BUB1B Overexpression Is an Independent Prognostic Marker and Associated with CD44, p53, and PD-L1 in Renal Cell Carcinoma

CD44 and its implication in neoplastic diseases

CD44, a nonkinase single span transmembrane glycoprotein, is a major cell surface receptor for many other extracellular matrix components as well as classic markers of cancer stem cells and immune cells. Through alternative splicing of CD44 gene, CD44 is divided into two isoforms, the standard isoform of CD44 (CD44s) and the variant isoform of CD44 (CD44v). Different isoforms of CD44 participate in regulating various signaling pathways, modulating cancer proliferation, invasion, metastasis, and drug resistance, with its aberrant expression and dysregulation contributing to tumor initiation and progression. However, CD44s and CD44v play overlapping or contradictory roles in tumor initiation and progression, which is not fully understood. Herein, we discuss the present understanding of the functional and structural roles of CD44 in the pathogenic mechanism of multiple cancers. The regulation functions of CD44 in cancers-associated signaling pathways is summarized. Moreover, we provide an overview of the anticancer therapeutic strategies that targeting CD44 and preclinical and clinical trials evaluating the pharmacokinetics, efficacy, and drug-related toxicity about CD44-targeted therapies. This review provides up-to-date information about the roles of CD44 in neoplastic diseases, which may open new perspectives in the field of cancer treatment through targeting CD44.

m6A demethylase ALKBH5 maintains stemness of intrahepatic cholangiocarcinoma by sustaining BUB1B expression and cell proliferation

ALKBH5 plays critical roles in various cellular processes via post-transcriptional regulation of oncogenes or tumor suppressors in an N6-methyladenosine (m6A)-dependent manner. However, its function in intrahepatic cholangiocarcinoma (ICC) remains unclear. In the present study, bioinformatic analyses of The Cancer Genome Atlas (TCGA) data were performed, and the association of ALKBH5 in predicting overall survival in patients with ICC was investigated. Then, the clinical data of patients from The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University (Changzhou, China) was used to reveal the overall survival of patients with ICC with different ALKBH5 expression levels by Kaplan-Meier survival analysis. Subsequently, in vitro and in vivo studies were conducted to explore and verify the downstream genes regulated by ALKBH5. The results from TCGA data demonstrated that ALKBH5 expression is elevated in ICC and that patients with high ALKBH5 expression exhibited poor survival compared with patients with low expression. In addition, in vitro assays demonstrated that ALKBH5 promoted cell viability and maintained the stemness of ICC cells, leading to ICC progression. The present study also demonstrated that BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) is the downstream gene regulated by ALKBH5 and targeting BUB1B suppressed cell growth. The in vitro and vivo experiments revealed that ALKBH5 might function through BUB1B to maintain the stemness of ICC and that altering BUB1B may suppress ICC progression.

The m6A reader IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties, and immune resistance of non-small-cell lung cancer stem cells

N6-methyladenosine (m6A) modification is the most common internal modification in eukaryotic mRNA and an important mechanism for post-transcriptional regulation of genes. This study focuses on the role of the m6A reader insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) in the malignant behaviors of non-small-cell lung cancer (NSCLC) cells and especially the cancer stem cells (CSCs). We obtained IGF2BP1 as an aberrantly upregulated gene linking to poor survival of patients with NSCLC by bioinformatics, and then confirmed increased IGF2BP1 expression in NSCLC tissues and cells, especially in the enriched CSCs. Knockdown of IGF2BP1 suppressed proliferation, mobility and epithelial-mesenchymal transition activity of NSCLC cells and CSCs, and it reduced stemness, self-renewal ability, xenograft tumorigenesis and immune resistance of the CSCs. IGF2BP1 was predicted to have a positive correlation with BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and it upregulated BUB1B expression through m6A modification. Further overexpression of BUB1B in CSCs counteracted the effects of IGF2BP1 silencing and restored the malignant phenotype, self-renewal, and immune resistance of CSCs in vitro and in vivo. Taken together, this work demonstrates that IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties and immune resistance of NSCLC stem cells.

Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values

Objective

Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes among which clear cell RCC (ccRCC) accounts for 70% of all RCC cases. DNA methylation constitutes a main part of the molecular mechanism of cancer evolution and prognosis. In this study, we aim to identify differentially methylated genes related to ccRCC and their prognostic values.

Methods

The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between ccRCC tissues and paired tumor-free kidney tissues. DEGs were submitted to public databases for functional and pathway enrichment analysis, protein-protein interaction (PPI) analysis, promoter methylation analysis, and survival correlation analysis.

Results

In the setting of |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues were sorted out. The most enriched pathways were "T cell activation" and "cytokine-cytokine receptor interaction." After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM exhibited higher methylation levels, and BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (p < 0.001).

Conclusion

Our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be promising results for the prognosis of ccRCC.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

Identification of novel candidate genes and predicted miRNAs in atopic dermatitis patients by bioinformatic methods

Atopic dermatitis (AD) is a common, chronic inflammatory dermatosis with relapsing eruptions. Our study used bioinformatics to find novel candidate differentially expressed genes (DEGs) and predicted miRNAs between AD patients and healthy controls. The Mesh term "atopic dermatitis" was retrieved to obtain DEGs in GEO datasets. DEGs between AD patients and healthy controls were analyzed using GEO2R. Overlapping DEGs between different datasets were obtained with use of Draw Venn software. GO and KEGG enrichment analyses were conducted by the use of DAVID. STRING and miRWalk were used to individually analyze PPI networks, interactions of candidate genes and predicted miRNAs. A total of 571 skin samples, as retrieved from 9 databases were assessed. There were 225 overlapping DEGs between lesioned skin samples of AD patients and that of healthy controls. Nineteen nodes and 160 edges were found in the largest PPI cluster, consisting of 17 up-regulated and 2 down-regulated nodes. Two KEGG pathways were identified, including the cell cycle (CCNB1, CHEK1, BUB1B, MCM5) and p53 (CCNB1, CHEK1, GTSE1) pathways. There were 56 nodes and 100 edges obtained in the miRNA-target gene network, with has-miR-17-5p targeted to 4 genes and has-miR-106b-5p targeted to 3 genes. While these findings will require further verification as achieved with experiments involving in vivo and in vitro modles, these results provided some initial insights into dysfunctional inflammatory and immune responses associated with AD. Such information offers the potential to develop novel therapeutic targets for use in preventing and treating AD.

Identification of prognostic biomarkers in papillary renal cell carcinoma and PTTG1 may serve as a biomarker for predicting immunotherapy response

OBJECTIVE

This study aims to identify potential prognostic and therapeutic biomarkers in papillary renal cell carcinoma (pRCC).

METHODS

Two microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified. The protein-protein interaction (PPI) networks and functional annotations of DEGs were established. Survival analysis was utilized to evaluate the prognostic significance of the DEGs and the association between the expression level of candidate biomarkers and various tumour-infiltrating immune cells was explored. The role of PTTG1 in tumour microenvironments (TME) was further explored using Single-cell RNA-seq and its prognostic and therapeutic significance was validated in Fudan University Shanghai Cancer Centre (FUSCC) cohort.

RESULTS

Eight genes, including BUB1B, CCNB1, CCNB2, MAD2L1, TTK, CDC20, PTTG1, and MCM were found to be negatively associated with patients' prognosis. The expression level of PTTG1 was found to be significantly associated with lymphocytes, immunomodulators, and chemokine in the TCGA cohort. Single-cell RNA-seq information indicated that PTTG1 was strongly associated with the proliferation of T cells. In the FUSCC cohort, the expression level of PTTG1 was also statistically significant for both progression-free survival (PFS) and overall survival (OS) prediction (HR = 2.683, p < .001; HR = 2.673, p = .001). And higher expression level of PTTG1 was significantly associated with immune checkpoint blockade (ICB) response in the FUSCC cohort (χ²=3.99, p < .05).

