestion and absorption, cysteine and methionine metabolism, ECM-receptor interaction, and drug metabolism. We have detected key hub proteins (CDC20, EZH2, TOP2A, SPTBN1) based on a topological analysis of the PPI network which play vital roles in the progression and regulation of EC and BC. The survival analysis of the hub genes demonstrated that they were significantly associated with worse survival outcomes in EC and BC. In the present study, we have identified novel pathways shared by EC and BC at the molecular level, and also identified possible gene expression links between EC and BC. Keywords: Endometrial cancer, Breast cancer, Differentially expressed genes, Molecular pathways, Protein–protein interaction, Survival analysis

Aim: Diffuse low-grade glioma (LGG) patients have a tendency to develop glioblastoma multiforme within 5–10 years, with only 15 months life expectancy. The present study aimed to decode a diagnostic and prognostic gene signature in LGG and identify repositioned potential candidate biomarker therapeutics. Methods: We have utilized a publicly available transcriptomics dataset to identify differentially expressed genes (DEGs) using Limma. The functional annotation of the DEGs was performed to identify Gene Ontology and pathways. The protein-protein interaction (PPI) network of the (DEGs) encoding proteins was analyzed utilizing STRING via NetworkAnalyst. The ROC analysis and survival analysis of the hub gene was also performed in R and SurvExpress-a biomarker validation tool. We have utilized the L1000CDS2 to identify candidate small molecules or drugs to target the diagnostic and prognostic biomarkers. Results: We have identified 238 genes as DEGs in LGG compared to non-LGG samples involved in the biological process of signaling and metabolic systems. We then identified molecular pathways enriched by the DEGs including the VEGF signaling pathway, Fc epsilon RI signaling pathway, and p38 signaling mediated by MAPKAP kinases. We identified hub proteins (MAPK11, MRPL1, RAF1, CDC20, NOP2, TUBB4B, CTNND1, PWP2, FGR, BMP2) from PPI analysis. The ROC and survival analysis of the hub genes revealed that these biomarkers can be utilized as diagnostic and prognostic biomarker signatures in LGG. Several novel candidate drugs were repositioned; among them penfluridol, perheiline maleate is used for purposes other than cancer. Other repositioned drugs: AG14361, NCGC00185094-01, BRD-K61717269, C8273, BRD-A28970875, KIN001-127, BYL719, JW-7-24-1, HG-6-64-01, radicicol, MK-2206, and WZ-4-14 have not been described in the literature for treatment purposes. Conclusions: Our analysis of LGG datasets identified 10 hub genes as diagnostic and prognostic signatures, and the candidate repositioned drugs linked to these biomarkers. These biomolecules and repositioned drugs may be utilized as biomarkers for diagnosis and prognosis, and for development of a treatment strategy for LGG. Keywords: Low grade glioma, Diagnostic gene signature, Prognostic gene signature, Protein-protein interaction, Drug repositioning, Molecular biomarkers

Multiple sclerosis (MS) is a severely disabling disease affecting the brain and spinal cord. The detection of MS at an early stage is difficult. However, detection of MS from blood cell gene expression may ameliorate the early identification of MS. The present study addressed overlapped genes between blood cell and brain tissues of MS patients. We analyzed microarray gene expression brain tissue datasets and eQTL data to identify overlapped genes in the blood and brain of MS patients. We identified 23 overlapped differentially expressed genes (DEGs) (PWP2, UPK3BL, NFATC3, NPIPA5, KNOP1, PIK3C2B, LDLRAP1, HN1L, FAM226B, STAG3L2, ZNF814, BMS1P5, SDCCAG8, CLN8, ARHGEF7, NEAT1, ANKRD42, C5orf34, DOK6, PKN2, SP1, DBF4B, VAMP3) in the blood cells and brain tissues of MS patients. The axon guidance mediated and postsynaptic differentiation associated pathways were enriched by the DEGs. The significant regulatory transcription factors (TFs) including SP1, ARNT, MEF2A, YY1, EGR1, MIF, FOS, NFYA, TFAP2A, MYC, USF, MXI1, JUN, MAX, E2F1, POU3F2, GABPA, TFAP2C, CUX1, 2623, SPI1 and microRNAs (miRNAs) including miR-650, miR-223, miR-9, miR-181b, miR-190, miR-561, miR-520c-3p, miR-658, miR-199b-5p, miR-660, miR-20 were identified in MS. The candidate drugs were determined from drug target overrepresentation analysis. This study provides biomarkers at protein levels (hub proteins and TFs) and RNA levels (mRNAs, miRNAs) in MS subjects that were similarly dysregulated in both blood and brain tissues. Keywords: Multiple sclerosis, Molecular biomarkers, Blood-brain common gene, Differentially expressed genes, Protein-protein interaction, cis-eQTL, Transcription factors, microRNAs

