Identification of potential biomarkers associated with immune infiltration in papillary renal cell carcinoma

Knockdown of NFIC Promotes Bovine Myoblast Proliferation through the CENPF/CDK1 Axis

As cellular transcription factors and DNA replicators, nuclear factor I (NFI) family members play an important role in mammalian development. However, there is still a lack of research on the muscle regeneration of NFI family members in cattle. In this study, the analysis of NFI family factors was conducted on their characterization, phylogenetics, and functional domains. We found that NFI family members were relatively conserved among different species, but there was heterogeneity in amino acid sequences, DNA coding sequences, and functional domain among members. Furthermore, among NFI family factors, we observed that NFIC exhibited highly expression in bovine muscle tissues, particularly influencing the expression of proliferation marker genes in myoblasts. To investigate the influence of NFIC on myoblast proliferation, we knocked down NFIC (si-NFIC) and found that the proliferation of myoblasts was significantly promoted. In terms of regulation mechanism, we identified that si-NFIC could counteract the inhibitory effect of the cell cycle inhibitor RO-3306. Interestingly, CENPF, as the downstream target gene of NFIC, could affect the expression of CDK1, CCNB1, and actively regulate the cell cycle pathway and cell proliferation. In addition, when CENPF was knocked down, the phosphorylation of p53 and the expression of Bax were increased, but the expression of Bcl2 was inhibited. Our findings mainly highlight the mechanism by which NFIC acts on the CENPF/CDK1 axis to regulate the proliferation of bovine myoblasts.

TPX2 influences the regulation of macrophage polarization via the NF-κB pathway in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer. Xklp2 targeting protein (TPX2), a crucial oncogene exhibits high expression levels in various cancers including LUAD, may serve as a potential target for clinical intervention. Additionally, the growth of lung cancer is significantly influenced by the tumor microenvironment (TME). However, there have been no reports on experiments investigating TPX2 in tumor-infiltrating immune cells (TIICs) in LUAD. Therefore, we verified the effect of TPX2 on macrophage polarization both in vitro and in vivo.

METHODS

We silenced TPX2 the gene in A549 cells and collected supernatants for macrophage culture. We then used flow cytometry and Western blot analysis to assess macrophage polarization. Additionally, we verified the expression of macrophage colony-stimulating factor (M-CSF), and CD163 by immunohistochemistry (IHC) in tissue specimens from LUAD patients. Finally, pathways related to TPX2's regulatory function in macrophage polarization were analyzed through whole genome sequencing, Western blotting, and immunofluorescence (IF).

RESULTS

Silencing TPX2 can affect the ratio of CD80+ M1/CD163+ M2 and reduce the polarization of M0 macrophages to CD163+ M2 macrophages mainly by inhibiting the expression of M-CSF. In human LUAD tissues, the expression levels of TPX2, M-CSF and CD163 increased with the degree of differentiation. Silencing TPX2 inhibits the NF-κB signaling pathway, thereby reducing the expression of M-CSF, and affecting macrophage polarization.

CONCLUSION

Silencing TPX2 can inhibit the expression of M-CSF by blocking the NF-κB signal, thereby reducing CD163+ M2 macrophage polarization. The TPX2/NF-κB/M-CSF signaling axis may be involved in regulating macrophage polarization.

Comparative transcriptomics analysis identifies crucial genes and pathways during goose spleen development

