Identification of AGXT2, SHMT1, and ACO2 as important biomarkers of acute kidney injury by WGCNA

Prediction and Validation of Hub Genes Related to Major Depressive Disorder Based on Co-expression Network Analysis

Major depressive disorder (MDD) is generally among the most prevalent psychiatric illnesses. Significant advances have occurred in comprehension of the MDD biology. However, it is still essential to recognize new biomarkers for potential targeted treatment of patients with MDD. The present work deals with in-depth comparative computational analyses to obtain new insights, such as gene ontology and pathway enrichment analyses and weighted gene co-expression network analysis (WGCNA) through gene expression dataset. The expression of selected hub-genes was validated in MDD patients using quantitative real-time PCR (RT-qPCR). We found that MDD progression includes the turquoise module genes (p-value = 1e-18, r = 0.97). According to gene enrichment analysis, the cytokine-mediated signaling pathway mostly involves genes in this module. By selection of four candidate hub-genes (IL6, NRG1, TNF, and BDNF), RT-qPCR validation was performed. A significant NRG1 downregulation was revealed by the RT-qPCR outcomes in MDD. In MDD patients, TNF and IL6 expression were considerably higher, and no considerable differences were found in the BDNF expression. Ultimately, based on ROC analyses, IL6, NRG1, and TNF had a higher MDD diagnostic performance. Therefore, our study presents information on the intricate association between MDD development and cytokine-mediated signaling, thus providing new rationales to develop new therapeutic approaches.

Analysis of the differentially expressed genes in the combs and testes of Qingyuan partridge roosters at different developmental stages

BACKGROUND

The sexual maturity of chickens is an important economic trait, and the breeding of precocious and delayed puberty roosters is an important selection strategy for broilers. The comb serves as an important secondary sexual characteristic of roosters and determines their sexual precocity. Moreover, comb development is closely associated with gonad development in roosters. However, the underlying molecular mechanism regulating the sexual maturity of roosters has not yet been fully explored.

RESULTS

In order to identify the genes related to precocious puberty in Qingyuan partridge roosters, and based on the synchrony of testis and combs development, combined with histological observation and RNA-seq method, the developmental status and gene expression profile of combs and testis were obtained. The results showed that during the early growth and development period (77 days of age), the development of combs and testis was significant in the high comb (H) group versus the low comb (L) group (p < 0.05); however, the morphological characteristic of the comb and testicular tissues converged during the late growth and development period (112 days of age) in the H and L groups. Based on these results, RNA-sequencing analysis was performed on the comb and testis tissues of the 77 and 112 days old Qingyuan Partridge roosters with different comb height traits. GO and KEGG analysis enrichment analysis showed that the differentially expressed genes were primarily enriched in MAPK signaling, VEGF signaling, and retinol metabolism pathways. Moreover, weighted correlation network analysis and module co-expression network analysis identified WNT6, AMH, IHH, STT3A, PEX16, KPNA7, CATHL2, ROR2, PAMR1, WISP2, IL17REL, NDRG4, CYP26B1, and CRHBP as the key genes associated with the regulation of precocity and delayed puberty in Qingyuan Partridge roosters.

CONCLUSIONS

In summary, we identified the key regulatory genes of sexual precocity in roosters, which provide a theoretical basis for understanding the developmental differences between precocious and delayed puberty in roosters.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Background

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.

Methods

Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.

Results

Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56^dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.

Conclusion

Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.