CONCLUSIONS

Eight genes were identified as a prognostic biomarker and the expression level of PTTG1 was also found to serve as a potential predictor for ICB response in pRCC patients.Key messages:Eight genes, including BUB1B, CCNB1, CCNB2, MAD2L1, TTK, CDC20, PTTG1, and MCM were found to be negatively associated with pRCC patients' prognosis.Expression level of PTTG1 was significantly associated with tumour microenvironment including lymphocytes, immunomodulators, and chemokines.Higher expression level of PTTG1 was significantly associated with immune checkpoint blockade (ICB) response in FUSCC cohort.

Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach

BACKGROUND

The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.

METHODS

We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.

FINDINGS

First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

INTERPRETATION

These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.

FUNDING

This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.

Identification and Validation of a Ferroptosis-Related Long Non-Coding RNA (FRlncRNA) Signature to Predict Survival Outcomes and the Immune Microenvironment in Patients With Clear Cell Renal Cell Carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing worldwide, contributing to 70–85% of kidney cancer cases. Ferroptosis is a novel type of programmed cell death and could predict prognoses in cancers. Here, we developed a ferroptosis-related long non-coding RNA (FRlncRNA) signature to improve the prognostic prediction of ccRCC.

Methods: The transcriptome profiles of FRlncRNAs and clinical data of ccRCC were obtained from The Cancer Genome Atlas and ICGC databases. Patients were randomly assigned to training cohorts, testing cohorts, and overall cohorts. The FRlncRNA signature was constructed by Lasso regression and Cox regression analysis, and Kaplan–Meier (K-M) analysis was used to access the prognosis of each group. The accuracy of this signature was evaluated by the receiver operating characteristic (ROC) curve. The visualization of functional enrichment was carried out by the gene set enrichment analysis (GSEA). Internal and external datasets were performed to verify the FRlncRNA signature.

Results: A FRlncRNA signature comprising eight lncRNAs (AL590094.1, LINC00460, LINC00944, AC024060.1, HOXB-AS4, LINC01615, EPB41L4A-DT, and LINC01550) was identified. Patients were divided into low- and high-risk groups according to the median risk score, in which the high-risk group owned a dramatical shorter survival time than that of the low-risk group. Through ROC analysis, it was found that this signature had a greater predictive capability than traditional evaluation methods. The risk score was an independent risk factor for overall survival suggested by multivariate Cox analysis (HR = 1.065, 95%CI = 1.036–1.095, and p < 0.001). We constructed a clinically predictive nomogram based on this signature and its clinical features, which is of accurate prediction about the survival rate of patients. The GSEA showed that primary pathways were the P53 signaling pathway and tumor necrosis factor–mediated signaling pathway. The major FRlncRNAs (LINC00460, LINC00944, LINC01550, and EPB41L4A-DT) were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases. Their major target genes (BNIP3, RRM2, and GOT1) were closely related to the stage, grade, and survival outcomes of ccRCC by the validation of multiple databases. Additionally, we found two groups had a significant distinct pattern of immune function, immune checkpoint, and immune infiltration, which may lead to different survival benefits.

Conclusions: The FRlncRNA signature was accurate and act as reliable tools for predicting clinical outcomes and the immune microenvironment of patients with ccRCC, which may be molecular biomarkers and therapeutic targets.

