Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma.

The poor prognosis of glioma patients brought attention to the need for effective therapeutic approaches for precision therapy. Here, we deployed algorithms relying on network medicine and artificial intelligence to design the framework for subtype-specific target identification and drug response prediction in glioma. We identified the driver mutations that were differentially expressed in each subtype of lower-grade glioma and glioblastoma multiforme and were linked to cancer-specific processes. Driver mutations that were differentially expressed were also subjected to subtype-specific disease module identification. The drugs from the drug bank database were retrieved to target these disease modules. However, the efficacy of anticancer drugs depends on the molecular profile of the cancer and varies among cancer patients due to intratumor heterogeneity. Hence, we developed a deep-learning-based drug response prediction framework using the experimental drug screening data. Models for 30 drugs that can target the disease module were developed, where drug response measured by IC50 was considered a response and gene expression and mutation data were considered predictor variables. The model construction consists of three steps: feature selection, data integration, and classification. We observed the consistent performance of the models in training, test, and validation datasets. Drug responses were predicted for particular cell lines derived from distinct subtypes of gliomas. We found that subtypes of gliomas respond differently to the drug, highlighting the importance of subtype-specific drug response prediction. Therefore, the development of personalized therapy by integrating network medicine and a deep learning-based approach can lead to cancer-specific treatment and improved patient care.

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.

BACKGROUND AND OBJECTIVE: The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis. RESULTS: Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data. CONCLUSIONS: The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine.

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing.

BACKGROUND: Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. METHODS: Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease-gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. RESULTS: In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein-protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. CONCLUSION: Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

Antibody-based biosensor to detect oncogenic splicing factor Sam68 for the diagnosis of lung cancer.

OBJECTIVE: The present work aimed to investigate the potential utility of Sam68 protein as a prognostic marker in lung cancer. Then an electrochemical immunosensor is fabricated that is sufficiently sensitive to detect Sam68. RESULTS: Analysis of stage-specific Lung cancer microarray data shows that differential expression of Sam68 is associated with cancer stage and monotonically increases from early tumor stage to advanced metastatic stage. Moreover, the higher expression of Sam68 results in reduced survival of lung cancer patients. Based on these observations, an electrochemical immunosensor was developed for the quantification of Sam68 protein. The target protein was captured by the Anti-Sam68 antibody that was immobilized on the modified Glassy carbon electrode. The stepwise assembly process was characterized by cyclic voltammetry and electrochemical impedance spectroscopy. This fabricated immunosensor displayed good analytical performance in comparison to commercial ELISA kit with good sensitivity, lower detection limit (LOD) of 10.5 pg mL-1, and wide linear detection range from 1 to 5 µg mL-1. This method was validated with satisfactory detection of Sam68 protein in lung adenocarcinoma cell line, NCI-H23. Besides, spike and recovery assay reconfirm that the sensor can precisely quantify Sam68 protein in a complex physiological sample. CONCLUSION: We conclude Sam68 as a valuable prognostic biomarker for early detection of lung cancer. Moreover, we report the first study on the development of an electrochemical immunosensor for the detection of Sam68. The fabricated immunosensor exhibit excellent analytical performance, which can accurately predict the lung cancer patient pathological state.

Small-world networks of prognostic genes associated with lung adenocarcinoma development.

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

Role of the ATP-dependent chromatin remodeling enzyme Fun30/Smarcad1 in the regulation of mRNA splicing.

The yeast ATP-dependent chromatin remodeling enzyme Fun30 has been shown to regulate heterochromatin silencing, DNA repair, transcription, and chromatin organization. Although chromatin structure has been proposed to influence splice site recognition and regulation, whether ATP-dependent chromatin remodeling enzyme plays a role in regulating splicing is not known. In this study, we find that pre-mRNA splicing efficiency is impaired and the recruitment of spliceosome is compromised in Fun30-depleted cells. In addition, Fun30 is enriched in the gene body of individual intron-containing genes. Moreover, we show that pre-mRNA splicing efficiency is dependent on the chromatin remodeling activity of Fun30. The function of Fun30 in splicing is further supported by the observation that, Smarcad1, the mammalian homolog of Fun30, regulates alternative splicing. Taken together, these results provide evidence for a novel role of Fun30 in regulating splicing.

Grade-specific diagnostic and prognostic biomarkers in breast cancer.

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.

A comprehensive study on genome-wide coexpression network of KHDRBS1/Sam68 reveals its cancer and patient-specific association.

