Nanomedicine (Lond). 2025 Feb 7:1-17. doi: 10.1080/17435889.2025.2461985. Online ahead of print.

ABSTRACT

AIM: Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most formidable cancers, largely due to its distinct microenvironment characterized predominantly by extensive desmoplastic stroma. In this study, we synthesized three novel water-soluble fullerene-based nanomaterials targeting EGFR protein.

METHODS: The direct amination of fullerene carbon atoms, was followed by conjugation with a modified derivative of the EGFR inhibitor-erlotinib, resulting in the formation of novel water-soluble fullerene derivatives.

RESULTS: Further investigation into PAN02 and AsPC-1 cell lines revealed that these fullerene nanomaterials could induce cell cycle arrest in the G0/G1 phase, corroborated by alterations in the expression levels of the p27 and cyclin E1 proteins. Additionally, mechanisms of cell death were identified as autophagy for C60BUT and C70BUT-ERL, and apoptosis for Gd@C82EDA-ERL nanomaterials.

CONCLUSIONS: Crucially, the study uncovered the efficacy of synthesized aminofullerenes in inhibiting the EGFR signaling pathway. The further toxicological studies of Gd@C82EDA-ERL fullerene on Drosophila melanogaster, underscored its potential for theranostic applications.

PMID:39916650 | DOI:10.1080/17435889.2025.2461985

Sci Rep. 2025 Feb 6;15(1):4480. doi: 10.1038/s41598-025-88325-5.

ABSTRACT

Circadian rhythms, intrinsic 24-h cycles regulating physiological processes, are crucial for skin homeostasis. Disruptions in these rhythms are linked to various skin disorders and impaired barrier function. Circadian rhythms can be modulated by botanical compounds, which hold therapeutic potential. However, the effect of cinnamomi cortex (CC), an anti-inflammatory, antioxidant, and antimicrobial agent, on the circadian rhythm of keratinocytes remains unclear. This study aimed to examine the effects of CC extract and its 18 individual components on the circadian rhythm of HaCaT, an immortalized human keratinocyte line. CC extract and its bioactive components epicatechin (EC) and linalool (LO) significantly enhanced the circadian amplitude without altering the period. Gene expression analysis revealed that CC extract, EC, and LO altered the mRNA and protein levels of clock genes in a time-dependent manner. During molecular docking simulations, both EC and LO exhibited strong binding affinities for RORA, a key nuclear receptor involved in circadian regulation. Enhanced BMAL1 promoter activity following EC and LO treatments corroborated these findings. Furthermore, EC and LO demonstrated significant antioxidant activities, as evidenced by reduced reactive oxygen species levels and increased expression of antioxidant enzymes. EC and LO also upregulated skin barrier-related and ceramide synthesis genes and modulated the expression of cellular longevity-promoting genes. In conclusion, CC extract, particularly the components EC and LO, modulated circadian rhythms, reduced oxidative stress, and enhanced skin barrier function in keratinocytes. These findings highlight the potential of CC extract and its components as novel dermatological treatments to improve skin health and combat aging.

PMID:39915616 | DOI:10.1038/s41598-025-88325-5

Cancer Gene Ther. 2025 Feb 6. doi: 10.1038/s41417-025-00874-z. Online ahead of print.

ABSTRACT

PARP-inhibitors (PARPi) are an integral part of ovarian cancer treatment. However, overcoming acquired PARPi resistance or increasing the benefit of PARPi in patients without homologous recombination deficiency (HRD) remains an unmet clinical need. We sought to identify genetic modulators of PARPi response, guiding pharmacological PARPi sensitization. CRISPR-Cas9 mediated loss-of-function screen with a focused sgRNA library revealed that DNA-demethylases JMJD1B/JMJD1C, targetable by the small inhibitor methylstat, promote PARPi resistance. Methylstat synergistically interacted with olaparib, and (re-)sensitized ovarian cancer cells to PARPi treatment, surpassing the efficacy of common demethylase inhibitors. Genetic knockout of JMJD1B and/or JMJD1C phenocopied the effect of methylstat in an additive manner. Validation studies revealed methylstat to be a universal PARPi-sensitizing drug, effective, regardless of PARPi resistance status or BRCA1 mutational background. Methylstat modulated clonal cancer dynamics by mitigating positive selection of PARPi-resistant or BRCA1-proficient cells under olaparib treatment. Using a model of PARPi-induced cellular toxicity, we showed that methylstat impairs cellular DNA repair, indicated by an increased susceptibility of ovarian cancer cells to olaparib-induced DNA double strand breaks after methylstat exposure. This study proposes the histone demethylase inhibitor methylstat as an epigenetic drug for overcoming PARPi-resistance or for increasing efficacy of PARPi beyond HRD in ovarian cancer patients.

