OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma

1-Naphthaleneacetic Acid Improved the In Vitro Cell Culturing by Inhibiting Apoptosis

In vitro cell culturing witnessed its applications in scientific research and industrial activities. Attempts to shorten the doubling time of cultured cells have never ceased. In plants, auxin is applied to promote plant growth, the synthetic derivative 1-Naphthaleneacetic acid (NAA) is a good example. Despite the auxin's naturally occurring receptors are not present in mammalian cells, studies suggested they may affect cell culturing. Yet the effects and mechanisms are still unclear. Here, an up to 2-fold increase in the yield of in vitro cultured human cells is observed. Different types of human cell lines and primary cells are tested and found that NAA is effective in all the cells tested. The PI staining followed by FACS suggested that NAA do not affect the cell cycling. Apoptosis-specific dye staining analysis implicated that NAA rescued cell death. Further bulk RNA sequencing is done and it is identified that the lipid metabolism-engaging and anti-apoptosis gene, ANGPTL4, is enhanced in expression upon NAA treatment. Studies on ANGPTL4 knockout cells indicated that ANGPTL4 is required for NAA-mediated response. Thus, the data identified a beneficial role of NAA in human cell culturing and highlighted its potency in in vitro cell culturing.

Engineering and Characterization of an Artificial Drug-Carrying Vesicles Nanoplatform for Enhanced Specifically Targeted Therapy of Glioblastoma

Glioblastoma multiforme (GBM) treatment is hindered by complex pathologies and the need to cross the blood-brain barrier (BBB) during drug delivery. Although exosomes have great potential for GBM treatment, these alone cannot fully meet the therapeutic requirements, owing to their limitations in targeting and delivery. Herein, engineered artificial vesicles (EAVs), ANG-TRP-PK1@EAVs, which are constructed using a liposome extruder from HEK293T cells expressing ANG-TRP-PK1 peptides, is developed. ANG-TRP-PK1 is a fusion peptide of Angiopep-2 fused to the N-terminus of TRP-PK1, to present Angiopep-2 on the EAVs. ANG-TRP-PK1@EAVs have similar characteristics to the secreted exosomes, but a much higher yield. ANG-TRP-PK1@EAVs have efficient BBB-penetration and GBM-targeting abilities in a mock BBB model in in vitro and orthotopic GBM mouse models in vivo. Doxorubicin loading EAVs (ANG-TRP-PK1@DOX) do not alter the characteristics of the EAVs, which can cross the BBB, reach the GBM, and kill tumor cells in orthotopic GBM mouse models. These engineered drug-loaded artificial vesicles show better therapeutic effects on GBM than temozolomide in mice, with very few side effects. In conclusion, EAVs can be inserted into different targeting ligands and packed into different drugs, and they may serve as unique and efficient nanoplatforms for drug delivery and tumor promise therapy.

Cytogenetics analysis as the central point of genetic testing in acute myeloid leukemia (AML): a laboratory perspective for clinical applications

Chromosomal abnormalities in acute myeloid leukemia (AML) have significantly contributed to scientific understanding of its molecular pathogenesis, which has aided in the development of therapeutic strategies and enhanced management of AML patients. The diagnosis, prognosis and treatment of AML have also rapidly transformed in recent years, improving initial response to treatment, remission rates, risk stratification and overall survival. Hundreds of rare chromosomal abnormalities in AML have been discovered thus far using chromosomal analysis and next-generation sequencing. As a result, the World Health Organization (WHO) has categorized AML into subgroups based on genetic, genomic and molecular characteristics, to complement the existing French-American classification which is solely based on morphology. In this review, we aim to highlight the most clinically relevant chromosomal aberrations in AML together with the technologies employed to detect these aberrations in laboratory settings.

Identification and characterization of putative biomarkers and therapeutic axis in Glioblastoma multiforme microenvironment

