TSHR Variant.xlsx

Three hundred and sixty-seven patients with primary congenital hypothyroidism were screened for variants in TSHR by whole exome sequencing.</p

Table_3_Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis.xlsx

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.</p

Table_2_Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis.xlsx

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.</p

Table_4_Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis.xlsx

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.</p

Table_1_Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis.xlsx

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.</p

Table_1_Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome.XLSX

Background: Turner syndrome (TS) is a sex chromosome aneuploidy with a variable spectrum of symptoms including short stature, ovarian failure and skeletal abnormalities. The etiology of TS is complex, and the mechanisms driving its pathogenesis remain unclear.Methods: In our study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE46687 to identify differentially expressed genes (DEGs) between monosomy X TS patients and normal female individuals. The relevant data on 26 subjects with TS (45,XO) and 10 subjects with the normal karyotype (46,XX) was investigated. Then, tissue-specific gene expression, functional enrichment, and protein-protein interaction (PPI) network analyses were performed, and the key modules were identified.Results: In total, 25 upregulated and 60 downregulated genes were identified in the differential expression analysis. The tissue-specific gene expression analysis of the DEGs revealed that the system with the most highly enriched tissue-specific gene expression was the hematologic/immune system, followed by the skin/skeletal muscle and neurologic systems. The PPI network analysis, construction of key modules and manual screening of tissue-specific gene expression resulted in the identification of the following five genes of interest: CD99, CSF2RA, MYL9, MYLPF, and IGFBP2. CD99 and CSF2RA are involved in the hematologic/immune system, MYL9 and MYLPF are related to the circulatory system, and IGFBP2 is related to skeletal abnormalities. In addition, several genes of interest with possible roles in the pathogenesis of TS were identified as being associated with the hematologic/immune system or metabolism.Conclusion: This discovery-driven analysis may be a useful method for elucidating novel mechanisms underlying TS. However, more experiments are needed to further explore the relationships between these genes and TS in the future.</p

Geometric means of carotid-to-femoral pulse wave velocity (cfPWV, in m/s) by the presence of prediabetes status defined by IFG, IGT, and high HbA1c (5.7–6.4%).

<p>The analyses were adjusted for age, sex, BMI, MAP, heart rate and lipids (total cholesterol, triglyceride, HDL-C and LDL-C). Symbol ‘*’ represents significant difference (p<0.05) between the ‘absence’ and ‘presence’ groups of each marker.</p

DataSheet_5_Identification of SERPINA1 promoting better prognosis in papillary thyroid carcinoma along with Hashimoto’s thyroiditis through WGCNA analysis.xls

BackgroundHashimoto’s thyroiditis (HT) is an autoimmune thyroid disease. Papillary thyroid carcinoma (PTC) is the most common endocrine cancer. In recent years the rate of coexistence between PTC and HT has increased but the relationship between them remains unclear, meaning it is necessary to find potential biomarkers for PTC coexistence with HT to predict its potential pathways.MethodA co-expression network was constructed using the weighted gene co-expression network analysis (WGCNA) in the R package. The modules of PTC associated with HT (PTC-W) were identified from the GSE138198 dataset. Protein-protein interaction network (PPI) was used to screen the hub genes. Immunohistochemical (IHC) analysis was performed to validate the expression of the hub genes in tissues. Clinical data from The Cancer Genome Atlas (TCGA) datasets were used to analyse the prognosis of the hub genes. Gene set enrichment analysis (GSEA) was used to screen potential pathways of PTC-W.ResultThe MEbrown module representing the most significant module, with 958 differentially expressed genes (DEGs), was screened in PTC-W, based on WGCNA analysis. Through PPI, SERPINA1 was identified as a hub gene. Immunostaining validated that SERPINA1 was highly expressed in PTC-W. Moreover, PTC-W expressing SERPINA1 exhibits a better prognosis than PTC without HT (PTC-WO).ConclusionOur study demonstrates that SERPINA1 promotes the occurrence of PTC-W, and its prognosis is better than PTC-WO. SERPINA1 promotes a better prognosis for PTC-W, possibly through a tumour inhibition signalling pathway.</p

Stratified associations between HbA1c and cfPWV (m/s) by sex, age and BMI.

<p>Analyses were adjusted for age, sex, BMI, total cholesterol, triglyceride, HDL-C, LDL-C, blood pressure, and heart rate but not the strata variable. cfPWV is presented as mean (standard error).</p

DataSheet_3_Identification of SERPINA1 promoting better prognosis in papillary thyroid carcinoma along with Hashimoto’s thyroiditis through WGCNA analysis.csv

BackgroundHashimoto’s thyroiditis (HT) is an autoimmune thyroid disease. Papillary thyroid carcinoma (PTC) is the most common endocrine cancer. In recent years the rate of coexistence between PTC and HT has increased but the relationship between them remains unclear, meaning it is necessary to find potential biomarkers for PTC coexistence with HT to predict its potential pathways.MethodA co-expression network was constructed using the weighted gene co-expression network analysis (WGCNA) in the R package. The modules of PTC associated with HT (PTC-W) were identified from the GSE138198 dataset. Protein-protein interaction network (PPI) was used to screen the hub genes. Immunohistochemical (IHC) analysis was performed to validate the expression of the hub genes in tissues. Clinical data from The Cancer Genome Atlas (TCGA) datasets were used to analyse the prognosis of the hub genes. Gene set enrichment analysis (GSEA) was used to screen potential pathways of PTC-W.ResultThe MEbrown module representing the most significant module, with 958 differentially expressed genes (DEGs), was screened in PTC-W, based on WGCNA analysis. Through PPI, SERPINA1 was identified as a hub gene. Immunostaining validated that SERPINA1 was highly expressed in PTC-W. Moreover, PTC-W expressing SERPINA1 exhibits a better prognosis than PTC without HT (PTC-WO).ConclusionOur study demonstrates that SERPINA1 promotes the occurrence of PTC-W, and its prognosis is better than PTC-WO. SERPINA1 promotes a better prognosis for PTC-W, possibly through a tumour inhibition signalling pathway.</p