Sarcoidosis is an inflammatory disorder characterized by granuloma formation in affected organs, most often in the lung (~90%) (1, 2). Scadding stage of sarcoidosis is therefore based on intrathoracic involvement. The most common organs involved are the lung, skin, eyes, liver, lymph nodes, salivary glands, bone/joints/muscle, spleen, nervous system, kidneys, sinuses, and heart. For example, lung sarcoidosis is featured by the presence of coalescing, tightly clustered, non-necrotizing granulomas, and is complicated by lung fibrosis in up to 20% progressive patients who account for 75% sarcoidosis-related deaths of respiratory causes (2–5). The etiology of sarcoidosis is still unknown since it was first described 1.5 centuries ago (1). The prevalence, presentation, prognosis, and triggering antigen are extremely variable (6). The outcomes of lung sarcoidosis are affected by age, gender, race, incomes, environment, lung morbidity, lung leucocyte infiltration, requirement of treatment, and genetic variants of associated genes (e.g., human leucocyte antigen class II (HLA class II), tumor necrosis factor α (TNF-α) and annexin XI (ANXA11), among others) (1, 2). However, there are no reliable biomarkers for predicting the propensity of sarcoidosis to progress to lung fibrosis (7).

The combination of advanced Omics techniques and machine learning algorithms holds the potential to facilitate mechanistic investigations and discovery of novel biomarkers for this intriguing illness. Molecular endotypes and biomarkers provide critical information for the development of new therapeutics. Several studies on the phenotypes of sarcoidosis have been published (8–10), but they are outside the scope of this review. In sharp contrast, only one study on the molecular endotypes of the disease has recently been reported to date (11). Biomarkers are widely applied to diagnosis, differential diagnosis, prognosis, treatment response, disease activity, severity assessment, chronicity evaluation, and the implementation of interventions. In this paper, we systematically review the identified biomarkers based on their clinical relevance. The clinical data were analyzed with differentiation analysis, correlation analysis, biomarker-specific tests, and machine learning algorithms. Recently, several publications have comprehensively reviewed sarcoidosis biomarkers identified using genomics (12–14). We will not reiterate this topic here. Instead, we will focus on the transcriptomics (and other Omics) studies published since 2018, as earlier studies have already been covered in a prior review (15).

The workflow of clinical omics studies is generally consistent, yet it adapts based on the specific objectives, materials, platforms, and data analysis strategies involved ( Figure 1 ). Omics studies could be applied to the identification of candidate biomarkers, molecular endotypes, gene expression signatures, mechanisms, and druggable targets. The workflow is initiated by collecting relevant biologic specimens, including cells, tissues, and body fluid samples from sarcoidosis patients and healthy controls. The specimens are then pre-processed (e.g., obtaining single-cell suspension, extracellular vesicle isolation, or DNA/RNA/protein extraction) ready for the analysis by Omics platforms to generate Omics datasets. Once the datasets become available, data cleaning is required before downstream analysis, including normalization, missing data imputation, batch effect correction, and application of cut-off criteria. Next, the datasets will be profiled to identify differentially expressed genes/transcripts/proteins/metabolites. Various statistical and machine learning techniques are employed based on the specific tasks at hand. For instance, unsupervised clustering can be utilized to uncover novel molecular endotypes, while supervised logistic regression is appropriate for finding potential biomarkers. Omics studies can shed light on disease signatures, candidate biomarkers, molecular endotypes, and clinical disease-omics correlations. Finally, these observations will be cross-referenced to functional annotations, correlations with clinical variables, and literature reports. Validation of candidate biomarkers and molecular endotypes is also required in a separate independent cohort. An alternative strategy is to split one cohort into training and validation group. Besides, preclinical animal models of the disease and organ-on-chip could be used to validate the identified markers and mechanisms of action (16, 17).