Human KHDRBS1/Sam68 is an oncogenic splicing factor involved in signal transduction and pre-mRNA splicing. We explored the molecular mechanism of KHDRBS1 to be a prognostic marker in four different cancers. Within specific cancer, including kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), acute myeloid leukemia (LAML), and ovarian cancer (OV), KHDRBS1 expression is heterogeneous and patient specific. In KIRP and LUAD, higher expression of KHDRBS1 affects the patient survival, but not in LAML and OV. Genome-wide coexpression analysis reveals genes and transcripts which are coexpressed with KHDRBS1 in KIRP and LUAD, form the functional modules which are majorly involved in cancer-specific events. However, in case of LAML and OV, such modules are absent. Irrespective of the higher expression of KHDRBS1, the significant divergence of its biological roles and prognostic value is due to its cancer-specific interaction partners and correlation networks. We conclude that rewiring of KHDRBS1 interactions in cancer is directly associated with patient prognosis.

Disease association of human tumor suppressor genes.

The multifactorial disease, cancer, frequently emerges due to perturbations in tumor suppressor genes (TSGs). However, a growing number of noncanonical target genes of TSGs and the highly interconnected nature of the human interactome reveal that the functions of TSGs are not limited to cancer-specific events. The various functions of TSGs lead to the assumption that cancer is linked with other human disorders. Therefore, a disease-gene association network of TSGs (TSDN) was constructed by integrating protein-protein interaction networks of TSGs (TSN) with Morbid Map in Online Mendelian Inheritance in Man. The TSDN revealed links between TSGs and 22 different human disorders including cancer and indicated disease-disease associations. In addition, high-density functional protein clusters in the TSN showed cohesive and overlapping disease-TSG associations, which proved the prevalent role of TSGs in various human diseases beyond cancer. The presence of overlapping disease-gene modules and disease-disease associations via the TSN demonstrated that other diseases can serve as possible roots of the life-threatening disease cancer. Therefore, a disease association map of TSGs could be a promising tool for exploring intricate relationships between cancer and other diseases for the early prediction of cancer and the understanding of disease etiology.

Fabrication of an immunosensor for quantitative detection of breast cancer biomarker UBE2C.

This work reports the design of a new electrochemical impedimetric immunosensor for the direct determination of ubiquitin-conjugating enzymes 2C (UBE2C), a potential diagnostic biomarker for breast cancer. The immunosensor was fabricated by immobilizing the capture anti-UBE2C antibody onto a polyaniline (PANI) modified glassy carbon electrode (GCE) through glutaraldehyde crosslinking. The assembly process of the immunosensor was examined using scanning electron microscopy, cyclic voltammetry, and electrochemical impedance spectroscopy. The fabricated immunosensor enabled the detection of recombinant human UBE2C in the range of 500 pg mL-1 to 5 µg mL-1. The limit of detection and limit of quantification were found to be 7.907 pg mL-1 and 26.356 pg mL-1, respectively. The diagnostic application of the fabricated immunosensor was explored for the analysis of breast cancer cell line MCF-7 cell extract. The immunosensor demonstrated high selectivity for UBE2C. The fabricated immunosensor also exhibited good reproducibility and storage stability.

Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/ß-catenin signaling network constitutively activate inter-pathway positive feedback loops.

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/ß-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/ß-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

Recent advances in biosensor development for the detection of cancer biomarkers.

Cancer is the second largest disease throughout the world with an increasing mortality rate over the past few years. The patient's survival rate is uncertain due to the limitations of cancer diagnosis and therapy. Early diagnosis of cancer is decisive for its successful treatment. A biomarker-based cancer diagnosis may significantly improve the early diagnosis and subsequent treatment. Biosensors play a crucial role in the detection of biomarkers as they are easy to use, portable, and can do analysis in real time. This review describes various biosensors designed for detecting nucleic acid and protein-based cancer biomarkers for cancer diagnosis. It mainly lays emphasis on different approaches to use electrochemical, optical, and mass-based transduction systems in cancer biomarker detection. It also highlights the analytical performances of various biosensor designs concerning cancer biomarkers in detail.

Alternative splicing within the Wnt signaling pathway: role in cancer development.

BACKGROUND: The Wnt signaling cascade plays a fundamental role in embryonic development, adult tissue regeneration, homeostasis and stem cell maintenance. Abnormal Wnt signaling has been found to be prevalent in various human cancers. Also, a role of Wnt signaling in the regulation of alternative splicing of several cancer-related genes has been established. In addition, accumulating evidence suggests the existence of multiple splice isoforms of Wnt signaling cascade components, including Wnt ligands, receptors, components of the destruction complex and transcription activators/suppressors. The presence of multiple Wnt signaling-related isoforms may affect the functionality of the Wnt pathway, including its deregulation in cancer. As such, specific Wnt pathway isoform components may serve as therapeutic targets or as biomarkers for certain human cancers. Here, we review the role of alternative splicing of Wnt signaling components during the onset and progression of cancer. CONCLUSIONS: Splice isoforms of components of the Wnt signaling pathway play distinct roles in cancer development. Isoforms of the same component may function in a tissue- and/or cancer-specific manner. Splice isoform expression analyses along with deregulated Wnt signaling pathway analyses may be of help to design efficient diagnostic and therapeutic strategies.

