Dataset Information

Differential transcriptomics in sarcoidosis lung and lymph node granulomas with comparisons to pathogen-specific granulomas.

ABSTRACT: Rationale: Despite the availability of multi-"omics" strategies, insights into the etiology and pathogenesis of sarcoidosis have been elusive. This is partly due to the lack of reliable preclinical models and a paucity of validated biomarkers. As granulomas are a key feature of sarcoidosis, we speculate that direct genomic interrogation of sarcoid tissues, may lead to identification of dysregulated gene pathways or biomarker signatures.

Objective: To facilitate the development sarcoidosis genomic biomarkers by gene expression profiling of sarcoidosis granulomas in lung and lymph node tissues (most commonly affected organs) and comparison to infectious granulomas (coccidiodomycosis and tuberculosis).

Methods: Transcriptomic profiles of immune-related gene from micro-dissected sarcoidosis granulomas within lung and mediastinal lymph node tissues and compared to infectious granulomas from paraffin-embedded blocks. Differentially-expressed genes (DEGs) were profiled, compared among the three granulomatous diseases and analyzed for functional enrichment pathways.

Results: Despite histologic similarities, DEGs and pathway enrichment markedly differed in sarcoidosis granulomas from lymph nodes and lung. Lymph nodes showed a clear immunological response, whereas a structural regenerative response was observed in lung. Sarcoidosis granuloma gene expression data corroborated previously reported genomic biomarkers (STAB1, HBEGF, and NOTCH4), excluded others and identified new genomic markers present in lung and lymph nodes, ADAMTS1, NPR1 and CXCL2. Comparisons between sarcoidosis and pathogen granulomas identified pathway divergences and commonalities at gene expression level.

Conclusion: These findings suggest the importance of tissue and disease-specificity evaluation when exploring sarcoidosis genomic markers. This relevant translational information in sarcoidosis and other two histopathological similar infections provides meaningful specific genomic-derived biomarkers for sarcoidosis diagnosis and prognosis.

SUBMITTER: Casanova NG

PROVIDER: S-EPMC7716494 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Differential transcriptomics in sarcoidosis lung and lymph node granulomas with comparisons to pathogen-specific granulomas.

Casanova Nancy G NG Gonzalez-Garay Manuel L ML Sun Belinda B Bime Christian C Sun Xiaoguang X Knox Kenneth S KS Crouser Elliott D ED Sammani Nora N Gonzales Taylor T Natt Bhupinder B Chaudhary Sachin S Lussier Yves Y Garcia Joe G N JGN

Respiratory research 20201204 1

<h4>Rationale</h4>Despite the availability of multi-"omics" strategies, insights into the etiology and pathogenesis of sarcoidosis have been elusive. This is partly due to the lack of reliable preclinical models and a paucity of validated biomarkers. As granulomas are a key feature of sarcoidosis, we speculate that direct genomic interrogation of sarcoid tissues, may lead to identification of dysregulated gene pathways or biomarker signatures.<h4>Objective</h4>To facilitate the development sarco ...[more]

PMID: 33276795

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Project description:Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60-0.88), 0.72 (95% CI 0.57-0.87) and 0.64 (95% CI 0.48-0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61-0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61-0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66-0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Dataset Information

Differential transcriptomics in sarcoidosis lung and lymph node granulomas with comparisons to pathogen-specific granulomas.

Publications

Differential transcriptomics in sarcoidosis lung and lymph node granulomas with comparisons to pathogen-specific granulomas.

Similar Datasets

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