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Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein-protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics.

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T cells are essential tumor suppressors in cancer immunology, but their dysfunction induced by cancer cells can result in T cell exhaustion. Exhausted T cells (Tex) significantly influence the tumor immune environment, and thus, there is a need for their thorough investigation across different types of cancer. Here, we address the role of Tex cells in pan-cancer, focusing on the expression, mutations, methylation, immune infiltration, and drug sensitivity of a molecular signature comprising of the genes HAVCR2, CXCL13, LAG3, LAYN, TIGIT, and PDCD1across multiple cancer types, using bioinformatics analysis of TCGA data. Our analysis revealed that the Tex signature genes are differentially expressed across 14 cancer types, being correlated with patient survival outcomes, with distinct survival trends. Pathway analysis indicated that the Tex genes influence key cancer-related pathways, such as apoptosis, EMT, and DNA damage pathways. Immune infiltration analysis highlighted a positive correlation between Tex gene expression and immune cell infiltration in bladder cancer, while mutations in these genes were associated with specific immune cell enrichments in UCEC and SKCM. CNVs in Tex genes were widespread across cancers. We also highlight high LAYN methylation in most tumors and a negative correlation between methylation levels and immune cell infiltration in various cancers. Drug sensitivity analysis identified numerous correlations, with CXCL13 and HAVCR2 expressions influencing sensitivity to several drugs, including Apitolisib, Belinostat, and Docetaxel. Overall, these findings highlight the importance of reviving exhausted T cells to enhance the treatment efficacy to significantly boost anti-tumor immunity and achieve better clinical outcomes.

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Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 910 patients, were considered eligible. Mean 3- and 5-year overall survival were 25.7% and 19.6%, respectively, for the multi-omics subtypes related to poor prognosis, while they were 70.2% and 63.3%, respectively, for the subtypes linked to a better prognosis. Several negative prognostic factors were identified, such as genes' expression profile promoting inflammation, mutations in the KRAS gene, advanced tumor stage, and elevated levels of oncological markers. The subtype with worse clinicopathological characteristics was associated with worse survival (Ref.: good prognosis subtype; pooled hazard ratio 2.06, 95%CI 1.67-2.53). Several attempts have been made to classify molecular ICC subtypes, but they have yielded heterogeneous results and need a clear clinical definition. More efforts are required to build a comprehensive classification system that includes both molecular and clinical characteristics before implementation in clinical practice to facilitate decision-making and select patients who may benefit the most from comprehensive molecular profiling in the disease's earlier stages.

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Recently, interest in transcriptomic assessment of kidney biopsies has been growing. This study investigates the use of NGS to identify gene expression changes and analyse the pathways involved in rejection. An Illumina bulk RNA sequencing on the polyadenylated RNA of 770 kidney biopsies was conducted. Differentially-expressed genes (DEGs) were determined for AMR and TCMR using DESeq2. Genes were segregated according to their previous descriptions in known panels (microarray or the Banff Human Organ Transplant (B-HOT) panel) to obtain NGS-specific genes. Pathway enrichment analysis was performed using the Reactome and Kyoto Encyclopaedia of Genes and Genomes (KEGG) public repositories. The differential gene expression using NGS analysis identified 6,141 and 8,478 transcripts associated with AMR and TCMR. While most of the genes identified were included in the microarray and the B-HOT panels, NGS analysis identified 603 (9.8%) and 1,186 (14%) new specific genes. Pathways analysis showed that the B-HOT panel was associated with the main immunological processes involved during AMR and TCMR. The microarrays specifically integrated metabolic functions and cell cycle progression processes. Novel NGS-specific based transcripts associated with AMR and TCMR were discovered, which might represent a novel source of targets for drug designing and repurposing.

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Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

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The bronchoalveolar organoid (BAO) model is increasingly acknowledged as an ex-vivo platform that accurately emulates the structural and functional attributes of proximal airway tissue. The transition from bronchoalveolar progenitor cells to alveolar organoids is a common event during the generation of BAOs. However, there is a pressing need for comprehensive analysis to elucidate the molecular distinctions characterizing the pre-differentiated and post-differentiated states within BAO models. This study established a murine BAO model and subsequently triggered its differentiation. Thereafter, a suite of multidimensional analytical procedures was employed, including the morphological recognition and examination of organoids utilizing an established artificial intelligence (AI) image tracking system, quantification of cellular composition, proteomic profiling and immunoblots of selected proteins. Our investigation yielded a detailed evaluation of the morphologic, cellular, and molecular variances demarcating the pre- and post-differentiation phases of the BAO model. We also identified of a potential molecular signature reflective of the observed morphological transformations. The integration of cutting-edge AI-driven image analysis with traditional cellular and molecular investigative methods has illuminated key features of this nascent model.

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Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical-associated -related genes in patients, warranting further exploration.

