Glioblastoma (GBM), the most aggressive form of brain tumor, has a median survival rate of 12-15 months. Understanding the relationship between genetics and tumor location, as well as identifying non-invasive biomarkers, is crucial for improving treatment strategies and survival outcomes in GBM. In this study, we investigated the impact of tumor location on survival outcome of GBM patients along with genetic factors that influence tumor behaviour in different brain regions. Interestingly, we found that patients with parietal lobe tumors had significantly poor survival outcome compared to those with tumors in other brain regions, particularly the frontal lobe. In a comprehensive genomic analysis, we identified genetic factors, seemingly contributing to the poor survival outcomes in parietal lobe patients. We found the enrichment of PTEN loss-of-function mutations in parietal lobe tumors and interestingly these mutations are known to be associated with chemoresistance and poor patient survival. We also found two fusion genes i.e., FGFR3-TACC3 and EGFR-SEPT14, exclusively in parietal lobe tumors, which are known to play crucial roles in tumorigenesis. Differential gene expression analysis revealed the upregulation of genes like PITX2, HOXB13, and DTHD1, which could be responsible for tumor progression in parietal lobe tumors. Conversely, the downregulation of ALOX15 increased relapse risk. Copy number alterations, such as deletions in tumor suppressor gene (LINC00290), were linked to the aggressive nature of parietal lobe tumors. Radiomic analysis revealed two key features, lower LLL\_GLDM\_DependanceEntropy and higher HLL\_firstorder\_Mean, both of which show a significant correlation with increased risk and poorer survival outcome. These findings suggest the potential for targeted therapies and personalized treatments based on tumor location, genetic profile, and radiomic markers. We anticipate that as the size of the datasets will increase for radiogenomics based studies, it will further strengthen these findings and our understanding of molecular drivers for GBM progression, treatment resistance and survival outcome. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Genomics dataset (gbm\_tcga\_pub2013) analysed in this study is available in cBioPortal: www.cbioportal.org and imaging data (TCGA-GBM) is available at The Cancer Image Archive database (TCIA): https://www.cancerimagingarchive.net/. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The Genomics dataset (gbm\_tcga\_pub2013) analysed in this study is available in cBioPortal: www.cbioportal.org and imaging data (TCGA-GBM) is available at The Cancer Image Archive database (TCIA): https://www.cancerimagingarchive.net/.

Acute myeloid leukemia (AML) is one of the leading leukemic malignancies in adults. The heterogeneity of the disease makes the diagnosis and treatment extremely difficult. With the advent of next-generation sequencing (NGS) technologies, exploration at the molecular level for the identification of biomarkers and drug targets has been the focus for the researchers to come up with novel therapies for better prognosis and survival outcomes of AML patients. However, the huge amount of data from NGS platforms requires a comprehensive AML platform to streamline literature mining efforts and save time. To facilitate this, we developed AMLdb, an interactive multi-omics platform that allows users to query, visualize, retrieve, and analyse AML related multi-omics data. AMLdb contains 86 datasets for gene expression profiles, 15 datasets for methylation profiles, CRISPR-Cas9 knockout screens of 26 AML cell lines, sensitivity of 26 AML cell lines to 288 drugs, mutations in 41 unique genes in 23 AML cell lines, and information on 41 experimentally validated biomarkers. In this study, we have reported five genes, i.e. CBFB, ENO1, IMPDH2, SEPHS2, and MYH9 identified via our analysis using AMLdb. ENO1 is uniquely identified gene which requires further investigation as a novel potential target while other reported genes have been previously confirmed as targets through experimental studies. Top of form we believe that these findings utilizing AMLdb can make it an invaluable resource to accelerate the development of effective therapies for AML and assisting the research community in advancing their understanding of AML pathogenesis. AMLdb is freely available at https://project.iith.ac.in/cgntlab/amldb.

Magnetic resonance imaging (MRI) has become an essential and a frontline technique in the detection of brain tumor. However, segmenting tumors manually from scans is laborious and time-consuming. This has led to an increasing trend towards fully automated methods for precise tumor segmentation in MRI scans. Accurate tumor segmentation is crucial for improved diagnosis, treatment, and prognosis. This study benchmarks and evaluates four widely used CNN-based methods for brain tumor segmentation CaPTk, 2DVNet, EnsembleUNets, and ResNet50. Using 1251 multimodal MRI scans from the BraTS2021 dataset, we compared the performance of these methods against a reference dataset of segmented images assisted by radiologists. This comparison was conducted using segmented images directly and further by radiomic features extracted from the segmented images using pyRadiomics. Performance was assessed using the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). EnsembleUNets excelled, achieving a DSC of 0.93 and an HD of 18, outperforming the other methods. Further comparative analysis of radiomic features confirmed EnsembleUNets as the most precise segmentation method, surpassing other methods. EnsembleUNets recorded a Concordance Correlation Coefficient (CCC) of 0.79, a Total Deviation Index (TDI) of 1.14, and a Root Mean Square Error (RMSE) of 0.53, underscoring its superior performance. We also performed validation on an independent dataset of 611 samples (UPENN-GBM), which further supported the accuracy of EnsembleUNets, with a DSC of 0.85 and an HD of 17.5. These findings provide valuable insight into the efficacy of EnsembleUNets, supporting informed decisions for accurate brain tumor segmentation.

