Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

Targeting MELK in tumor cells and tumor microenvironment: from function and mechanism to therapeutic application

Maternal embryonic leucine zipper kinase (MELK), a member of the adenosine monophosphate-activated protein kinase (AMPK) protein family, has been reported to be involved in the regulation of many cellular events. The aberrant expression of MELK is associated with tumorigenesis and malignant progression of various tumors. Moreover, MELK plays an essential role in the regulation of tumor microenvironment (TME), which affects the function of immune cells and the responsiveness to immunotherapy. Currently, small molecule inhibitors targeting MELK have been developed and evaluated in clinical trials. A comprehensive understanding of MELK may provide clues and confidence for subsequent basic research and scientific transformation. In this review, we provide a comprehensive overview of the structural features, molecular biological functions, and critical roles of MELK in tumors and TME, as well as the targeted agents under development for the treatment of tumors and discuss the perspective for MELK-targeted therapies for tumors.

AURKB affects the proliferation of clear cell renal cell carcinoma by regulating fatty acid metabolism

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer with a high metastatic rate and high mortality rate. The molecular mechanism of ccRCC development, however, needs further study. Aurora kinase B (AURKB) functions as an important oncogene in various tumors; therefore, in the present study, we aimed to explore the mechanism by which AURKB affects ccRCC development.

METHODS

We performed bioinformatics analysis, CCK-8 assay, RNA sequencing, RT-PCR and Western blot to analyze the function and mechanism of AURKB in ccRCC.

RESULTS

TIMER2.0 showed that AURKB was overexpressed in Kidney Renal Clear Cell Carcinoma (KIRC), the UALCAN database showed the survival rate of KIRC patients with different expression levels of AURKB and different gender indicated in the same gender, high AURKB expression predicts lower survival rate. Silencing of AURKB expression inhibits the proliferation of ccRCC cells. RNA-seq data suggested that AURKB is involved in fatty acid metabolism. Silencing of AURKB inhibited the expression of fatty acid synthase (FASN). FASN is a key gene involved in fatty acid metabolism. TIMER2.0 showed that FASN is upregulated in KIRC. Silencing of FASN inhibited the proliferation of ccRCC cells.

CONCLUSIONS

AURKB induces the proliferation of ccRCC cells by regulating fatty acid metabolism.

Abnormal expression of FOXM1 in carcinogenesis of renal cell carcinoma: From experimental findings to clinical applications

Renal cell carcinoma (RCC) is a prevalent malignancy within the genitourinary system. At present, patients with high-grade or advanced RCC continue to have a bleak prognosis. Mounting research have emphasized the significant involvement of Forkhead box M1 (FOXM1) in RCC development and progression. Therefore, it is imperative to consolidate the existing evidence regarding the contributions of FOXM1 to RCC tumorigenesis through a comprehensive review. This study elucidated the essential functions of FOXM1 in promoting RCC growth, invasion, and metastasis by regulating cell cycle progression, DNA repair, angiogenesis, and epithelial-mesenchymal transition (EMT). Also, FOXM1 might serve as a novel diagnostic and prognostic biomarker as well as a therapeutic target for RCC. Clinical findings demonstrated that the expression of FOXM1 was markedly upregulated in RCC samples, while a high level of FOXM1 was found to be associated with a poor overall survival rate of RCC. Furthermore, it is worth noting that FOXM1 may have a significant impact on the resistance of renal cell carcinoma (RCC) to radiotherapy. This observation suggests that inhibiting FOXM1 could be a promising strategy to impede the progression of RCC and enhance its sensitivity to radiotherapy. The present review highlighted the pivotal role of FOXM1 in RCC development. FOXM1 has the capacity to emerge as not only a valuable diagnostic and prognostic tool but also a viable therapeutic option for unresectable RCC.