As the largest peripheral lymphoid organ in poultry, the spleen plays an essential role in regulating the body's immune capacity. However, compared with chickens and ducks, information about the age- and breed-related changes in the goose spleen remains scarce. In this study, we systematically analyzed and compared the age-dependent changes in the morphological, histological, and transcriptomic characteristics between Landes goose (LG; Anser anser) and Sichuan White goose (SWG; Anser cygnoides). The results showed a gradual increase in the splenic weights for both LG and SWG until week 10, while their splenic organ indexes reached the peak at week 6. Meanwhile, the splenic histological indexes of both goose breeds continuously increased with age, reaching the highest levels at week 30. The red pulp (RP) area was significantly higher in SWG than in LG at week 0, while the splenic corpuscle (AL) diameter was significantly larger in LG than in SWG at week 30. At the transcriptomic level, a total of 1710 and 1266 differentially expressed genes (DEGs) between week 0 and week 30 were identified in spleens of LG and SWG, respectively. Meanwhile, a total of 911 and 808 DEGs in spleens between LG and SWG were identified at weeks 0 and 30, respectively. Both GO and KEGG enrichment analysis showed that the age-related DEGs of LG or SWG were dominantly enriched in the Cell cycle, TGF-beta signaling, and Wnt signaling pathways, while most of the breed-related DEGs were enriched in the Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction, ECM-receptor interaction, and metabolic pathways. Furthermore, through construction of protein-protein interaction networks using significant DEGs, it was inferred that three hub genes including BUB1, BUB1B, and TTK could play crucial roles in regulating age-dependent goose spleen development while GRIA2, GRIA4, and RYR2 could be crucial for the breed-specific goose spleen development. These data provide novel insights into the splenic developmental differences between Chinese and European domestic geese, and the identified crucial pathways and genes are helpful for a better understanding of the mechanisms regulating goose immune functions.

Novel molecular classification and prognosis of papillary renal cell carcinoma based on a large-scale CRISPR-Cas9 screening and machine learning

Papillary renal cell carcinoma (PRCC) is a highly heterogeneous cancer, and PRCC patients with advanced/metastatic subgroup showed obviously shorter survival compared to other kinds of renal cell carcinomas. However, the molecular mechanism and prognostic predictors of PRCC remain unclear and are worth deep studying. The aim of this study is to identify novel molecular classification and construct a reliable prognostic model for PRCC. The expression data were retrieved from TCGA, GEO, GTEx and TARGET databases. CRISPR data was obtained from Depmap database. The key genes were selected by the intersection of CRISPR-Cas9 screening genes, differentially expressed genes, and genes with prognostic capacity in PRCC. The molecular classification was identified based on the key genes. Drug sensitivity, tumor microenvironment, somatic mutation, and survival were compared among the novel classification. A prognostic model utilizing multiple machine learning algorithms based on the key genes was developed and tested by independent external validation set. Our study identified three clusters (C1, C2 and C3) in PRCC based on 41 key genes. C2 had obviously higher expression of the key genes and lower survival than C1 and C3. Significant differences in drug sensitivity, tumor microenvironment, and mutation landscape have been observed among the three clusters. By utilizing 21 combinations of 9 machine learning algorithms, 9 out of 41 genes were chosen to construct a robust prognostic signature, which exhibited good prognostic ability. SERPINH1 was identified as a critical gene for its strong prognostic ability in PRCC by univariate and multiple Cox regression analyses. Quantitative real-time PCR and Western blot demonstrated that SERPINH1 mRNA and protein were highly expressed in PRCC cells compared with normal human renal cells. This study exhibited a new molecular classification and prognostic signature for PRCC, which may provide a potential biomarker and therapy target for PRCC patients.

Identification of prognostic and therapeutic biomarkers in type 2 papillary renal cell carcinoma

BACKGROUND

Papillary renal cell carcinoma (PRCC) can be divided into type 1 (PRCC1) and type 2 (PRCC2) and PRCC2 share a more invasive phenotype and worse prognosis. This study aims to identify potential prognostic and therapeutic biomarkers in PRCC2.

METHODS

A cohort from The Cancer Genome Atlas and two datasets from Gene Expression Omnibus were examined. Common differentially expressed genes (DEGs) were screened and potential biomarkers were explored by using Kaplan-Meier method and cox regression analysis. Functional enrichment analysis was utilized to evaluate the potential biological functions. Tumor infiltrating immune cells were estimated by CIBERSORT algorithm. Ninety-two PRCC2 samples from Fudan University Shanghai Cancer Center were obtained, and immunostaining was performed to validate prognostic and therapeutic significance of the potential biomarker.

RESULTS

PRCC2 has worse overall survival and shares distinct molecular characteristics from PRCC1. There was significant higher expression level of Targeting protein for Xklp2 (TPX2) in PRCC2 compared with normal tissues. Higher expression level of TPX2 was significantly associated with worse overall survival in PRCC2 and kinesin family genes expression were found significantly elevated in high risk PRCC2. Abundance of tumor infiltrating M1 macrophage was significantly higher in PRCC2 and it was also associated with worse overall survival. In the FUSCC cohort, higher TPX2 expression was significantly correlated with worse overall and progression-free survival. Retrospective analysis indicated that mTOR inhibitor (everolimus) had greater efficacy in the high-risk group than in the low-risk group (overall response rate: 28.6% vs. 16.7%) and that everolimus had greater efficacy than sunitinib in the high-risk group (overall response rate: 28.6% vs. 20%).

