Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
Whole Blood
GSM6245977 GSM6245980 GSM6245981 GSM6245984 GSM6245990 GSM6245993 GSM6245996 GSM6246004 GSM6246013 GSM6246021 GSM6246030 GSM6246039 GSM6246040 GSM6246041 GSM6246042 GSM6246050 GSM6246051 GSM6246057 GSM6246058 GSM6246059
GSM5824840 GSM5824841 GSM5824842 GSM5824843 GSM5824844 GSM5824845 GSM5824846 GSM5824848 GSM5824850 GSM5824851 GSM5824853 GSM5824854 GSM5824856 GSM5824859 GSM5824861 GSM5824862 GSM5824863 GSM5824864 GSM5824865 GSM5824866 GSM5824867 GSM5824869 GSM5824870 GSM5824871 GSM5824872 GSM5824873 GSM5824874 GSM5824875 GSM5824877 GSM5824879 GSM5824880 GSM5824881 GSM5824882 GSM5824883 GSM5824884 GSM5824886 GSM5824887 GSM5824889 GSM5824890 GSM5824891 GSM5824892 GSM5824893 GSM5824897 GSM5824898 GSM5824899 GSM5824901 GSM5824902 GSM5824903 GSM5824905 GSM5824906 GSM5824908 GSM5824909 GSM5824910 GSM5824911 GSM5824912 GSM5824913 GSM5824914 GSM5824915 GSM5824916 GSM5824917 GSM5824919 GSM5824920 GSM5824921 GSM5824922 GSM5824923 GSM5824925 GSM5824926 GSM5824927 GSM5824928 GSM5824930 GSM5824931 GSM5824933 GSM5824935 GSM5824936 GSM5824938 GSM5824939 GSM5824941 GSM5824945 GSM5824947 GSM5824949 GSM5824950 GSM5824951 GSM5824953 GSM5824954 GSM5824956 GSM5824957 GSM5824958 GSM5824959 GSM5824960 GSM5824961 GSM5824963 GSM5824964 GSM5824965 GSM5824966 GSM5824969 GSM5824972 GSM5824973 GSM5824974 GSM5824975 GSM5824976 GSM5824977 GSM5824978 GSM5824980 GSM5824981 GSM5824985
GSM6245975 GSM6245976 GSM6245978 GSM6245979 GSM6245982 GSM6245983 GSM6245985 GSM6245986 GSM6245987 GSM6245988 GSM6245989 GSM6245991 GSM6245992 GSM6245994 GSM6245995 GSM6245997 GSM6245998 GSM6245999 GSM6246000 GSM6246001 GSM6246002 GSM6246003 GSM6246005 GSM6246006 GSM6246007 GSM6246008 GSM6246009 GSM6246010 GSM6246011 GSM6246012 GSM6246014 GSM6246015 GSM6246016 GSM6246017 GSM6246018 GSM6246019 GSM6246020 GSM6246022 GSM6246023 GSM6246024 GSM6246025 GSM6246026 GSM6246027 GSM6246028 GSM6246029 GSM6246031 GSM6246032 GSM6246033 GSM6246034 GSM6246035 GSM6246036 GSM6246037 GSM6246038 GSM6246043 GSM6246044 GSM6246045 GSM6246046 GSM6246047 GSM6246048 GSM6246049 GSM6246052 GSM6246053 GSM6246054 GSM6246055 GSM6246056 GSM6246060 GSM6246061
GSM5824838 GSM5824839 GSM5824847 GSM5824849 GSM5824852 GSM5824855 GSM5824857 GSM5824858 GSM5824860 GSM5824868 GSM5824876 GSM5824878 GSM5824885 GSM5824894 GSM5824895 GSM5824896 GSM5824900 GSM5824904 GSM5824907 GSM5824918 GSM5824924 GSM5824929 GSM5824932 GSM5824934 GSM5824940 GSM5824942 GSM5824943 GSM5824944 GSM5824948 GSM5824952 GSM5824955 GSM5824967 GSM5824968 GSM5824970 GSM5824971 GSM5824979 GSM5824982 GSM5824983 GSM5824984
Description

Submission Date: Jan 19, 2022

Summary: Globally, the anti-tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success rates are lower among people with diabetes mellitus (DM). Predicting treatment failure early after diagnosis would allow early treatment adaptation and may improve global TB control. Methods Samples were collected in a longitudinal cohort study of adult TB patients with or without concomitant DM from South Africa and Indonesia to characterize whole blood transcriptional profiles before and during anti-TB treatment, using unbiased RNA-Seq and targeted gene dcRT-MLPA. Findings We report differences in whole blood transcriptome profiles between patients with a good versus poor anti-TB treatment outcome, which were observed before initiation of treatment and throughout treatment. An eight-gene and 22-gene blood transcriptional signatures distinguished patients with a good treatment outcome from patients with a poor treatment outcome at diagnosis (AUC=0·815) or two weeks (AUC=0·834) after initiation of anti-TB treatment, respectively. Importantly, high accuracy was obtained by cross-validating this signature in an external cohort (AUC=0·749). Interpretation These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM.

GEO Accession ID: GSE193979

PMID: 35841871

Description

Submission Date: Jan 19, 2022

Summary: Globally, the anti-tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success rates are lower among people with diabetes mellitus (DM). Predicting treatment failure early after diagnosis would allow early treatment adaptation and may improve global TB control. Methods Samples were collected in a longitudinal cohort study of adult TB patients with or without concomitant DM from South Africa and Indonesia to characterize whole blood transcriptional profiles before and during anti-TB treatment, using unbiased RNA-Seq and targeted gene dcRT-MLPA. Findings We report differences in whole blood transcriptome profiles between patients with a good versus poor anti-TB treatment outcome, which were observed before initiation of treatment and throughout treatment. An eight-gene and 22-gene blood transcriptional signatures distinguished patients with a good treatment outcome from patients with a poor treatment outcome at diagnosis (AUC=0·815) or two weeks (AUC=0·834) after initiation of anti-TB treatment, respectively. Importantly, high accuracy was obtained by cross-validating this signature in an external cohort (AUC=0·749). Interpretation These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM.

GEO Accession ID: GSE193979

PMID: 35841871

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.