PMID:39915607 | DOI:10.1038/s41417-025-00874-z

Sci Rep. 2025 Feb 6;15(1):4464. doi: 10.1038/s41598-025-86893-0.

ABSTRACT

Second harmonic generation (SHG) microscopy is a powerful tool for imaging collagen and other noncentrosymmetric fibrillar structures in biological tissue. Polarimetric SHG measurements provide ultrastructural information about the fibrillar organization in a focal volume (voxel). We present a reduced nonlinear polarimetry method named double Stokes polarimetry (DSP) for quick characterization of chiral C 6 symmetry fibers without data fitting that simplifies and speeds up the polarimetric analysis. The method is based on double Stokes-Mueller polarimetry and uses linear and circular incident and outgoing polarization states. The analytical expressions of DSP polarimetric parameters are defined in terms of conventional SHG Stokes vector components. A complex chiral susceptibility (CCS) model is assumed to derive expressions of ultrastructural parameters consisting of the magnitude and phase of molecular complex-valued chiral susceptibility ratio, real-valued achiral ratio, and fiber orientation in a voxel. The ultrastructural parameters are expressed in terms of directly measurable DSP polarimetric parameters. DSP is validated with rat tail tendons sectioned at different orientations. DSP can be applied to investigate the origin of chiral complex-valued susceptibility of collagen, to study modifications of collagen in cancerous tissue, and to map ultrastructural parameters of large areas for whole-slide histopathology.

PMID:39915558 | DOI:10.1038/s41598-025-86893-0

Nat Commun. 2025 Feb 6;16(1):1402. doi: 10.1038/s41467-025-56659-3.

ABSTRACT

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8 T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

PMID:39915487 | DOI:10.1038/s41467-025-56659-3

Mol Cell Proteomics. 2025 Feb 4:100914. doi: 10.1016/j.mcpro.2025.100914. Online ahead of print.

ABSTRACT

Noncanonical micropeptides or called novel microproteins, i.e., polypeptides mostly under 10 kDa, are encoded by genomic sequences that have been previously annotated as noncoding but now known as small open reading frames (sORFs). The recent identification of microproteins encoded by sORFs has provided evidence that many sORFs encode functional microproteins that play crucial roles in various biological processes. T cell activation is a critical biological process for adaptive immune response. Understanding key players in this process will allow us to decipher the complex mechanisms as well as develop immunotherapy for treating a wide range of diseases. Although there have been extensive studies on canonical proteins in T cell activation, the novel microproteins in T cells and their roles have been uncharted water to date. Nascent proteins are defined as newly synthesized polypeptides emerged during the translation of mRNA. In this study, we combined nascent proteomics and quantitative proteomics to identify 411 novel microproteins in primary human T cells, including 83 nascent microproteins. We activated the T cell function with either PMA/Ionomycin (distal activation) or CD3/CD28 activating antibodies (proximal activation), and obtained a comprehensive canonical protein and microprotein profiles to pinpoint common and distinct differentially expressed proteins under these two activation conditions. After experimental testing, three microproteins numbered T1, T2 and T3 were found to be functional in regulating T cell activation. Bioinformatic and proteomic analyses suggested that T1 was functional related to immune as negative feedback to T cell activation. Our study not only established an integrated approach to uncover and elucidate novel microproteins but also highlight the significant role of microproteins in regulating T cell activation.

PMID:39914663 | DOI:10.1016/j.mcpro.2025.100914

Cell Genom. 2025 Jan 29:100768. doi: 10.1016/j.xgen.2025.100768. Online ahead of print.

ABSTRACT

Emerging research underscores the gut microbiome's impact on the nervous system via the microbiota-gut-brain axis, yet comprehensive insights remain limited. Using a CHD8-haploinsufficient model for autism spectrum disorder (ASD), we explored host-gut microbiota interactions by constructing a single-cell transcriptome atlas of brain and intestinal tissues in wild-type and mutant mice across three developmental stages. CHD8 haploinsufficiency caused delayed development of radial glial precursors and excitatory neural progenitors in the E14.5 brain, inflammation in the adult brain, immunodeficiency, and abnormal intestinal development. Selective CHD8 knockdown in intestinal epithelial cells generated Chd8ΔIEC mice, which exhibited normal sociability but impaired social novelty recognition. Probiotic intervention with Lactobacillus murinus selectively rescued social deficits in Chd8ΔIEC mice, with single-cell transcriptome analysis revealing underlying mechanisms. This study provides a detailed single-cell transcriptomic dataset of ASD-related neural and intestinal changes, advancing our understanding of the gut-brain axis and offering potential therapeutic strategies for ASD.

PMID:39914389 | DOI:10.1016/j.xgen.2025.100768

Cell Genom. 2025 Jan 28:100765. doi: 10.1016/j.xgen.2025.100765. Online ahead of print.