Studies designed to identify diagnostic markers typically compare patients with healthy controls. For the identification of differential diagnosis biomarkers, other diseases often serve as control groups. In contrast, individuals with only sarcoidosis are required for the studies aimed to identify predictive biomarkers, biomarkers for the response to therapy, activity, chronicity, organ involvement, and severity of the disease, with longitudinal follow up in some cases. The average sample size across 30 non-omics studies is 88, ranging from 6 to 694. Only two studies included a separate validation cohort (27, 33).

We first review cross-sectional studies ( Tables 1 – 3 ). Among the diagnostic biomarkers for distinguishing sarcoidosis from healthy control, the following have been independently validated: serum and CSF soluble interleukin-2 receptor (sIL-2R) (AUCs: 0.67 - 0.90) (23, 37, 44, 46–49, 55), urinary U-8-OHdG (AUCs: 0.87 - 0.98) (41–43), serum angiotensin converting enzyme (ACE) (AUC: 0.78-0.88) (23, 44, 46, 55), serum chitotriosidase (AUC: 0.89) (30, 35, 40), serum KL-6 (AUC: 0.79-0.83) (18, 50), serum CRP (AUC: 0.72-0.95) (46, 50), and serum BNP (0.85-0.91) (39, 45). In addition, several isolated reports of diagnostic biomarkers have also been reported, without independent validation: BAL IL12, IL18, MMP14, CTSS, serum amyloid A, ZNF688, ARFGAP1, CD14, LBP, a-2chain of haptoglobin, and PHA, with AUCs ranging from 0.76 to 0.84. The best performers among these include serum LBP (AUCs: 0.84) and CD14 (AUC: 0.81) (23).

Serum sIL-2R have been reported to be reflective of lung dysfunction, inflammation, multiple organ involvement including thoracic phenotype, and diagnosis of neurosarcoidosis based on cerebrospinal fluid (CSF) level (37, 44, 46–49). Urinary 8-hydroxy-2′-deoxyguanosine (U-8-OHdG) has been reported to predict inflammation in active patients, cardiac death, and ventricular tachycardia (41–43). Serum chitotriosidase has been validated for the diagnosis, chronicity, and response to therapy (30, 35, 40), Serum ACE has been proposed as a biomarker for identifying active parenchymal infiltration and pulmonary involvement (44, 46). Similarly, C-reactive protein (CRP) can serve as a biomarker to discriminate between inactive and active cases, and also distinguishing sarcoidosis from interstitial lung disease (ILD) and chronic hypersensitivity pneumonitis (46, 50). Krebs von den Lungen-6 (KL-6) is a biomarker of fibrotic lungs in sarcoidosis (18, 50), as reported by Bergantini and coworkers. B-type natriuretic peptide (BNP) is a diagnostic marker for cardiac sarcoidosis and a predictor for heart failure (39, 45). Besides protein biomarkers, CD4 and CD8 T-cells, and their respective ratios in BAL extracellular vesicles (BAL EVs) and lymph node puncture fluid can potentially function as diagnostic biomarkers (32, 33). The sensitivity and specificity of these biomarkers vary. Other biomarkers reported in one study but not yet cross-validated include neopterin, interleukin-12 (IL12), interleukin 18 (IL18), surfactant protein D (SPD), metallic metalloproteinase-14 (MMP-14), a disintegrin and metalloproteinase domain-like protein decysin-1 (ADAMDEC1), B-cell-activating factor (BAFF), C-C motif chemokine receptor 4 (CCR4), chemokine receptor 3 (CXCR3), chemokine receptor 5 (CXCR5), lymphocyte profile (LP), and those composite biomarker panels (22, 25–29, 31–34, 36, 38, 51, 52).

In recent years, Omics studies for the identification of biomarkers and molecular endotypes have been emerging, using a wide array of platforms. They apply both machine learning algorithms and traditional biostatistics for data analysis. The biomarkers identified by genome-wide association and transcriptomics studies have been comprehensively reviewed recently (15, 20, 69, 70). Thus, this review will specifically delve into transcriptomics studies since 2018. Finally, we review other Omics studies, including proteomics, metabolomics, and microbiomics.