Non-cellular secretory components, including chemokines, cytokines, and growth factors in the tumor microenvironment, are often dysregulated, impacting tumorigenesis in Glioblastoma multiforme (GBM) microenvironment, where the prognostic significance of the current treatment remains unsatisfactory. Recent studies have demonstrated the potential of post-translational modifications (PTM) and their respective enzymes, such as acetylation and ubiquitination in GBM etiology through modulating signaling events. However, the relationship between non-cellular secretory components and post-translational modifications will create a research void in GBM therapeutics. Therefore, we aim to bridge the gap between non-cellular secretory components and PTM modifications through machine learning and computational biology approaches. Herein, we highlighted the importance of BMP1, CTSB, LOX, LOXL1, PLOD1, MMP9, SERPINE1, and SERPING1 in GBM etiology. Further, we demonstrated the positive relationship between the E2 conjugating enzymes (Ube2E1, Ube2H, Ube2J2, Ube2C, Ube2J2, and Ube2S), E3 ligases (VHL and GNB2L1) and substrate (HIF1A). Additionally, we reported the novel HAT1-induced acetylation sites of Ube2S (K211) and Ube2H (K8, K52). Structural and functional characterization of Ube2S (8) and Ube2H (1) have identified their association with protein kinases. Lastly, our results found a putative therapeutic axis HAT1-Ube2S(K211)-GNB2L1-HIF1A and potential predictive biomarkers (CTSB, HAT1, Ube2H, VHL, and GNB2L1) that play a critical role in GBM pathogenesis.

GNA13 inhibits glioblastoma metastasis via the ERKs/FOXO3 signaling pathway

Glioblastoma (GBM) is a malignant tumor characterized by poor prognosis and low overall survival (OS) rate. Identification of novel biological markers for the diagnosis and treatment of GBM is crucial to developing interventions to improve patient survival. GNA13, a member of the G12 family, has been reported to play important roles in a variety of biological processes involved in tumorigenesis and development. However, its role in GBM is currently unknown. Here, we explored the expression patterns and functions of GNA13 in GBM, as wells its impact on metastasis process. Results showed that GNA13 was downregulated in GBM tissues and correlated with poor prognosis of GBM. Downregulation of GNA13 promoted the migration, invasion and proliferation of GBM cells; whereas its overexpression abolished these effects. Western blots revealed that GNA13 knockdown and overexpression upregulated and inhibited the phosphorylation of ERKs, respectively. Moreover, GNA13 was the upstream of ERKs signaling to regulating ERKs phosphorylation level. Furthermore, U0126 alleviated the metastasis effect induced by GNA13 knockdown. Bioinformatics analyses and qRT-PCR experiments demonstrated that GNA13 could regulate FOXO3, a downstream signaling molecule of ERKs pathway. Overall, our results demonstrate that GNA13 expression is negatively correlated with GBM and can suppress tumor metastasis by inhibiting the ERKs signaling pathway and upregulating FOXO3 expression.

Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

High APLN Expression Predicts Poor Prognosis for Glioma Patients

Apelin (APLN) is an endogenous ligand of the G protein-coupled receptor APJ (APLNR). APLN/APLNR system was involved in a variety of pathological and physiological functions, such as tumorigenesis and development. However, its prognostic roles in patients with central nervous system (CNS) cancers remain unknown. The present study was designed to explore the expression profile, prognostic significance, and interaction network of APLN/APLNR by integrating data from Oncomine, GEPIA, LOGpc, STRING, GeneMANIA, and immunohistochemical staining. The results demonstrated that APLN and APLNR mRNA expression were significantly increased in CNS cancers, including both low-grade glioma (LGG) and glioblastoma (GBM), when compared with normal CNS tissues. The high APLN, but not APLNR, expression was significantly correlated with overall survival (OS), recurrence free survival (RFS), and progression free survival (PFS) of LGG patients. However, neither APLN nor APLNR expression was significantly related to prognostic value in terms of OS, disease free interval (DFI), disease specific survival (DSS), or progression free interval (PFI) for GBM patients. Additionally, immunohistochemistry staining confirmed the increased APLN expression in tissues of LGG patients with grade II than grade I. These results showed that an elevated APLN level could predict poor OS, RFS, and PFS for LGG patients, and it could be a promising prognostic biomarker for LGG.

LncRNAs in neuropsychiatric disorders and computational insights for their prediction