ABSTRACT

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

PMID:39914387 | DOI:10.1016/j.xgen.2025.100765

Science. 2025 Feb 6:eadq2634. doi: 10.1126/science.adq2634. Online ahead of print.

ABSTRACT

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

PMID:39913643 | DOI:10.1126/science.adq2634

PLoS One. 2025 Feb 6;20(2):e0314982. doi: 10.1371/journal.pone.0314982. eCollection 2025.

ABSTRACT

Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool.

PMID:39913463 | DOI:10.1371/journal.pone.0314982

STAR Protoc. 2025 Feb 4;6(1):103625. doi: 10.1016/j.xpro.2025.103625. Online ahead of print.

ABSTRACT

Spatially resolved transcriptomics (SRT) data contain intricate noise due to the diffusion of transcripts caused by tissue fixation, permeabilization, and cell lysis during the experiment. Here, we present a protocol for denoising SRT data using SpotGF, an optimal transport-based gene filtering algorithm, without modifying the raw gene expression. We describe steps for data preparation, SpotGF score calculation, filtering threshold determination, denoised data generation, and visualization. Our protocol enhances SRT quality and improves the performance of downstream analyses. For complete details on the use and execution of this protocol, please refer to Du et al.1.

PMID:39913289 | DOI:10.1016/j.xpro.2025.103625

Bull Math Biol. 2025 Feb 6;87(3):39. doi: 10.1007/s11538-025-01415-3.

ABSTRACT

The concept of identifiability describes the possibility of inferring the parameters of a dynamic model by observing its output. It is common and useful to distinguish between structural and practical identifiability. The former property is fully determined by the model equations, while the latter is also influenced by the characteristics of the available experimental data. Structural identifiability can be determined by means of symbolic computations, which may be performed before collecting experimental data, and are hence sometimes called a priori analyses. Practical identifiability is typically assessed numerically, with methods that require simulations-and often also optimization-and are applied a posteriori. An approach to study structural local identifiability is to consider it as a particular case of observability, which is the possibility of inferring the internal state of a system from its output. Thus, both properties can be analysed jointly, by building a generalized observability matrix and computing its rank. The aim of this paper is to investigate to which extent such observability-based methods can also inform about practical aspects related with the experimental setup, which are usually not approached in this way. To this end, we explore a number of possible extensions of the rank tests, and discuss the purposes for which they can be informative as well as others for which they cannot.

PMID:39913007 | DOI:10.1007/s11538-025-01415-3

Mar Biotechnol (NY). 2025 Feb 6;27(1):43. doi: 10.1007/s10126-025-10415-2.

ABSTRACT

Aurantiochytrium not only excels in producing long-chain polyunsaturated fatty acids such as docosahexaenoic acid for humans, but it is also a source of essential fatty acids with minimal impacts on wild fisheries and is vital in the transfer of atmospheric carbon to oceanic carbon sinks and cycles. This study aims to unveil the systems biology of lipid production in the Australian Aurantiochytrium sp. TC20 by comparing the transcriptomic profiles under optimal growth conditions with increased fatty acid production from the early (Day 1) to late exponential growth phase (Day 3). Particular attention was paid to 227 manually annotated genes involved in lipid metabolism, such as FAS (fatty acid synthetase) and subunits of polyunsaturated fatty acids (PUFA) synthase. PCA analysis showed that differentially expressed genes, related to lipid metabolism, efficiently discriminated Day 3 samples from Day 1, highlighting the key robustness of the developed lipid-biosynthesis signature. Highly significant (pFDR < 0.01) upregulation of polyunsaturated fatty acid synthase subunit B (PFAB) involved in fatty acid synthesis, lipid droplet protein (TLDP) involved in TAG-synthesis, and phosphoglycerate mutase (PGAM-2) involved in glycolysis and gluconeogenesis were observed. KEGG enrichment analysis highlighted significant enrichment of the biosynthesis of unsaturated fatty acids (pFDR < 0.01) and carbon metabolism pathways (pFDR < 0.01). This study provides a comprehensive overview of the transcriptional landscape of Australian Aurantiochytrium sp. TC20 in the process of fatty acid production.

PMID:39912956 | DOI:10.1007/s10126-025-10415-2

ACS Infect Dis. 2025 Feb 6. doi: 10.1021/acsinfecdis.4c00912. Online ahead of print.