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BACKGROUND: Hepatoblastoma is the most frequent pediatric liver cancer. The current treatments lead to 80% of survival rate at 5 years. In this study, we evaluated the clinical relevance of molecular features to identify patients at risk of chemoresistance, relapse and death of disease. METHODS: All the clinical data of 86 children with hepatoblastoma were retrospectively collected. Pathological slides were reviewed, tumor DNA sequencing (by whole exome, whole genome or target) and transcriptomic profiling with RNAseq or 300-genes panel were performed. Associations between the clinical, pathological, mutational and transcriptomic data were investigated. RESULTS: High-risk patients represented 44% of our series and the median age at diagnosis was 21.9 months (range: 0-208). Alterations of the WNT/ß-catenin pathway and of the 11p15.5 imprinted locus were identified in 98% and 74% of the tumors, respectively. Other cancer driver genes mutations were only found in less than 11% of tumors. After neoadjuvant chemotherapy, disease-specific survival and poor response to neoadjuvant chemotherapy were associated with 'Liver Progenitor' (p = 0.00049, p < 0.0001) and 'Immune Cold' (p = 0.0011, p < 0.0001) transcriptomic tumor subtypes, SBS35 cisplatin mutational signature (p = 0.018, p = 0.001), mutations in rare cancer driver genes (p = 0.0039, p = 0.0017) and embryonal predominant histological type (p = 0.0013, p = 0.0077), respectively. Integration of the clinical and molecular features revealed a cluster of molecular markers associated with resistance to chemotherapy and survival, enlightening transcriptomic 'Immune Cold' and Liver Progenitor' as a predictor of survival independent of the clinical features. CONCLUSIONS: Response to neoadjuvant chemotherapy and survival in children treated for hepatoblastoma are associated with genomic and pathological features independently of the clinical features.

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Metastatic progression of colorectal adenocarcinoma (CRC) remains poorly understood and poses significant challenges for treatment. To overcome these challenges, we performed multiomics analyses of primary CRC and liver metastases. Genomic alterations, such as structural variants or copy number alterations, were enriched in oncogenes and tumor suppressor genes and increased in metastases. Unsupervised mass spectrometry-based proteomics of 135 primary and 123 metastatic CRCs uncovered distinct proteomic subtypes, three each for primary and metastatic CRCs, respectively. Integrated analyses revealed that hypoxia, stemness, and immune signatures characterize these 6 subtypes. Hypoxic CRC harbors high epithelial-to-mesenchymal transition features and metabolic adaptation. CRC with a stemness signature shows high oncogenic pathway activation and alternative telomere lengthening (ALT) phenotype, especially in metastatic lesions. Tumor microenvironment analysis shows immune evasion via modulation of major histocompatibility complex (MHC) class I/II and antigen processing pathways. This study characterizes both primary and metastatic CRCs and provides a large proteogenomics dataset of metastatic progression.

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BACKGROUND: Distinguishing between allergic and nonallergic forms of Contact Dermatitis (CD) is challenging and requires investigations based on patch-testing. Early detection of allergy biomarkers in active CD lesions could refine and simplify the management of CD patients. OBJECTIVE: To characterize the molecular signatures of active CD lesions. METHODS: We studied the expression of 12 allergy biomarkers by qRT-PCR in active lesions of 38 CD patients. Allergic CD (ACD) was diagnosed based on patch test (PT) results and exposure assessment. Molecular signatures of active lesions, as well as positive PT reactions, were compared with those of reference chemical allergens and irritants. RESULTS: Nineteen of the 38 CD patients reacted positively upon patch-testing and exposure assessment confirmed ACD diagnosis for 17 of them. Gene profiling of active CD lesions revealed 2 distinct molecular patterns: patients harboring signatures similar to reference allergens (n = 23) or irritants (n = 15). Among the 23 patients with an "allergy signature," we found the 17 patients with confirmed ACD, while no culprit allergen was identified for the 6 other patients. Interestingly, the 15 patients without biomarker induction had negative PT, suggesting that they developed nonallergic CD reactions. CONCLUSION: Molecular signatures from active skin lesions may help to stratify CD patients and predict those suffering from ACD.

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Mitochondrial DNA (mtDNA) is a 16,569 base pairs, double-stranded, circular molecule that contains 37 genes coding for 13 subunits of the respiratory chain plus 2 rRNAs and 22 tRNAs. Mutations in these genes have been identified in patients with a variety of disorders affecting every system in the body. The advent of next generation sequencing technologies has provided the possibility to perform the whole mitochondrial DNA sequencing, allowing the identification of disease-causing pathogenic variants in a single platform. In this study, the whole mtDNA of 100 patients from South Italy affected by mitochondrial diseases was analyzed by using an amplicon-based approach and then the enriched libraries were deeply sequenced on the ION Torrent platform (Thermofisher Scientific Waltham, MA, USA). After bioinformatics analysis and filtering, we were able to find 26 nonsynonymous variants with a MAF <1% that were associated with different pathological phenotypes, expanding the mutational spectrum of these diseases. Moreover, among the new mutations found, we have also analyzed the 3D structure of the MT-ATP6 A200T gene variation in order to confirm suspected functional alterations. This work brings light on new variants possibly associated with several mitochondriopathies in patients from South Italy and confirms that deep sequencing approach, compared to the standard methods, is a reliable and time-cost reducing strategy to detect all the variants present in the mitogenome, making the possibility to create a genomics landscape of mitochondrial DNA variations in human diseases.