Prognostic impact of Schlafen 11 in bladder cancer patients treated with platinum-based chemotherapy

The utility of Schlafen 11 (SLFN11) expression as a predictive biomarker for platinum‐based chemotherapy has been established for cancers from different histologies. However, the therapeutic relevance of SLFN11 in bladder cancer (BC) is unknown. Here, we examined the clinicopathologic significance of SLFN11 expression across 120 BC cases by immunohistochemistry. We divided the cases into two cohorts, one including 50 patients who received adjuvant or neoadjuvant platinum‐based chemotherapy, and the other including 70 BC patients treated by surgical resection without chemotherapy. In the cohort of 50 BC cases treated with platinum‐based chemotherapy, the SLFN11‐positive group (n = 25) showed significantly better overall survival than the SLFN11‐negative group (n = 25, P = .012). Schlafen 11 expression correlated significantly with the expression of luminal subtype marker GATA3. Multivariate analyses identified SLFN11 expression as an independent prognostic predictor (odds ratio, 0.32; 95% confidence interval, 0.11‐0.91; P = .033). Conversely, in the cohort of 70 BC cases not receiving platinum‐based chemotherapy, the SLFN11‐positive group (n = 29) showed significantly worse overall survival than the SLFN11‐negative group (n = 41, P = .034). In vitro analyses using multiple BC cell lines confirmed that SLFN11 KO rendered cells resistant to cisplatin. The epigenetic modifying drugs 5‐azacytidine and entinostat restored SLFN11 expression and resensitized cells to cisplatin and carboplatin in SLFN11‐negative BC cell lines. We conclude that SLFN11 is a predictive biomarker for BC patients who undergo platinum‐based chemotherapy and that the combination of epigenetic modifiers could rescue refractory BC patients to platinum derivatives by reactivating SLFN11 expression.

Identification of a Prognostic Model Based on 2-Gene Signature and Analysis of Corresponding Tumor Microenvironment in Alcohol-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with the poor prognosis. Nowadays, alcohol is becoming a leading risk factor of HCC in many countries. In our study, we obtained the DEGs in alcohol-related HCC through two databases (TCGA and GEO). Subsequently, we performed enrichment analyses (GO and KEGG), constructed the PPI network and screened the 53 hub genes by Cytoscape. Two genes (BUB1B and CENPF) from hub genes was screened by LASSO and Cox regression analyses to construct the prognostic model. Then, we found that the high risk group had the worse prognosis and verified the clinical value of the risk score in alcohol-related HCC. Finally, we analyzed the tumor microenvironment between high and low risk groups through CIBERSORT and ESTIMATE. In summary, we constructed the two-gene prognostic model that could predict the poor prognosis in patients with alcohol-related HCC.

PD-1/PD-L1 inhibitors-based treatment for advanced renal cell carcinoma: Mechanisms affecting efficacy and combination therapies

With the widespread use of PD-1/PD-L1 monoclonal antibodies (mAbs) in the treatment of multiple malignant tumors, they were also gradually applied to advanced renal cell carcinoma (aRCC). Nowadays, multiple PD-1/PD-L1 mAbs, such as nivolumab, avelumab, and pembrolizumab, have achieved considerable efficacy in clinical trials. However, due to the primary, adaptive, and acquired resistance to these mAbs, the efficacy of this immunotherapy is not satisfactory. Theories also vary as to why the difference in efficacy occurs. The alterations of PD-L1 expression and the interference of cellular immunity may affect the efficacy. These mechanisms demand to be revealed to achieve a sustained and complete objective response in patients with aRCC. Tyrosine kinase inhibitors have been proven to have synergistic mechanisms with PD-1/PD-L1 mAb in the treatment of aRCC, and CTLA-4 mAb has been shown to have a non-redundant effect with PD-1/PD-L1 mAb to enhance efficacy. Although combinations with targeted agents or other checkpoint mAbs have yielded enhanced clinical outcomes in multiple clinical trials nowadays, the potential of PD-1/PD-L1 mAbs still has a large development space. More potential mechanisms that affect the efficacy demand to be developed and transformed into the clinical treatment of aRCC to search for possible combination regimens. We elucidate these mechanisms in RCC and present existing combination therapies applied in clinical trials. This may help physicians' select treatment options for patients with refractory kidney cancer.