Long non-coding RNAs (lncRNAs) are 200 nucleotide extended transcripts that do not encode proteins or possess limited coding ability. LncRNAs epigenetically control several biological functions such as gene regulation, transcription, mRNA splicing, protein interaction, and genomic imprinting. Over the years, drastic progress in understanding the role of lncRNAs in diverse biological processes has been made. LncRNAs are reported to show tissue-specific expression patterns suggesting their potential as novel candidate biomarkers for diseases. Among all other non-coding RNAs, lncRNAs are highly expressed within the brain-enriched or brain-specific regions of the neural tissues. They are abundantly expressed in the neocortex and pre-mature frontal regions of the brain. LncRNAs are co-expressed with the protein-coding genes and have a significant role in the evolution of functions of the brain. Any deregulation in the lncRNAs contributes to disruptions in normal brain functions resulting in multiple neurological disorders. Neuropsychiatric disorders such as schizophrenia, bipolar disease, autism spectrum disorders, and anxiety are associated with the abnormal expression and regulation of lncRNAs. This review aims to highlight the understanding of lncRNAs concerning normal brain functions and their deregulation associated with neuropsychiatric disorders. We have also provided a survey on the available computational tools for the prediction of lncRNAs, their protein coding potentials, and sub-cellular locations, along with a section on existing online databases with known lncRNAs, and their interactions with other molecules.

Effects of the m6Am methyltransferase PCIF1 on cell proliferation and survival in gliomas

BACKGROUND

Previous studies have suggested an important role for N6-methyladenosine (m6A) modification in the proliferation of glioma cells. N6, 2'-O-dimethyladenosine (m6Am) is another methylated form affecting the fate and function of most RNA. PCIF1 has recently been identified as the sole m6Am methyltransferase in mammalian mRNA. However, it remains unknown about the role of PCIF1 in the growth and survival of glioma cells.

METHODS

We constructed glioma cell lines that stably downregulated/upregulated PCIF1, established intracranial xenograft models using these cell lines, and employed the following methods for investigations: CCK-8, EdU, colony formation, flow cytometry, qRT-PCR, Western blot, and immunohistochemistry.

FINDINGS

Downregulating PCIF1 promoted glioma cell proliferation, while overexpressing PCIF1 showed the opposite effects. Overexpression of PCIF1 blocked cell cycle progression and induced apoptosis in glioma cells, which was further confirmed by alterations in the expression of cell checkpoint proteins and apoptotic markers. Interestingly, disruption of PCIF1 methyltransferase activity slightly reversed the effect of PCIF1 overexpression on cell proliferation, but had no significant reversal effects on cell cycle progression or apoptosis. Knockdown of PCIF1 promoted the growth of gliomas, while overexpressing PCIF1 inhibited tumor growth and prolonged the survival time of tumor-bearing mice. In addition, the mRNA and protein levels of PCIF1 were gradually decreased with the increase of WHO grade in glioma tissues, but there was no significant correlation with patient survival.

INTERPRETATION

These results indicated that PCIF1 played a suppressing role in glioma growth and survival, which may not entirely depend on its methyltransferase activity.

Iron commensalism of mesenchymal glioblastoma promotes ferroptosis susceptibility upon dopamine treatment

The heterogeneity of glioblastoma multiforme (GBM) leads to poor patient prognosis. Here, we aim to investigate the mechanism through which GBM heterogeneity is coordinated to promote tumor progression. We find that proneural (PN)-GBM stem cells (GSCs) secreted dopamine (DA) and transferrin (TF), inducing the proliferation of mesenchymal (MES)-GSCs and enhancing their susceptibility toward ferroptosis. PN-GSC-derived TF stimulates MES-GSC proliferation in an iron-dependent manner. DA acts in an autocrine on PN-GSC growth in a DA receptor D1-dependent manner, while in a paracrine it induces TF receptor 1 expression in MES-GSCs to assist iron uptake and thus enhance ferroptotic vulnerability. Analysis of public datasets reveals worse prognosis of patients with heterogeneous GBM with high iron uptake than those with other GBM subtypes. Collectively, the findings here provide evidence of commensalism symbiosis that causes MES-GSCs to become iron-addicted, which in turn provides a rationale for targeting ferroptosis to treat resistant MES GBM.

Glioblastoma stem-cell derived mesenchymal cells become reliant on iron but vulnerable to ferroptosis and within patients of heterogeneous glioblastoma multiforme prognosis for those with high iron uptake is poorer than other subtypes.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Sulfonated Amphiphilic Poly(α)glutamate Amine—A Potential siRNA Nanocarrier for the Treatment of Both Chemo-Sensitive and Chemo-Resistant Glioblastoma Tumors

