Identification of diagnostic biomarkers for idiopathic pulmonary … – Nature.com

The etiology of IPAH is unknown, yet the disease places a great physical, mental, and economic burden on patients. Existing studies have identified a proportion of new biomarkers able to facilitate the diagnosis of IPAH. Immune-infiltration studies of IPAH have been initially reported previously, but this investigation is the first to combine IPAH with MS. Meanwhile, the identification of candidate diagnostic genes has not been considered in the diagnosis of IPAH. We used a series of integrated bioinformatics analyses and machine learning methods to identify common pathways and shared candidate diagnostic genes for IPAH and MS. To avoid errors, we combined the DEGs and WGCNA module genes to identify a total of 287 shared candidate genes. Enrichment analysis indicated that these candidate genes are associated with immune- and metabolism-related signaling pathways. Next, we applied a machine learning approach to further screen for key genes. The crossover results of random forest and LASSO analyses were considered shared candidate diagnostic genes for IPAH and MS, and we further validated the diagnostic effect of each shared candidate diagnostic genes. In particular, EVI5L, RNASE2, and PARP10 have great diagnostic value and high AUC values.

IPAH is a rare disease characterized by increased pulmonary vascular resistance. In this study, we selected two datasets with IPAH lung tissue, which were more representative of gene expression in IPAH patients than peripheral blood gene sequencing, as analytical samples. We then verified results above using the data and found that the identified candidate diagnostic genes were equally differentially expressed in another IPAH lung tissue dataset. Therefore, we could infer that the discovered candidate diagnostic genes can detect hidden IPAH through peripheral blood examinations of MS patients, which is an insanely simple and economical operation and avoids an invasive examination by right heart catheterization.

Ultimately, we identified 11 key candidate genes (EVI5L, RNASE2, PARP10, TMEM131, TNFRSF1B, BSDC1, ACOT2, SAC3D1, SLA2, P4HB, and PHF1), and a nomogram to diagnose IPAH in MS patients showing high diagnostic value was also established.

EVI5L belongs to a small subfamily of TRE-2/BUB2/CDC16 domain proteins and is a byproduct of EVI5. EVI5L has about a 70% similar identity to Evi5. Due to the few existing reports about EVI5L, however, we mainly analyzed EVI5. EVI5 has different regulatory roles in cell cycle progression, cytokinesis, and cell membrane trafficking. In tumors, EVI5 expression is dysregulated in multiple cancer types, such as nonsmall-cell lung cancer, laryngeal cancer, and hepatocellular carcinoma, and EVI5 is therefore considered potential oncogenes and cell-cycle regulators25,26,27. EVI5 is also a risk factor for multiple sclerosis28. Multiple sclerosis is a fairly common autoimmune demyelinating disease; EVI5L may therefore play an important role in cellular immunity as an immune-related gene. However, the mechanism of EVI5L in IPAH and MS requires further investigation.

RNASE2 is an eosinophil-derived neurotoxin (EDN/RNase2) and an endolysosomal ribonuclease that acts synergistically to release uridine from oligonucleotides. RNASE2 activates human toll-like receptor 8 (TLR8), whereas TLR8 activation induces a potent T helper-1 cell response critical for defense against intracellular pathogens. This suggests that RNASE2 plays an important role in the immune system29. As an immune-related molecule, RNASE2 is a biomarker of various immune system diseases, including chronic myelogenous leukemia, systemic lupus erythematosus, rheumatoid arthritis, and multiple myeloma30,31,32,33. In terms of cancer, RNASE2 promotes the malignant progression of glioma through the PI3K/Akt signaling pathway34. It is also an immune-related biomarker used for evaluating the prognosis of gastric and kidney cancers35,36. In the respiratory system, RNASE2 affects the eosinophil-specific protein levels of the asthma family and plays a key role in allergic reactions that trigger asthma37. Previous bioinformatics studies have indicated that RNASE2 is overexpressed in IPAH and is a biomarker of IPAH38. However, existing research still does not clearly define the major role of RNASE2 in IPAH. In this study, we found that RNASE2 is a common immune- and metabolism-related biomarker for both MS and IPAH, which suggests that RNASE2 may be responsible for the development of metabolic disorders in both diseases, proving it has an important potential role in diagnosing MS patients with IPAH.