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/ .

Recently, we have reported that extracellular vesicles (EVs) from the bone marrow mesenchymal stromal cells (BM-MSC) of aplastic anemia (AA) patients inhibit hematopoietic stem and progenitor cell (HSPC) proliferative and colony-forming ability and promote apoptosis. One mechanism by which AA BM-MSC EVs might contribute to these altered HSPC functions is through microRNAs (miRNAs) encapsulated in EVs. However, little is known about the role of BM-MSC EVs derived miRNAs in regulating HSPC functions in AA. Therefore, we performed miRNA profiling of EVs from BM-MSC of AA (n=6) and normal controls (NC) (n=6), to identify differentially expressed miRNAs carried in AA BM-MSC EVs. DEseq2 analysis identified 34 significantly altered mature miRNAs in AA BM-MSC EVs. Analysis of transcriptome dataset of AA HSPC genes identified that 235 differentially expressed HSPC genes were targeted by these 34 EV miRNAs. The pathway enrichment analysis of 235 HSPC genes revealed their involvement in pathways associated with cell cycle, proliferation, apoptosis, and hematopoiesis regulation, thus highlighting that AA BM-MSC EV miRNAs could potentially contribute to impaired HSPC functions in AA.

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

Recently, we have reported that extracellular vesicles (EVs) from the bone marrow mesenchymal stromal cells (BM-MSC) of aplastic anemia (AA) patients inhibit hematopoietic stem and progenitor cell (HSPC) proliferative and colony-forming ability and promote apoptosis. One mechanism by which AA BM-MSC EVs might contribute to these altered HSPC functions is through microRNAs (miRNAs) encapsulated in EVs. However, little is known about the role of BM-MSC EVs derived miRNAs in regulating HSPC functions in AA. Therefore, we performed miRNA profiling of EVs from BM-MSC of AA (n = 6) and normal controls (NC) (n = 6) to identify differentially expressed miRNAs. The Integrated DEseq2 analysis revealed 34 significantly altered mature miRNAs, targeting 235 differentially expressed HSPC genes in AA. Hub gene analysis revealed 10 HSPC genes such as IGF-1R, IGF2R, PAK1, PTPN1, etc., which are targeted by EV miRNAs and had an enrichment of chemokine, MAPK, NK cell-mediated cytotoxicity, Rap1, PI3k-Akt, mTOR signalling pathways which are associated with hematopoietic homeostasis. We further showed that miR-139-5p and its target, IGF-1R (hub-gene), might regulate HSPC proliferation and apoptosis, which may serve as potential therapeutic targets in AA. Overall, the study highlights that AA BM-MSC EV miRNAs could contribute to impaired HSPC functions in AA.

The process of aging is an intrinsic and inevitable aspect of life that impacts every living organism. As biotechnological advancements continue to shape our understanding of medicine, peptide therapeutics have emerged as a promising strategy for anti-aging interventions. This is primarily due to their favorable attributes, such as low immunogenicity and cost-effective production. Peptide-based treatments have garnered widespread acceptance and interest in aging research, particularly in the context of age-related therapies. To effectively develop anti-aging treatments, a comprehensive understanding of the physicochemical characteristics of anti-aging peptides is essential. Factors such as amino acid composition, instability index, hydrophobic areas and other relevant properties significantly determine their efficacy as potential therapeutic agents. Consequently, the creation of 'AagingBase', a comprehensive database for anti-aging peptides, aims to facilitate research on aging by leveraging the potential of peptide therapies. AagingBase houses experimentally validated 282 anti-aging peptides collected from 54 research articles and 236 patents. Employing state-of-the-art computational techniques, the acquired sequences have undergone rigorous physicochemical calculations. Furthermore, AagingBase presents users with various informative analyses highlighting atomic compositions, secondary structure fractions, tertiary structure, amino acid compositions and frequencies. The database also offers advanced search and filtering options and similarity search, thereby aiding researchers in understanding their biological functions. Hence, the database enables efficient identification and prioritization of potential peptide candidates in geriatric medicine and holds immense potential for advancing geriatric medicine research and innovations. AagingBase can be accessed without any restriction.Database URL: https://project.iith.ac.in/cgntlab/aagingbase/