Nanomaterials towards fabrication of cholesterol biosensors: Key roles and design approaches.

Importance of cholesterol biosensors is already recognized in the clinical diagnosis of cardiac and brain vascular diseases as discernible from the enormous amount of research in this field. Nevertheless, the practical application of a majority of the fabricated cholesterol biosensors is ordinarily limited by their inadequate performance in terms of one or more analytical parameters including stability, sensitivity and detection limit. Nanoscale materials offer distinctive size tunable electronic, catalytic and optical properties which opened new opportunities for designing highly efficient biosensor devices. Incorporation of nanomaterials in biosensing devices has found to improve the electroactive surface, electronic conductivity and biocompatibility of the electrode surfaces which then improves the analytical performance of the biosensors. Here we have reviewed recent advances in nanomaterial-based cholesterol biosensors. Foremost, the diverse roles of nanomaterials in these sensor systems have been discussed. Later, we have exhaustively explored the strategies used for engineering cholesterol biosensors with nanotubes, nanoparticles and nanocomposites. Finally, this review concludes with future outlook signifying some challenges of these nanoengineered cholesterol sensors.

Human recombinant Cripto-1 increases doubling time and reduces proliferation of HeLa cells independent of pro-proliferation pathways.

Human oncofetal protein Cripto-1 (CR-1) is overexpressed in many types of cancers. CR-1 binds to cell surface Glypican-1 to activate Erk1/2 MAPK and Akt pathways leading to cell proliferation. However, we show that treatment with recombinant CR-1 reduces proliferation of HeLa cells by increasing the doubling time without triggering cell death or cell cycle arrest. Using a comparative study with U-87 MG cells, we show that the pro-proliferative pathway of CR-1 is not effective in HeLa cells due to lower expression of Glypican-1. Further we show that treatment with recombinant CR-1 increases PTEN in HeLa cells leading to downregulation of PI3K/Akt pathway. The anti-proliferative effect gets potentiated when the pro-proliferative pathway is blocked.

Status of Stat3 in an ovalbumin-induced mouse model of asthma: analysis of the role of Socs3 and IL-6.

BACKGROUND: Stat3, Socs3 and cytokines play an integral role in the coordination and persistence of inflammation. However, a clear understanding of the role played by the Stat3/IL-6 and Socs3 pathway in airway inflammation is lacking. We report the alteration in the status of expression and activation of Stat3 by ovalbumin (OVA), and establish its relationship with Socs3 and IL-6 in the lungs of mice with eosinophilic pulmonary inflammation and airway hyperresponsiveness. METHODS: Alterations in the expression of Stat3, Socs3 and IL-6 were determined in a murine model of asthma, where Balb/c mice were sensitized and challenged with OVA (OVA/OVA) and compared with control mice sensitized and challenged with saline (SAL) (SAL/SAL) mice. The OVA/OVA mice were characterized by a moderate increase in methacholine-induced specific airway resistance, the presence of 150 microg/ml of OVA-specific IgG and 8.93 microg/ml OVA-specific IgE antibody and elevated levels of eosinophils and Th2 cytokines (IL-4 and IL-5) in the bronchoalveolar lavage fluid. In contrast SAL/SAL mice had low eosinophils, IL-4 and IL-5 and no OVA-specific IgG and IgE antibodies in the BALF. Stat3 and Socs3 expression profiles were monitored in OVA/OVA and Stat3- and Socs3-silenced OVA/OVA mice. Furthermore, expression of IL-6 in Stat3- and Socs3-silenced mice and the exogenous effect of IL-6 on Stat3 were studied. RESULTS: The results show that expression and activation of Stat3 mRNA and proteins are significantly low in lung of OVA/OVA mice in comparison to SAL/SAL mice following OVA challenge. An increased pool of Socs3 mRNA is observed in OVA/OVA mice with or without OVA challenge and in SAL/SAL mice 24 h after OVA challenge. Transient in vivo blocking of Socs3 gene by Socs3 siRNA restores the expression of IL-6 mRNA and protein in OVA/OVA mice, and nasal administration of recombinant IL-6 to OVA/OVA mice enhanced Stat3 mRNA expression. CONCLUSIONS: Our data suggest that airway inflammation is associated with low expression of Stat3 and IL-6 and overexpression of Socs3 genes in a mouse model of asthma. Furthermore, IL-6 is under the influence of the Socs3 gene and may contribute to the negative regulation of Stat3 via IL-6 following a challenge with an allergen during the development of asthma.