Depressive illness is a significant risk factor for ovarian cancer development (OC). The underlying mechanism is unclear (perhaps involving altered neuroendocrine factors), but identifying associated alterations in gene expression of OC in depression sufferers may uncover novel factors that affect OC progression. We thus analyzed microarray gene expression data from OC tissue taken from patients diagnosed with and without depression. We identified 34 differentially expressed genes (DEGs) of depression from OC patients. Gene ontology (GO) and KEGG pathway analyses indicated several molecular pathways including complement and coagulation cascades, the hippo signaling pathway, ether lipid metabolism, the MAPK signaling pathway, and antigen processing and presentation were overrepresented among DEGs. Subsequent, protein-protein interaction (PPI) analysis revealed pathway hub proteins (FOS, EGR1, JUNB, HSPA1B, FGFR3, TRIB1, CTSB, SERPINE1), regulatory transcription factors (TFs; FOS, EGR1, JUNB) and one miRNA, miR-101-5p from TFs-miRNAs coregulatory networks. The prognostic survival analysis of the DEGs revealed CXCL12, ARL4C, NQO2 associated with worse OC survival outcomes. Protein-metabolite interaction network analysis showed that upregulated protein CHPT1 (a choline phosphotransferase) interacts with four important phospholipid synthesis and signaling metabolites. Protein-drug interaction analysis revealed SERPINE1 and PLAT proteins interact with compounds that include thrombolytic drugs, plasmin inhibitors, thiazolidinediones, NSAIDs, and hypertension treatments. Thus, we identified evidence for factors and pathways altered in OC tissue when patient depression is evident, and characterized important aspects of these OC-depression links that may be instrumental in developing new treatments for OC patients. Keywords: Ovarian cancer, Depression, Drug targets, Biomarker signatures, Differentially expressed genes, Protein-protein interaction, Protein-metabolite interaction, Protein-drug interactions, Survival analysis

Background. The use of complementary and alternative medicine (CAM) has increased and little is known on CAM use during the initial period. Therefore, the aim was to determine prevalence of CAM use among newly diagnosed breast cancer patients prior to seeking conventional treatment. Methods. This is a cross-sectional study involved interviewing newly diagnosed breast cancer patients in the University Malaya Medical Centre (UMMC) using a structured questionnaire. Eligible respondents were interviewedduring a routine clinical visit. Results. A total of 400 patients were interviewed, of whom 139 (34.8%) were CAM users. Dietary supplementation (n = 107, 77.0%) was the most frequently used type of CAM, followed by spiritual healing (n = 40, 28.8%) and traditional Chinese medicine (n = 32, 23.0%). Malay ethnic group (n = 61, 43.9%) was the largest group of CAM users, followed by Chinese (n = 57, 41.0%) and Indian (n = 20, 14.4%). Majority of these CAM users (n = 87, 73.1%) did not disclose the use of CAM to their doctors. Most of them used remedies based on the recommendation of family and friends. Malay ethnicity and patients with 3 or more comorbidities were more likely to use CAM. Conclusion. There is substantial use of CAM among breast cancer patients in UMMC prior to seeking hospital treatment, and the most popular CAM modality is dietary supplements. Since, the majority of CAM users do not disclose the use of CAM to their physicians, therefore health care providers should ensure that those patients who are likely to use CAM are appropriately counseled and advised.

BackgroundBreast cancer risk factors have been examined extensively in Western setting and more developed Asian cities/countries. However, there are limited data on developing Asian countries. The purpose of this study was to examine breast cancer risk factors and the change of selected risk factors across birth cohorts in Malaysian women.MethodsAn unmatched hospital based case-control study was conducted from October 2002 to December 2016 in Selangor, Malaysia. A total of 3,683 cases and 3,980 controls were included in this study. Unconditional logistic regressions, adjusted for potential confounding factors, were conducted. The breast cancer risk factors were compared across four birth cohorts by ethnicity.ResultsEver breastfed, longer breastfeeding duration, a higher soymilk and soy product intake, and a higher level of physical activity were associated with lower risk of breast cancer. Chinese had the lowest breastfeeding rate, shortest breastfeeding duration, lowest parity and highest age of first full term pregnancy.ConclusionsOur study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer. With the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their breast cancer risk.

Background and objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein−protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein−drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein–protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan–Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.