CONCLUSIONS

TPX2 was a prognostic and therapeutic biomarker in PRCC2. Higher abundance of tumor infiltrating M1 macrophage was significantly associated with worse overall survival in PRCC2. mTOR inhibitors may have good efficacy in patients with high-risk PRCC2.

High expression of KNL1 in prostate adenocarcinoma is associated with poor prognosis and immune infiltration

Prostate adenocarcinoma (PRAD) is a common malignancy with increasing morbidity and mortality. Kinetochore scaffold 1 (KNL1) has been reported to be involved in tumor progression and prognosis in other tumors, but its role in PRAD has not been reported in detail. KNL1 expression analysis, clinicopathological parameters analysis, prognostic correlation analysis, molecular interaction network and functional abdominal muscle analysis and immune infiltration analysis by using multiple online databases and downloaded expression profile. The results suggest that KNL1 is highly expressed in PRAD, which is associated with worse prognosis in PRAD patients. KnL1-related genes are highly enriched in mitotic function, which is considered to be highly related to the development of cancer. Finally, KNL1 expression is associated with a variety of tumor infiltrating immune cells, especially Treg and Th2 cells. In conclusion, our findings provide preliminary evidence that KNL1 may be an independent prognostic predictor of PRAD and is associated with immune infiltration.

Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses

Objective

Numerous studies have reported on the pathogenesis of poor immune reconstitution (PIR) after antiretroviral treatment in human immunodeficiency virus (HIV) patients. However, fewer studies focused on both immune-related genes (IRGs) and immune cells, and the correlation between IRGs and immune cells was evaluated via bioinformatics analyses.

Methods

Gene expression profiling of GSE143742 from the Gene Expression Omnibus (GEO) database was analyzed to get differentially expressed immune-related genes (DEIRGs). The enrichment analysis and protein-protein interaction (PPI) networks of DEIRGs were established. The relative fractions of 22 immune cell types were detected using the "CIBERSORT". The correlation analysis between DEIRGs and immune cells was constructed to discover the potential IRGs associated with immune cells. A logistic regression diagnostic model was built, and a receiver operating characteristic (ROC) curve was performed to evaluate the model's diagnostic efficacy. The CMap database was used to find molecules with therapeutic potential. RT-qPCR was used to verify the expression of the hub DEIRGs.

Results

We identified eight types of significantly changed immune cells and five hub IRGs in INRs. The DEIRGs were mainly enriched in lymphocyte activation, receptor-ligand activity, and T cell receptor signaling pathway. The correlation analysis showed that the expression of TNF, CXCR4 and TFRC correlate with CD8 cells, resting mast cells, activated NK cells, and naïve CD4 cells in INRs. Meanwhile, TFRC and IL7R relate to activated NK cells and resting memory CD4 cells respectively in IRs. A diagnostic model was constructed using multiple logistic regression and nine small molecules were identified as possible drugs.

Conclusion

In this study, we suggested that the process of PIR might be related to TNF, CXCR4, TFRC, CD48, and IL7R. And these IRGs play roles in regulating immune-competent cells. And our constructed diagnostic model has excellent effectiveness. Moreover, some small-molecule drugs are screened to alleviate PIR.

Identification of potential biomarkers associated with immune infiltration in papillary renal cell carcinoma

Background

Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune‐mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune‐related biomarkers for pRCC.

Methods

The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC samples. Univariate Cox analysis was used to select the prognostic‐related tumor‐infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic‐related TIICs. Then, these pRCC samples were divided into low‐ and high‐risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high‐ and low‐risk groups. The hub genes were identified using a weighted gene co‐expression network analysis (WGCNA) and a protein‐protein interaction (PPI) analysis. The hub genes were subsequently validated by multiple clinical traits and databases.

Results

According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune‐related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.

Conclusion

In this study, our data revealed the crucial TIICs and potential immune‐related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.