Alzheimer’s disease (AD) is acknowledged as one of the most common types of dementia. Various brain regions were found to associated with AD pathology. Precuneus and fusiform gyrus are two notable regions whose role has been implicated in cognitive function. However, a thorough investigation was lacking to link these regions with AD pathology. In this study, we conducted a comprehensive radiomic based investigation using magnetic resonance imaging (MRI) scans to link precuneus and fusiform gyrus with AD pathology. We obtained T1 weighted MR scans of AD (n=133), MCI (n=311) and CN (n=195) subjects from ADNI database at three different time points (i.e., 0, 6 and 12 months). Then, we conducted statistical analysis to compare these features among AD, MCI and CN subjects. We found significant decline in gray matter volume (GMV) and cortical thickness of both precuneus and fusiform gyrus in AD as compared to the MCI and CN subjects. Further, we utilized these features to develop machine learning classifiers to classify AD from MCI and CN subjects and achieved accuracy of 97.78% and 94.41% respectively. These results strengthen the connection of precuneus and fusiform gyrus with AD pathology and opens a new avenue of AD research. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the seed research grant from the Indian Institute of Technology Hyderabad. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All the datasets used in this study are available on ADNI database website ().

Although the role of G-quadruplex (G4) DNA structures has been suggested in chromosomal looping this was not tested directly. Here, to test causal function, an array of G4s, or control sequence that does not form G4s, were inserted within chromatin in cells. In vivo G4 formation of the inserted G4 sequence array, and not the control sequence, was confirmed using G4-selective antibody. Compared to the control insert, we observed a remarkable increase in the number of 3D chromatin looping interactions from the inserted G4 array. This was evident within the immediate topologically associated domain (TAD) and throughout the genome. Locally, recruitment of enhancer histone marks and the transcriptional coactivator p300/Acetylated-p300 increased in the G4-array, but not in the control insertion. Resulting promoter-enhancer interactions and gene activation were clear up to 5 Mb away from the insertion site. Together, these show the causal role of G4s in enhancer function and long-range chromatin interactions. Mechanisms of 3D topology are primarily based on DNA-bound architectural proteins that induce/stabilize long-range interactions. Involvement of the underlying intrinsic DNA sequence/structure in 3D looping shown here therefore throws new light on how long-range chromosomal interactions might be induced or maintained.

Background:Circadian rhythms broadly impact human health by regulating our daily physiological and metabolic processes. The circadian clocks substantially regulate our immune responses and susceptibility to infections. Malaria parasites have intrinsic molecular oscillations and coordinate their infection cycle with host rhythms. Considering the cyclical nature of malaria, a clear understanding of the circadian regulations in malaria pathogenesis and host responses is of immense importance. Methods:We have thoroughly investigated the transcript level rhythmic patterns in blood proteins altered in falciparum and vivax malaria and malaria-related immune factors in mice, baboons, and humans by analyzing datasets from published literature and comprehensive databases. Using the Metascape and DAVID platforms, we analyzed Gene Ontology terms and physiological pathways associated with the rhythmic malaria-associated host immune factors. Results:We observed that almost 50% of the malaria-associated host immune factors are rhythmic in mice and humans. Overlapping rhythmic genes identified in mice, baboons, and humans, exhibited enrichment (Q < 0.05, fold-enrichment > 5) of multiple physiological pathways essential for host immune and defense response, including cytokine production, leukocyte activation, cellular defense, and response, regulation of kinase activity, B-cell receptor signaling pathway, and cellular response to cytokine stimulus. Conclusions:Our analysis indicates a robust circadian regulation on multiple interconnected host response pathways and immunological networks in malaria, evident from numerous rhythmic genes involved in those pathways. Host immune rhythms play a vital role in the temporal regulation of host-parasite interactions and defense machinery in malaria.

Peptide informatics is a rapidly growing field that is at the intersection of bioinformatics, chemistry, and biology. Peptides are short chains of amino acids that play important roles in a wide variety of biological processes, such as protein folding, signal transduction, and immune function. Peptide informatics is the use of computational methods to study peptides and their sequence, structure, function, and interactions. Recent advances in peptide informatics have led to a number of new discoveries and applications. For example, new methods have been developed to predict the structure of peptides, which can be used to design new drugs and therapies. New methods for identifying peptide-protein interactions have also been introduced, which can be used to understand the molecular basis of disease.

