Construction of a Diagnosis Model for Tuberculosis based on Long Non-coding RNA
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE289620 | GEO | 2025/08/13
REPOSITORIES: GEO
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2023-04-18 | MTBLS3505 | MetaboLights
Project description:Carcinomas of unknown primary origin constitute 3-5% of all newly diagnosed metastatic cancers, of which the primary source is difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor, which is why patients with metastases of unknown origin have poor prognosis and short survival. Because microRNA expression is highly tissue specific, the microRNA profile of a metastasis may be used to identify its origin. As a first step to realize this goal, we evaluated the potential of microRNA profiling for identification of the primary tumor of known metastases. 208 formalin-fixed paraffin-embedded samples representing 15 different histologies were profiled on an LNA-enhanced microarray platform, which allows for highly sensitive and specific detection of microRNA. Based on these data, we developed and cross-validated a novel classification algorithm, LASSO (Least Absolute Shrinkage and Selection Operator), which had an overall accuracy of 85%. When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy). Our findings suggest that microRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor, and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors, and eventually, enable tailored therapy. 94 samples
2013-12-01 | E-GEOD-50890 | biostudies-arrayexpress
Project description:Background: The therapeutic regimen of immune-mediated necrotizing myopathy (IMNM), a category of immune-related myopathies featuring progressive proximal muscle weakness and extremely high level of serum creatine kinase, remains a formidable barrier for clinicians. The proposed study intended to investigate new diagnostic biomarkers based on high-throughput sequencing data of IMNM and generate novel IMNM management strategies by bioinformatics technology. Methods: Eighteen IMNM patients were synthesized in a local sequencing cohort (high-throughput sequencing data) with Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis to pinpoint robust IMNM-related differentially expressed genes (DEGs). The definitive feature genes were categorized by employing the protein-protein interaction (PPI) network, Least Absolute Shrinkage Selection Operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE), and their sample discriminatory utility was appraised by obtaining the area under the curve (AUC) of the receiver operating curve (ROC) curve. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Enrichment analysis of predefined gene sets disclosed the hidden functions of diagnostic DEGs. The 28 immune cell abundance patterns in the IMNM cohort were measured using single-sample gene set enrichment analysis (ssGSEA). Results: We recognized 193 genes that were aberrantly upregulated in the IMNM population and were firmly affiliated with immune-inflammatory responses, regulation of skeletal and cardiac muscle contraction and lipidprotein metabolism. LTK, MYBPH, and MYL4 were authenticated as diagnostic markers for IMNM (all AUC = 1.00), and functional enrichment analysis highlighted that these genes were notably integrated with immune-inflammatory response, autophagy-lysosome pathway and myofiber differentiation and regeneration. Ultimately, ssGSEA revealed a pronounced immune cell infiltrative microenvironment in the IMNM population. The results revealed the most significant relationship among CD4 T cells, CD8 T cells, macrophages, natural killer cells, T helper cells, Th17 cells and dendritic cells. Conclusion: LTK, MYBPH, and MYL4 might be served as diagnostic molecules for IMNM and possibly yield innovative insights into the mechanisms of progression and therapeutic options for IMNM in the future.
2024-09-30 | GSE213055 | GEO
Project description:People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To address these challenges, we explored circulating cell-free RNA (cfRNA) as a blood-borne bioanalyte to monitor ME/CFS. cfRNA is released into the bloodstream during cellular turnover and reflects dynamic changes in gene expression, cellular signaling, and tissue-specific processes. We profiled cfRNA in plasma by RNA sequencing for 93 ME/CFS cases and 75 healthy sedentary controls, then applied machine learning to develop diagnostic models and advance our understanding of ME/CFS pathobiology. A generalized linear model with least absolute shrinkage selector operator regression trained on condition-specific signatures achieved a test-set AUC of 0.81 and an accuracy of 77%. Immune cfRNA deconvolution revealed differences in platelet-derived cfRNA between cases and controls, as well as elevated levels of plasmacytoid dendritic, monocyte, and T cell–derived cfRNA in ME/CFS. Biological network analysis further implicated immune dysfunction in ME/CFS, with signatures of cytokine signaling and T cell exhaustion. These findings demonstrate the utility of RNA liquid biopsy as a minimally invasive tool for unraveling the complex biology behind chronic illnesses.
