Bioinformatic Analysis Identifying PSMB 1/2/3/4/6/8/9/10 as Prognostic Indicators in Clear Cell Renal Cell Carcinoma

Discovery of Kinetin in inhibiting colorectal cancer progression via enhancing PSMB1-mediated RAB34 degradation

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Understanding the underlying mechanism driving CRC progression and identifying potential therapeutic drug targets are of utmost urgency. We previously utilized LC-MS-based proteomic profiling to identify proteins associated with postoperative progression in stage II/III CRC. Here, we revealed that proteasome subunit beta type-1 (PSMB1) is an independent predictor for postoperative progression in stage II/III CRC. Mechanistically, PSMB1 binds directly to onco-protein RAB34 and promotes its proteasome-dependent degradation, potentially leading to the inactivation of the MEK/ERK signaling pathway and inhibition of CRC progression. To further identify potential anticancer drugs, we screened a library of 2509 FDA-approved drugs using computer-aided drug design (CADD) and identified Kinetin as a potentiating agent for PSMB1. Functional assays confirmed that Kinetin enhanced the interaction between PSMB1 and RAB34, hence facilitated the degradation of RAB34 protein and decreased the MEK/ERK phosphorylation. Kinetin suppresses CRC progression in patient-derived xenograft (PDX) and liver metastasis models. Conclusively, our study identifies PSMB1 as a potential biomarker and therapeutic target for CRC, and Kinetin as an anticancer drug by enhancing proteasome-dependent onco-protein degradation.

Molecular markers of type II alveolar epithelial cells in acute lung injury by bioinformatics analysis

In this study, we aimed to identify molecular markers associated with type II alveolar epithelial cell injury in acute lung injury (ALI) models using bioinformatics methods. The objective was to provide new insights for the diagnosis and treatment of ALI/ARDS. We downloaded RNA SEQ datasets (GSE109913, GSE179418, and GSE119123) from the Gene Expression Omnibus (GEO) and used R language package to screen differentially expressed genes (DEGs). DEGs were annotated using Gene Ontology (GO), and their pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG). DEGs were imported into the STRING database and analyzed using Cytoscape software to determine the protein network of DEGs and calculate the top 10 nodes for the hub genes. Finally, potential therapeutic drugs for the hub genes were predicted using the DGIdb database. We identified 78 DEGs, including 70 up-regulated genes and 8 down-regulated genes. GO analysis revealed that the DEGs were mainly involved in biological processes such as granulocyte migration, response to bacterial-derived molecules, and cytokine-mediated signaling pathways. Additionally, they had cytokine activity, chemokine activity, and receptor ligand activity, and functioned in related receptor binding, CXCR chemokine receptor binding, G protein-coupled receptor binding, and other molecular functions. KEGG analysis indicated that the DEGs were mainly involved in TNF signaling pathway, IL-17 signaling pathway, NF-κB signal pathway, chemokine signal pathway, cytokine-cytokine receptor interaction signal pathway, and others. We identified eight hub genes, including IRF7, IFIT1, IFIT3, PSMB8, PSMB9, BST2, OASL2, and ZBP1, which were all up-regulated genes. We identified several hub genes of type II alveolar epithelial cells in ALI mouse models using bioinformatics analysis. These results provide new targets for understanding and treating of ALI.

Complexity in radiological morphology predicts worse prognosis and is associated with an increase in proteasome component levels in clear cell renal cell carcinoma

Background

We previously reported preoperative radiological morphology (RM) as an independent predictor for pathological upstaging after partial nephrectomy in patients with T1 renal cell carcinoma (RCC).

Purpose

To investigate the prognostic importance of RM in all stages and the molecular characteristics underlying the differences between each type of RM in patients with clear cell RCC (ccRCC).

Design setting and participants

The Cancer Imaging Archive datasets (TCIA), comprising CT images and RNA-sequencing data, were used (n = 163). Specimens from 63 patients with ccRCC at our institution and their CT images were used. All images were divided into three types according to RM classification.

Outcome measurements and statistical analysis

Relationships with outcome were analyzed using Cox regression analysis and log-rank test.

Results and limitations

The irregular type was a significant independent predictor of worse disease-free survival (odds ratio: 2.22, p = 0.037) compared to round and lobular types in TCIA datasets. The irregular type showed a significant increase in both mRNA and protein expression of proteasome components, PSMB1 and PSMB3. Moreover, high expression of their coding genes shortened the progression-free survival of the patients with ccRCC who received sunitinib or avelumab plus axitinib therapy. The study limitations include the qualitative classification of RM and the need for novel radiomics and texture analysis techniques.

Conclusions

Investigating RM on pre-treatment CT scans can effectively predict worse prognosis. Increased RM complexity may indirectly predict drug sensitivity via increased expression of PSMB1 and PSMB3 in patients with ccRCC. Specific targeting of the ubiquitin-proteasome system might be a novel treatment strategy for ccRCC with increased RM complexity.

Patient summary

The clinical and morphological characteristics of patients with ccRCC vary greatly according to cancer staging. In this study, we built upon our prior findings of the prognostic importance of RM in T1 RCC and expanded it to encompass all stages of RCC, using a series of patients from a Japanese hospital.