Development of chemo-resistance is a major challenge in glioblastoma (GB) treatment. This phenomenon is often driven by increased activation of genes associated with DNA repair, such as the alkyl-removing enzyme O⁶-methylguanine-DNA methyltransferase (MGMT) in combination with overexpression of canonical genes related to cell proliferation and tumor progression, such as Polo-like kinase 1 (Plk1). Hereby, we attempt to sensitize resistant GB cells using our established amphiphilic poly(α)glutamate (APA): small interfering RNA (siRNA) polyplexes, targeting Plk1. Furthermore, we improved brain-targeting by decorating our nanocarrier with sulfonate groups. Our sulfonated nanocarrier showed superior selectivity towards P-selectin (SELP), a transmembrane glycoprotein overexpressed in GB and angiogenic brain endothelial cells. Self-assembled polyplexes of sulfonated APA and siPlk1 internalized into GB cells and into our unique 3-dimensional (3D) GB spheroids inducing specific gene silencing. Moreover, our RNAi nanotherapy efficiently reduced the cell viability of both chemo-sensitive and chemo-resistant GB cells. Our developed sulfonated amphiphilic poly(α)glutamate nanocarrier has the potential to target siRNA to GB brain tumors. Our findings may strengthen the therapeutic applications of siRNA for chemo-resistant GB tumors, or as a combination therapy for chemo-sensitive GB tumors.

Drug repositioning: Using psychotropic drugs for the treatment of glioma

Psychotropic drugs can penetrate the blood-brain barrier and regulate the levels of neurotransmitters and neuromodulators such as γ-aminobutyric acid, glutamate, serotonin, dopamine, and norepinephrine in the brain, and thus influence neuronal activity. Neuronal activity in the tumor microenvironment can promote the growth and expansion of glioma. There is increasing evidence that in addition to their use in the treatment of mental disorders, antipsychotic, antidepressant, and mood-stabilizing drugs have clinical potential for cancer therapy. These drugs have been shown to inhibit the malignant progression of glioma by targeting signaling pathways related to cell proliferation, apoptosis, or invasion/migration or by increasing the sensitivity of glioma cells to conventional chemotherapy or radiotherapy. In this review, we summarize findings from preclinical and clinical studies investigating the use of antipsychotics, antidepressants, and mood stabilizers in the treatment of various types of cancer, with a focus on glioma; and discuss their presumed antitumor mechanisms. The existing evidence indicates that psychotropic drugs with established pharmacologic and safety profiles can be repurposed as anticancer agents, thus providing new options for the treatment of glioma.

Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene's expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 (p = 0.004). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

Comprehensive analysis of expression, prognosis and immune infiltration for TIMPs in glioblastoma

Background

Tissue inhibitors of metalloproteinase (TIMP) family proteins are peptidases involved in extracellular matrix (ECM) degradation. Various diseases are related to TIMPs, and the primary reason is that TIMPs can indirectly regulate remodelling of the ECM and cell signalling by regulating matrix metalloproteinase (MMP) activity. However, the link between TIMPs and glioblastoma (GBM) is unclear.

Objective

This study aimed to explore the role of TIMP expression and immune infiltration in GBM.

Methods

Oncomine, GEPIA, OSgbm, LinkedOmics, STRING, GeneMANIA, Enrichr, and TIMER were used to conduct differential expression, prognosis, and immune infiltration analyses of TIMPs in GBM.

Results

All members of the TIMP family had significantly higher expression levels in GBM. High TIMP3 expression correlated with better overall survival (OS) and disease-specific survival (DSS) in GBM patients. TIMP4 was associated with a long OS in GBM patients. We found a positive relationship between TIMP3 and TIMP4, identifying gene sets with similar or opposite expression directions to those in GBM patients. TIMPs and associated genes are mainly associated with extracellular matrix organization and involve proteoglycan pathways in cancer. The expression levels of TIMPs in GBM correlate with the infiltration of various immune cells, including CD4+ T cells, macrophages, neutrophils, B cells, CD8⁺ T cells, and dendritic cells.

Conclusions

Our study inspires new ideas for the role of TIMPs in GBM and provides new directions for multiple treatment modalities, including immunotherapy, in GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02477-1.