2025-08-18 | GSE293840 | GEO
Project description:This multicenter retrospectively observational cohort study was conducted on participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery in 11 medical centers in China from August 1, 2015, to June 31, 2018. Baseline clinicopathologic data and nutritional status assessments including Nutrition Risk Screening 2002 (NRS 2002) score and Patient-generated Subjective Global Assessment (PG-SGA) rating were collected. Variables will be screened using the least absolute shrinkage and selection operator (LASSO) regression model and Cox regression analysis. Internal and external validations will be performed via the receiver operating curve (ROC), the area under the curve (AUC), the concordance index (C-index), calibration plots, decision curve analysis (DCA), and Five folds cross-validation by 200 times.
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Project description:Abstract Background There is an urgent need for new, accurate, rapid, and affordable tuberculosis (TB) diagnostic tests. The aim of the present study was to use mass spectrometry to identify new preliminary candidate TB diagnostic protein biomarkers in saliva obtained from individuals with TB, and patients with other respiratory diseases (ORD). Methods Saliva samples were collected from 22 individuals who self-presented with symptoms suggestive of TB as part of a larger TB biomarker project. Purified salivary proteins were subjected to tryptic digestion peptides were analysed using a QExactive Orbitrap Mass Spectrometer. Identified proteins were subjected to gene ontology and ingenuity pathway analysis for functional enrichment analysis. Results 26 of the 652 identified proteins significantly discriminated individuals with TB from those with ORD after Benjamini Hochberg correction (5% FDR), with five of these proteins diagnosing TB with an AUC ≥ 0.80. A 5-protein biosignature comprising of P01011, Q8NCW5, P28072, A0A2Q2TTZ9, and Q99574 diagnosed TB with an AUC of 1.00 (95% CI, 1.00-1.00), sensitivity of 100% (95% CI, 76.2-100%) and specificity of 90.9% (95% CI, 58.7-99.8%) after leave-one-out cross validation. Conclusions We identified novel candidate salivary protein biomarkers and biosignatures with strong potential as TB diagnostic candidates. Our results are preliminary and require validation in larger studies.
2021-08-05 | PXD027294 | Pride
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:The goal of this study was to find the longitudinal transcriptional response of Mouse Embryonic Fibroblast (MEF) primary cells during the progression of a cell cycle. Using RNASeq method (single-end 50-bp chemistry on Illumina Hi-seq 2500 instrument in high-throughput mode), we measured the transcriptional response for around two cell-cycles. Using the fold-change (with respect to average response before serum addition at t = 0) time-series data, we first identified, without using a priori knowledge, the duration and timing of cell cycle phases using a change-point detection algorithm. Next, using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle.
2019-05-10 | GSE124024 | GEO
Project description:Background: Tuberculosis (TB) among persons living with HIV (PLWH) poses diagnostic challenges. Although several transcriptional signatures have recently been identified as promising tool for TB diagnosis, data are limited in persons with advanced HIV. Methodology: Reads were aligned to the human transcriptome (GRCh38 version 100), comprising both mRNA and ncRNA, with Salmon v1.2.0. A Random Forest algorithm with “leave-one-out” cross-validation was applied in the variance stabilizing transformation gene expression. The identified biomarkers were compared to previous TB transcriptional signatures using the Area Under the Curve (AUC). Results: Functional analysis indicated that common upregulated pathways in TB/HIV patients were associated with Toll-like receptor cascades and neutrophil degranulation. A machine learning decision tree algorithm identified the expression values from RAB20 and INSL3 as most informative for classifying TB status in PLWH. Only these two genes were able to correctly classify all samples.
2021-02-02 | GSE162164 | GEO
Project description:An integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), was carried out based on transcriptomics data from a series of nine matched, histologically confirmed PCa and benign samples using the Affymetrix Clariom D Human array. The analysis identified a set of potential gene markers highly associated with tumor status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using the least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in the TCGA-PRAD dataset, with an area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. These findings may improve current prognostic tools for PFS and contribute to clinical decision-making in PCa treatment.
2023-10-31 | GSE246282 | GEO