Impaired skin wound healing is a significant global health issue, especially among the elderly. Wound healing is a well-orchestrated process involving the sequential phases of inflammation, proliferation, and tissue remodeling. Although wound healing is a highly dynamic and energy-requiring process, the role of metabolism remains largely unexplored. By combining transcriptomics and metabolomics of human skin biopsy samples, we mapped the core bioenergetic and metabolic changes in normal acute as well as chronic wounds in elderly subjects. We found upregulation of glycolysis, the tricarboxylic acid cycle, glutaminolysis, and (3-oxidation in the later stages of acute wound healing and in chronic wounds. To ascertain the role of these metabolic pathways on wound healing, we targeted each pathway in a wound healing assay as well as in a human skin explant model using metabolic inhibitors and stimulants. Enhancement or inhibition of glycolysis and, to a lesser extent, glutaminolysis had a far greater impact on wound healing than similar manipulations of oxidative phosphorylation and fatty acid (3-oxidation. These findings increase the understanding of wound metabolism and identify glycolysis and glutaminolysis as potential targets for therapeutic intervention.

Acute myeloid leukemia (AML) is one of the leading leukemic malignancies in adults. The heterogeneity of the disease makes the diagnosis and treatment extremely difficult. Despite the significant developments and the rapid advancements in finding new medication targets, the treatment for AML remains a challenge. With the advent of next-generation sequencing (NGS) technologies, exploration at the molecular level for the identification of biomarkers and drug targets has been the main focus for the researchers to come up with novel therapies for better prognosis and survival outcomes of AML patients. However, the massive amounts of data generated from the NGS platforms demands the necessity to create a comprehensive platform on AML to save the time invested in mining literature. To facilitate this, we developed AMLdb, an interactive multi-omics platform that allows users to query, visualize, retrieve and analyze AML-related multi-omics data. It provides a diverse collection of data resourced from various repositories allowing for a more comprehensive analysis. AMLdb contains 86 datasets for gene expression profiles, 15 datasets for methylation profiles, CRISPR-Cas9 knockout screens of 26 AML cell lines, sensitivity of 26 AML cell lines to 288 drugs, mutations in 41 unique genes in 23 AML cell lines and information on 27 experimentally validated biomarkers. The data provided can be used for deriving conclusions on potential targets for therapies, sensitivity of these targets towards the drugs, patient classification, prediction of treatment strategies and outcomes, chances of relapse etc. In this study, we have reported five genes i.e., CBFB, ENO1, IMPDH2, SEPHS2 and MYH9 identified via our analysis using AMLdb as potential targets. Amongst this, CBFB, IMPDH2, SEPHS2 and MYH9 have been previously validated as targets by experimental studies which is in par with our results. However, ENO1 is a novel target identified using AMLdb which needs further investigation. We anticipates that, AMLdb can be a valuable resource to aid the research community accelerate the development of effective therapies for AML. AMLdb is freely accessible at https://project.iith.ac.in/cgntlab/amldb/ without any restrictions.

Impaired skin wound healing is a significant global health issue, especially among the elderly. Wound healing is a well-orchestrated process involving the sequential phases of inflammation, proliferation, and tissue remodeling. Although wound healing is a highly dynamic and energy-requiring process, the role of metabolism remains largely unexplored. By combining transcriptomics and metabolomics of human skin biopsy samples, we mapped the core bioenergetic and metabolic changes in normal acute as well as chronic wounds in elderly subjects. We found upregulation of glycolysis, the tricarboxylic acid cycle, glutaminolysis, and β-oxidation in the later stages of acute wound healing and in chronic wounds. To ascertain the role of these metabolic pathways on wound healing, we targeted each pathway in a wound healing assay as well as in a human skin explant model using metabolic inhibitors and stimulants. Enhancement or inhibition of glycolysis and, to a lesser extent, glutaminolysis had a far greater impact on wound healing than similar manipulations of oxidative phosphorylation and fatty acid β-oxidation. These findings increase the understanding of wound metabolism and identify glycolysis and glutaminolysis as potential targets for therapeutic intervention.

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

The efficacy of the cancer treatment largely depends on the stage of its detection. Detecting cancer at an early stage can significantly improve cancer treatment. Unfortunately, early-stage cancer detection is limited by various factors and is still a moonshot. Recent advancements in the next generation sequencing technologies led to the identification of biomarkers for early detection and warranted further research in this area. These nine research studies published under this research topic identified potential biomarkers of high prognostic and diagnostic significance and advanced the field of biomarker development.