The endophytome (plant-associated microbiome): methodological approaches, biological aspects, and biotech applications

Similar to other organisms, plants establish interactions with a variety of microorganisms in their natural environment. The plant microbiome occupies the host plant's tissues, either internally or on its surfaces, showing interactions that can assist in its growth, development, and adaptation to face environmental stresses. The advance of metagenomics and metatranscriptomics approaches has strongly driven the study and recognition of plant microbiome impacts. Research in this regard provides comprehensive information about the taxonomic and functional aspects of microbial plant communities, contributing to a better understanding of their dynamics. Evidence of the plant microbiome's functional potential has boosted its exploitation to develop more ecological and sustainable agricultural practices that impact human health. Although microbial inoculants' development and use are promising to revolutionize crop production, interdisciplinary studies are needed to identify new candidates and promote effective practical applications. On the other hand, there are challenges in understanding and analyzing complex data generated within a plant microbiome project's scope. This review presents aspects about the complex structuring and assembly of the microbiome in the host plant's tissues, metagenomics, and metatranscriptomics approaches for its understanding, covering descriptions of recent studies concerning metagenomics to characterize the microbiome of non-model plants under different aspects. Studies involving bio-inoculants, isolated from plant microbial communities, capable of assisting in crops' productivity, are also reviewed.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma

MOTIVATION

Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.

RESULTS

To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at: https://github.com/mskspi/PathCNN.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Hypoxic microenvironment is a hallmark of solid tumors, especially glioblastoma. The strong reliance of glioma-propagating cells (GPCs) on hypoxia-induced survival advantages is potentially exploitable for drug development. Methods: To identify key signaling pathways for hypoxia adaptation by patient-derived GPCs, we performed a kinase inhibitor profiling by screening 188 small molecule inhibitors against 130 different kinases in normoxia and hypoxia. Potential kinase candidates were prioritized for in vitro and in vivo investigations using a ranking algorithm that integrated information from the kinome connectivity network and estimated patients' survival based on expression status. Results: Hypoxic drug screen highlighted extensive modifications of kinomic landscape and a crucial functionality of c-MET-PI3K. c-MET inhibitors diminished phosphorylation of c-MET and PI3K in GPCs subjected to hypoxia, suggesting its role in the hypoxic adaptation of GPCs. Mechanistically, the inhibition of c-MET and PI3K impaired antioxidant defense, leading to oxidative catastrophe and apoptosis. Repurposed c-MET inhibitors PF04217903 and tivantinib exhibited hypoxic-dependent drug synergism with temozolomide, resulting in reduced tumor load and growth of GPC xenografts. Detailed analysis of bulk and single-cell glioblastoma transcriptomes associates the cellular subpopulation over-expressing c-MET with inflamed, hypoxic, metastatic, and stem-like phenotypes. Conclusions: Thus, our "bench to bedside (the use of patient-derived GPCs and xenografts for basic research) and back (validation with independent glioblastoma transcriptome databases)" analysis unravels the novel therapeutic indications of c-MET and PI3K/Akt inhibitors for the treatment of glioblastoma, and potentially other cancers, in the hypoxic tumor microenvironment.

Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis

BACKGROUND

Previous researches have reported that tripartite motif-containing 44 (TRIM44) is related to the prognosis of multiple human tumors. This study was designed to systematically assess the prognostic value of TRIM44 in human malignancies and summarize its possible tumor-related mechanisms.

METHODS

The available databases were searched for eligible studies that evaluated the clinicopathological and prognostic roles of TRIM44 in patients with malignancies. The hazard ratios (HR) and odds ratios (OR) were combined to assess the predictive role of TRIM44 using Stata/SE 14.1 software.

RESULTS

A total of 1740 patients from thirteen original studies were finally included in this study. The results of the combined analysis showed that over-expression of TRIM44 protein was significantly correlated with shorter overall survival (OS) (HR = 1.94, 95% CI: 1.60-2.35) and worse disease-free survival (DFS) (HR = 2.13, 95% CI: 1.24-3.65) in cancer patients. Additionally, the combined ORs indicated that elevated expression level of TRIM44 protein was significantly associated with lymph node metastasis (OR = 2.69, 95% CI: 1.71-4.24), distant metastasis (OR = 10.35, 95% CI: 1.01-106.24), poor tumor differentiation (OR = 1.78, 95% CI: 1.03-3.09), increased depth of tumor invasion (OR = 2.72, 95% CI: 1.73-4.30), advanced clinical stage (OR = 2.75, 95% CI: 2.04-3.71), and recurrence (OR = 2.30, 95% CI: 1.34-3.95). Furthermore, analysis results using Gene Expression Profiling Interactive Analysis (GEPIA) showed that the expression level of TRIM44 mRNA was higher in most tumor tissues than in the corresponding normal tissues, and the relationship between TRIM44 mRNA level and prognosis in various malignant tumors also explored in GEPIA and OS analysis webservers.

CONCLUSIONS

TRIM44 may serve as a valuable prognostic biomarker and a potential therapeutic target for patients with malignancies.