iTRAQ-based quantitative proteomic analysis provides insight for molecular mechanism of neuroticism

Neuropathological signatures revealed by transcriptomic and proteomic analysis in Pten-deficient mouse models

PTEN hamartoma tumour syndrome is characterised by mutations in the human PTEN gene. We performed transcriptomic and proteomic analyses of neural tissues and primary cultures from heterozygous and homozygous Pten-knockout mice. The somatosensory cortex of heterozygous Pten-knockout mice was enriched in immune response and oligodendrocyte development Gene Ontology (GO) terms. Parallel proteomic analysis revealed differentially expressed proteins (DEPs) related to dendritic spine development, keratinisation and hamartoma signatures. However, primary astrocytes (ASTs) from heterozygous Pten-knockout mice were enriched in the extracellular matrix GO term, while primary cortical neurons (PCNs) were enriched in immediate-early genes. In ASTs from homozygous Pten-knockout mice, cilium-related activity was enriched, while PCNs exhibited downregulation of forebrain neuron generation and differentiation, implying an altered excitatory/inhibitory balance. By integrating DEPs with pre-filtered differentially expressed genes, we identified the enrichment of traits of intelligence, cognitive function and schizophrenia, while DEPs in ASTs were significantly associated with intelligence and depression.

Predictive value of proteomic markers for advanced rectal cancer with neoadjuvant chemoradiotherapy

Background

Preoperative neoadjuvant chemoradiation (nCRT) has been the standard treatment for locally advanced rectal cancer. Serum biomarkers to stratify patients with respect to prognosis and response to nCRT are needed due to the diverse response to the therapy.

Methods

Thirteen paired pre- and post-nCRT sera from rectal cancer patients were analyzed by isobaric tags for relative and absolute quantitation (iTRAQ) method. Twenty-five proteins were selected for validation by parallel reaction monitoring (PRM) in ninety-one patients.

Results

Totally, 310 proteins were identified and quantified in sera samples. Reactome pathway analysis showed that the immune activation-related pathways were enriched in response to nCRT. Twenty-five proteins were selected for further validation. PRM result showed that the level of PZP was higher in pathological complete response (pCR) patients than non-pCR patients. The Random Forest algorithm identified a prediction model composed of 10 protein markers, which allowed discrimination between pCR patients and non-pCR patients (area under the curve (AUC) = 0.886 on testing set). Higher HEP2 and GELS or lower S10A8 in baseline sera were associated with better prognosis. Higher APOA1 in post nCRT sera was associated with better disease-free survival (DFS).

Conclusions

We identified and confirmed a 10-protein panel for nCRT response prediction and four potential biomarkers HEP2, GELS, S10A8 and APOA1 for prognosis of rectal cancer based on iTRAQ-based comparative proteomics screening and PRM-based targeted proteomic validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09960-z.

A large-scale polygenic risk score analysis identified candidate proteins associated with anxiety, depression and neuroticism

Psychiatric disorders and neuroticism are closely associated with central nervous system, whose proper functioning depends on efficient protein renewal. This study aims to systematically analyze the association between anxiety / depression / neuroticism and each of the 439 proteins. 47,536 pQTLs of 439 proteins in brain, plasma and cerebrospinal fluid (CSF) were collected from recent genome-wide association study. Polygenic risk scores (PRS) of the 439 proteins were then calculated using the UK Biobank cohort, including 120,729 subjects of neuroticism, 255,354 subjects of anxiety and 316,513 subjects of depression. Pearson correlation analyses were performed to evaluate the correlation between each protein and each of the mental traits by using calculated PRSs as the instrumental variables of protein. In general population, six correlations were identified in plasma and CSF such as plasma protease C1 inhibitor (C1-INH) with neuroticism score (r = - 0.011, P = 2.56 × 10^- 9) in plasma, C1-INH with neuroticism score (r = -0.010, P = 3.09 × 10^- 8) in CSF, and ERBB1 with self-reported depression (r = - 0.012, P = 4.65 × 10^- 5) in CSF. C1-INH and ERBB1 may induce neuroticism and depression by affecting brain function and synaptic development. Gender subgroup analyses found that BST1 was correlated with neuroticism score in male CSF (r = - 0.011, P = 1.80 × 10^- 5), while CNTN2 was correlated with depression score in female brain (r = - 0.013, P = 6.43 × 10^- 4). BST1 and CNTN2 may be involved in nervous system metabolism and brain health. Six common candidate proteins were associated with all three traits (P < 0.05) and were confirmed in relevant proteomic studies, such as C1-INH in plasma, CNTN2 and MSP in the brain. Our results provide novel clues for revealing the roles of proteins in the development of anxiety, depression and neuroticism.

Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Methods

For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway.

Results

We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072.

Conclusion

To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC.

Prognostic Model of Eleven Genes Based on the Immune Microenvironment in Patients With Thymoma

Purpose: The pathogenesis of thymoma (THYM) remains unclear, and there is no uniform measurement standard for the complexity of THYM derived from different thymic epithelial cells. Consequently, it is necessary to develop novel biomarkers of prognosis estimation for patients with THYM.

Methods: Consensus clustering and single-sample gene-set enrichment analysis were used to divide THYM samples into different immunotypes. Differentially expressed genes (DEGs) between those immunotypes were used to do the Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology annotations, and protein-protein interaction network. Furthermore, the survival-related DEGs were used to construct prognostic model with lasso regression. The model was verified by survival analysis, receiver operating characteristic curve, and principal component analysis. Furthermore, the correlation coefficients of stemness index and riskscore, tumor mutation burden (TMB) and riskscore, drug sensitivity and gene expression were calculated with Spearman method.

Results: THYM samples were divided into immunotype A and immunotype B. A total of 707 DEGs were enriched in various cancer-related or immune-related pathways. An 11-genes signature prognostic model (CELF5, ODZ1, CD1C, DRP2, PTCRA, TSHR, HKDC1, KCTD19, RFX8, UGT3A2, and PRKCG) was constructed from 177 survival-related DEGs. The prognostic model was significantly related to overall survival, clinical features, immune cells, TMB, and stemness index. The expression of some genes were significantly related to drug sensitivity.

Conclusion: For the first time, a prognostic model of 11 genes was identified based on the immune microenvironment in patients with THYM, which may be helpful for diagnosis and prediction. The associated factors (immune microenvironment, mutation status, and stemness) may be useful for exploring the mechanisms of THYM.

iTRAQ‑based quantitative proteomics analysis of the potential application of secretoneurin gene therapy for cardiac hypertrophy induced by DL‑isoproterenol hydrochloride in mice

A previous study by our group demonstrated a protective role of the neuropeptide secretoneurin (SN) in DL‑isoproterenol hydrochloride (ISO)‑induced cardiac hypertrophy in mice. To further characterize the molecular mechanism of SN treatment, an isobaric tags for relative and absolute quantification (iTRAQ)‑based quantitative proteomic analysis was applied to identify putative target proteins and molecular pathways. An SN expression vector was injected into the myocardial tissues of mice, and the animals were then subcutaneously injected with ISO (5 mg/kg/day) for 7 days to induce cardiac hypertrophy. The results of echocardiography and hemodynamic measurements indicated that the function of the heart impaired by ISO treatment was significantly ameliorated via SN gene injection. The investigation of heart proteomics was performed by iTRAQ‑based liquid chromatography‑tandem mass spectrometry analysis. A total of 2,044 quantified proteins and 15 differentially expressed proteins were associated with SN overexpression in mice with cardiac hypertrophy. Functional enrichment analysis demonstrated that these effects were possibly associated with metabolic processes. A protein‑protein interaction network analysis was constructed and the data indicated that apolipoprotein C‑III (Apoc3) was associated with the positive effect of SN on the induction of cardiac hypertrophy in mice. The present study proposed a potential mechanism of SN action on Apoc3 upregulation that may contribute to the amelioration of cardiac hypertrophy. These findings can aid the clinical application of SN in patients with cardiac hypertrophy.