ABSTRACT

Tetracyclines (TCs) are an important class of antibiotics threatened by enzymatic inactivation. These tetracycline-inactivating enzymes, also known as tetracycline destructases (TDases), are a subfamily of class A flavin monooxygenases (FMOs) that catalyze hydroxyl group transfer and oxygen insertion (Baeyer-Villiger type) reactions on TC substrate scaffolds. Semisynthetic modification of TCs (e.g., tigecycline, omadacycline, eravacycline, and sarecycline) has proven effective in evading certain resistance mechanisms, such as ribosomal protection and efflux, but does not protect against TDase-mediated resistance. Here, we report the design, synthesis, and evaluation of a new series of 22 semisynthetic TDase inhibitors that explore D-ring substitution of anhydrotetracycline (aTC) including 14 C10-benzoate ester and eight C9-benzamides. Overall, the C10-benzoate esters displayed enhanced bioactivity and water solubility compared to the corresponding C9-benzamides featuring the same heterocyclic aryl side chains. The C10-benzoate ester derivatives of aTC were prepared in a high-yield one-step synthesis without the need for protecting groups. The C10-esters are water-soluble, stable toward hydrolysis, and display dose-dependent rescue of tetracycline antibiotic activity in E. coli expressing two types of tetracycline destructases, represented by TetX7 (Type 1) and Tet50 (Type 2). The best inhibitors recovered tetracycline antibiotic activity at concentrations as low as 2 μM, producing synergistic scores <0.5 in the fractional inhibitory concentration index (FICI) against TDase-expressing strains of E. coli and clinical P. aeruginosa. The C10-benzoate ester derivatives of aTC reported here are promising new leads for the development of tetracycline drug combination therapies to overcome TDase-mediated antibiotic resistance.

PMID:39912785 | DOI:10.1021/acsinfecdis.4c00912

Stat Med. 2025 Feb 28;44(5):e10334. doi: 10.1002/sim.10334.

ABSTRACT

High-throughput single-cell RNA-seq (scRNA-seq) data contains an excess of zero values, which can be contributed by unexpressed genes and detection signal dropouts. Existing imputation methods fail to distinguish between these two types of zeros. In this study, we introduce a statistical framework that effectively differentiates true zeros (lack of expression) from false zeros (dropouts). By focusing only on imputing the dropout zeros, we developed a new imputation tool, scRecover. Our approach utilizes a zero-inflated negative binomial framework to model the gene expression of each gene in each cell, enabling the estimation of zero-dropout probability. Additionally, we employ a modified version of the Good and Toulmin model to identify true zeros for each gene. To achieve imputation, scRecover is combined with other imputation methods such as scImpute, SAVER and MAGIC. Down-sampling experiments show that it recovers dropout zeros with higher accuracy and avoids over-imputing true zero values. Experiments conducted on real world data highlight the ability of scRecover to enhance downstream analysis and visualization.

PMID:39912305 | DOI:10.1002/sim.10334

Chem Commun (Camb). 2025 Feb 6. doi: 10.1039/d4cc03807j. Online ahead of print.

ABSTRACT

Bright near-infrared fluorophores are in demand for microscopy. We showcase a deuterated oxazine being 23% brighter vs. ATTO700. With a longer lifetime of 1.85 nanoseconds, we find the best-in-class SulfoOxazine700-d10 to stain mitochondria for confocal microscopy, and demonstrate unaffected diffusion properties in single molecule fluorescence correlation spectroscopy.

PMID:39912228 | DOI:10.1039/d4cc03807j

Biotechnol Notes. 2025 Jan 13;6:67-78. doi: 10.1016/j.biotno.2025.01.001. eCollection 2025.

ABSTRACT

Microbiome engineering has emerged as a transformative approach to enhancing food safety and quality by strategically modulating microbial communities. This review critically examines state-of-the-art techniques, including synthetic biology, artificial intelligence (AI), and systems biology, that are revolutionizing our ability to improve nutritional profiles, extend shelf life, and optimize food production processes. The review further explores complex social, ethical, and regulatory considerations, emphasizing the importance of robust public engagement and the establishment of standardized frameworks to ensure safe and effective implementation. While microbiome engineering holds significant promise for revolutionizing food safety and quality control, further research is needed to address critical challenges, including understanding microbial dynamics in complex food systems and developing harmonized regulatory frameworks. By bridging interdisciplinary gaps, this paper underscores the necessity of collaborative efforts to unlock the full potential of microbiome-driven innovations for a more resilient and sustainable food industry.

PMID:39912062 | PMC:PMC11795101 | DOI:10.1016/j.biotno.2025.01.001

Front Pharmacol. 2025 Jan 22;15:1470931. doi: 10.3389/fphar.2024.1470931. eCollection 2024.

ABSTRACT

INTRODUCTION: Most drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.

METHODS: We introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.

RESULTS: We were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.

DISCUSSION: This work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.

PMID:39911831 | PMC:PMC11794328 | DOI:10.3389/fphar.2024.1470931

Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.

ABSTRACT

BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945

Pathog Immun. 2025 Jan 29;10(1):120-139. doi: 10.20411/pai.v10i1.770. eCollection 2024.

ABSTRACT

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.

OBJECTIVE: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.

METHODS: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.

RESULTS: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.

CONCLUSION: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

PMID:39911144 | PMC:PMC11792529 | DOI:10.20411/pai.v10i1.770