PARP10, alternatively known as ARTD10, is a PARP protein family member that performs mono-ADP-ribosylation of target proteins39. PARP10 is a metabolic regulator that plays an important role in lipid metabolism. Silencing of PARP10 induces mitochondrial oxidation and AMPK activity. PARP10 is involved in regulating cellular autophagy in cellular models; in a cell cancer model, loss of PARP10 induces fatty acid oxidation40. PARP10 is commonly expressed in human tissues, especially in the liver and spleen. The secretion of apolipoprotein B in the liver is dependent on PARP10, and PARP10 silencing reduces apolipoprotein B expression in human hepatocytes41. Therefore, the expression of PARP10 may affect very-low-density, intermediate-density, and low-density lipoprotein levels, and PARP10 is closely related to lipid metabolism. PARP10 is also involved in the inflammatory response and tumor development, being overexpressed in the majority of human tumors, including breast and ovarian tumors, oral squamous cell carcinoma, colorectal carcinoma, and hepatocellular carcinoma, and PARP10 also plays a role in promoting the proliferation of related tumors42,43,44,45. In addition, PARP10 is required for anti-DNA damage, and PARP10 gene knockout causes cellular hypersensitivity to DNA damage and a DNA replication defect46. We determined that the crossover genes of IPAH and MS are mainly enriched in metabolic and immune pathways and found that PARP10, as a metabolic regulator, plays an important role in the development and development of both diseases. Our study has demonstrated that the overexpression of PARP10 in patients with IPAH with MS may be a vital metabolic-related biomarker in patients and has high diagnostic value.

Metabolic disorders are an important pathogenesis of PAH, and drug-targeted treatment of a patients pathological metabolic state for the treatment of increased pulmonary artery pressure is an area actively being studied by many researchers. Animal model tests found that the hypoglycemic drug metformin improved endothelial function in PAH and significantly increased the survival of PAH rats47. The results of a clinical trial also confirmed that biguanide, a hypoglycemic drug administered orally, significantly improved the right ventricular fraction area of PAH patients, with a good therapeutic effect48. Legchenko et al. found that the PPAR- agonist pioglitazone reversed pulmonary hypertension through fatty acid oxidation mainly associated with lipid metabolism and disturbed mitochondrial morphology/function in right ventricular failure and pulmonary vascular hypertension49. The sodiumglucose cotransporter 2 (SGLT2) inhibitor englizin enhanced urinary glucose excretion and reduced cardiovascular events and mortality in patients with type 2 diabetes. In their study50, found that SGLT2 reduced mortality in MCT-induced PAH rats and reduced maladaptive lung remodeling.

Inflammation is a critical component of all subtypesofPAH, activated immune cells secreted more cytokines, such astumornecrosis factor- andinterleukins,canbe found in theseraof patients at levels that positively correlate withtheseverityofdiseasein PAH51.Manycirculating immune cells (e.g., macrophages, monocytes, mast cells, dendritic cells, and T-cells)havebeenshowntobeactivatedin the spleen andlungin PAH, andalargenumberare recruited to or activatedwithinthe pulmonary circulation.Theyregulatepulmonaryarterycellfunction and differentiationstatusin a paracrinefashion.Thetypesofimmunecellsinvolved in PAH can become highly glycolyticonactivation, suggesting that these cellsmightalsoberesponsiveto altered metabolic therapies and other factors51. Previous exploration of lung tissue biopsy samples from IPAH patients revealed perivascular inflammatory cell infiltration of T-cells, B-cells, and macrophages52,53. Austin et al. further found that CD8 T-cells in the lung tissue of IPAH patients were significantly increased in number and the inflammation caused by abnormal immune function and loss of autoimmunity was related to the pathophysiology of IPAH54. According to our results, IPAH patients have higher levels of memory B-cells, CD8 T-cells, follicular helper T-cells, monocytes, and M1 and M2 macrophages and lower levels of plasma cells, memory resting CD4 T-cells, Tregs, resting NK cells, NK cells, resting mast cells, and eosinophils.Our results are consistent with those of previous studies. Therefore, exploring the immune and metabolism mechanisms of IPAH could clearly pave the way for the diagnosis and treatment of IPAH. Above all, considering metabolic disorders and autoimmunity is crucial in exploring the pathophysiology of IPAH and mining therapeutics. Metabolic syndrome is a clinical feature mainly characterized by metabolic disorders. The two diseases are closely linked, and comprehensive analysis of the common biomarkers of these diseases can help with the early detection of hidden increased pulmonary vascular resistance in patients with MS, with timely medical intervention enabling greater avoidance of serious consequences.

Recent years, it has become a trend for medical scientists to use bioinformatics technology, machine learning algorithms and deep learning methods to solve related medical problems, and there are countless related literatures. Scientists have made some advanced computational models for analyzing existing lncRNA-disease associations and predicting potential human lnc RNAs for disease-disease associations, which can be effectively used to identify disease-associated lnc RNAs on a large scale and select the most promising disease-associated lnc RNAs for experimental validation55. There are also models based on network algorithms and models based on machine learning to predict new Circular RNAs-computational models for disease correlation56. While traditional biological experiments typically require a lot of time and money to study the differences in the concentration of certain metabolites in patients and those in healthy people, a new deep learning algorithm named as Graph Convolutional Network with Graph Attention Network (GCNAT) can predict potential associations of disease-associated metabolites57. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. Combination of multiple algorithms to improve model performance and enhance predictive power has become the hottest trend58. Our research combines two machine learning algorithms to greatly enhance the predictive ability of IPAH and MS comorbid diagnosis genes, with high confidence.

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