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INTRODUCTION: Clinical guidelines offer little guidance for treatment selection following inadequate response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) was validated to predict tumor necrosis factor inhibitor (TNFi) inadequate response. The decision impact of MSRC results on biologic and targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) selection was evaluated. METHODS: This is an analysis of AIMS, a longitudinal, prospective database of patients with RA tested using the MSRC. This study assessed selection of b/tsDMARDs class after MSRC testing by surveying physicians, the rate of b/tsDMARD prescriptions aligning with MSRC results, and the percentage of physicians utilizing MSRC results for decision-making. RESULTS: Of 1018 participants, 70.7% (720/1018) had treatment selected after receiving MSRC results. In this MSRC-informed cohort, 75.6% (544/720) of patients received a b/tsDMARD aligned with MSRC results, and 84.6% (609/720) of providers reported using MSRC results to guide treatment selection. The most prevalent reason reported (8.2%, 59/720) for not aligning treatment selection with MSRC results from the total cohort was health insurance coverage issues. CONCLUSION: This study showed that rheumatologists reported using the MSRC test to guide b/tsDMARD selection for patients with RA. In most cases, MSRC test results appeared to influence clinical decision-making according to physician self-report. Wider adoption of precision medicine tools like the MSRC could support rheumatologists and patients in working together to achieve optimal outcomes for RA.

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This review article discusses the epigenetic regulation of quiescent stem cells. Quiescent stem cells are a rare population of stem cells that remain in a state of cell cycle arrest until activated to proliferate and differentiate. The molecular signature of quiescent stem cells is characterized by unique epigenetic modifications, including histone modifications and deoxyribonucleic acid (DNA) methylation. These modifications play critical roles in regulating stem cell behavior, including maintenance of quiescence, proliferation, and differentiation. The article specifically focuses on the role of histone modifications and DNA methylation in quiescent stem cells, and how these modifications can be dynamically regulated by environmental cues. The future perspectives of quiescent stem cell research are also discussed, including their potential for tissue repair and regeneration, their role in aging and age-related diseases, and their implications for cancer research. Overall, this review provides a comprehensive overview of the epigenetic regulation of quiescent stem cells and highlights the potential of this research for the development of new therapies in regenerative medicine, aging research, and cancer biology.

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Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

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We use in situ liquid secondary ion mass spectroscopy, cryogenic transmission electron microscopy, and density functional theory calculation to delineate the molecular process in the formation of the solid-electrolyte interphase (SEI) layer under the dynamic operating conditions. We discover that the onset potential for SEI layer formation and the thickness of the SEI show dependence on the solvation shell structure. On a Cu film anode, the SEI is noticed to start to form at around 2.0 V (nominal cell voltage) with a final thickness of about 40-50 nm in the 1.0 M LiPF6/EC-DMC electrolyte, while for the case of 1.0 M LiFSI/DME, the SEI starts to form at around 1.5 V with a final thickness of about 20 nm. Our observations clearly indicate the inner and outer SEI layer formation and dissipation upon charging and discharging, implying a continued evolution of electrolyte structure with extended cycling.

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Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

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Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS.

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Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

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We employed supervised machine learning algorithms to a cohort of colorectal cancer patients from the NCI to differentiate and classify the heterogenous disease based on anatomical laterality and multi-omics stratification, in a first of its kind. Multi-omics integrative analysis shows distinct clustering of left and right colorectal cancer with disentangled representation of methylome and delineation of transcriptome and genome. We present novel multi-omics findings consistent with augmented hypermethylation of genes in right CRC, epigenomic biomarkers on the right in conjunction with immune-mediated pathway signatures, and lymphocytic invasion which unlocks unique therapeutic avenues. Contrarily, left CRC multi-omics signature is found to be marked by angiogenesis, cadherins, and epithelial-mesenchymal transition (EMT). An integrated multi-omics molecular signature of RNF217-AS1, hsa-miR-10b, and panel of FBX02, FBX06, FBX044, MAD2L2, and MIIP copy number altered genes have been found by the study. Overall survival analysis reveals genomic biomarkers ABCA13 and TTN in 852 LCRC cases, and SOX11 in 170 RCRC cases that predicts a significant survival benefit. Our study exemplifies the translational competence and robustness of machine learning in effective translational bridging of research and clinic. Supplementary Information: The online version contains supplementary material available at 10.1007/s13193-023-01760-6.