The identification of molecular endotypes for a given disease will shed light on disease pathogenicity, diagnosis, stratification, prognosis, and development of new personalized therapies. Currently, there is only one report on the “transcriptomic” endotypes of sarcoidosis (11). Four potential endotypes were identified by unsupervised analysis of RNA-seq data in BAL cells, including hilar lymphadenopathy with increased acute T-cell immune response, extraocular organ involvement with phosphatidylinositol-3-kinase (PI3K) activation pathways, chronic and multiorgan disease with increased immune response pathways, and multiorgan disease with increased IL-1 and IL-18 immune and inflammatory responses. These mRNA-based endotypes based on signatures from BAL cells await independent validation (72). In addition, a clinical trial has recently been registered to define the endotypes of CD4 T helper and T regulator cell in sarcoidosis (73). Clearly, molecular endotype studies of sarcoidosis is just in its infancy.

Despite decades of both basic and clinician research, our understanding of sarcoidosis remains limited. No specific interventions exist for systemic or single organ sarcoidosis due to the incomplete understanding of its pathogenicity. Consequently, there is an urgent need to find the molecular basis of various phenotypes and biomarkers. This pursuit is crucial for predicting long-term outcomes and responses to the therapy targeting the different manifestations of sarcoidosis. So far, a substantial portion (70%) of biomarkers identified through targeted non-Omics studies have been cross validated by different groups, various phenotypes, or distinct organ involvements ( Table 5 ). These non-Omics-derived biomarkers may serve multiple purpose, including roles in diagnosis, differential diagnosis, prognosis determination, and assessment of disease activity, chronicity, and severity, and evaluation of therapeutic response. However, these focused studies need to be expanded to or fortified with larger, unbiased Omics based studies, in order to uncover improved biomarkers for this disease.

Very few individual biomarkers are identified by both Omics and non-Omics studies. It is most likely due to divergent approaches and tissues. Non-Omics studies measured one or few biomarkers at the protein level. In contrast, Omics studies profile the landscape of genes, transcripts, proteins or metabolites and identify a panel of top-ranked biomarkers. These Omics biomarkers shall be validated by other clinical studies independently. One challenge of implementing these biomarkers clinically is the difficulty of identifying these phenotypes at the bedside using the same approaches. It remains a question whether genomics, transcriptomics, and metabolomics biomarkers could be validated by proteomics. In addition, the organ specificity of identified biomarkers may lead to the inconsistency between identified Omics and non-Omics biomarkers. Of note, the organ specific biomarker could be applied to differentiate involved organs. All validated high-quality biomarkers are highlighted in italic in Table 5 . These validated biomarkers are recommended for sarcoidosis. Without doubt, the combination of non-Omics and Omics assays will improve the identification of biomarkers in sarcoidosis.

One advantage of Omics studies is their high throughput capacity. This feature enables the identification of molecular endotypes in sarcoidosis and multiple biomarkers ranked by their importance. The development of new bioinformatics and machine learning algorithms holds significant potential for extracting more accurate or predictive information from Omics datasets, to prioritize critical biomarkers, meet clinical needs, and to identify molecular endotypes associated with different phenotypes. With respect to sarcoidosis, Omics studies are still in its infancy. These reported Omics studies allude to several potential biomarkers/panels, signaling pathways, and integrated networks, but the general paucity of sensitivity, specificity and accuracy comparisons, insufficient statistical power, and few cross-validations prevent rigorous conclusions from being drawn. Most of Omics-derived biomarker panels are not ready to be translated to bedside. More Omics studies and multi-Omic integrative investigations are needed to validate published biomarkers, and to identify more accurate biomarkers for sarcoidosis, using well annotated reference cohorts. Clinical trials are necessary to evaluate the clinical application of top-ranked biomarkers, each on an individual basis. To sum, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes to facilitate disease diagnosis and prognosis, resolve disease heterogeneity and facilitate personalized medicine.

H-LJ: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. NX: Writing – original draft, Writing – review & editing. CM: Writing – review & editing. XY: Writing – review & editing. KJ: Writing – review & editing. RZ: Writing – original draft, Writing – review & editing. QZ: Writing – review & editing. VG: Writing – review & editing.