Maternal embryonic leucine zipper kinase in tumor cell and tumor microenvironment: Emerging player and promising therapeutic opportunities

Maternal embryonic leucine zipper kinase (MELK) is a member of the AMPK (AMP-activated protein kinase) protein family, which is widely and highly expressed in multiple cancer types. Through direct and indirect interactions with other proteins, it mediates various cascades of signal transduction processes and plays an important role in regulating tumor cell survival, growth, invasion and migration and other biological functions. Interestingly, MELK also plays an important role in the regulation of the tumor microenvironment, which can not only predict the responsiveness of immunotherapy, but also affect the function of immune cells to regulate tumor progression. In addition, more and more small molecule inhibitors have been developed for the target of MELK, which exert important anti-tumor effects and have achieved excellent results in a number of clinical trials. In this review, we outline the structural features, molecular biological functions, potential regulatory mechanisms and important roles of MELK in tumors and tumor microenvironment, as well as substances targeting MELK. Although many molecular mechanisms of MELK in the process of tumor regulation are still unknown, it is worth affirming that MELK is a potential tumor molecular therapeutic target, and its unique superiority and important role provide clues and confidence for subsequent basic research and scientific transformation.

Identification of fatty acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in renal clear cell carcinoma

Three subtypes of samples were generated based on genes involved in fatty acid metabolism in The Cancer Genome Atlas (TCGA)-RCC patients using a non-negative matrix factorization (NMF) algorithm. 32 co-expressed modules were identified using WCGNA. We constructed a four-gene signature in our training set using least absolute shrinkage selection operator regression analysis and verified it in our testing and overall sets. A relevant study analysis in clinical trials was conducted, which showed the model had good stability and potential application value for predicting outcomes. We analyzed the immune microenvironment using MCPcounter, CIBERSORT, quanTIseq, TIMER and ESTIMATE algorithms, and the result indicated risk was positively related to T cells, B-lineage, and fibroblasts and negatively correlated with monocytic lineage, myeloid dendritic cells, neutrophils, and endothelial cells, and CPT1B was positively related to T cells, CD8 + T cells, Cytotoxic lymphocytes and NK cells, and negatively correlated with myeloid dendritic cells, fibroblasts, endothelial cells. Tumor mutation burden was positively related to risk score and the expression of CPT1B using the R packages corrplot, circlize. Through the R package pRRophetic, drug sensitivity tests showed that the low-risk score group would benefit more from sunitinib and less from pazopanib, sorafenib, temsirolimus, gemcitabine and doxorubicin than the high-risk score group. We performed the relevant basic assay validation for CPT1B, and the proliferation ability of RCC cells was inhibited after the knockdown of protein expression of CPT1B. In conclusion, we established a four-gene model that can predict outcomes of RCC with potential applications in diagnosis and treatment.

Comprehensive analysis of a homeobox family gene signature in clear cell renal cell carcinoma with regard to prognosis and immune significance

The homeobox (HOX) family genes have been linked to multiple types of tumors, while their effect on malignant behaviors of clear cell renal cell carcinoma (ccRCC) and clinical significance remains largely unknown. Here, we comprehensively analyzed the expression profiles and prognostic value of HOX genes in ccRCC using datasets from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We developed a prognostic signature comprising eight HOX genes (HOXB1, HOXA7, HOXB5, HOXD8, HOXD9, HOXB9, HOXA9, and HOXA11) for overall survival prediction in ccRCC and it allowed patients to be subdivided into high- and low-risk groups. Kaplan-Meier survival analysis in all the internal and external cohorts revealed significant difference in clinical outcome of patients in different risk groups, indicating the satisfactory predictive power of the signature. Additionally, we constructed a prognostic nomogram by integrating signature-derived risk score and clinical factors such as gender, age, T and M status, which might be helpful for clinical decision-making and designing tailored management schedules. Immunological analysis revealed that the regulatory T cells (Tregs) infiltrated differently between the two subgroups in both TCGA and ICGC cohorts. ssGSEA method showed that the enrichment scores for mast cells were significantly lower in high-risk group compared with the low-risk group, which was consistent in both TCGA and ICGC cohorts. As for the related immune function, the enrichment scores of APC co-inhibition, para-inflammation, and type II IFN response were consistently lower in high-risk group in both cohorts. Of the eight HOX genes, the mRNA and protein levels of HOXD8 were downregulated in ccRCC than that in normal tissues, and decreased expression of HOXD8 was associated with increased tumor grade and stage, and lymph node metastasis. Survival analysis revealed that lower expression of HOXD8 predicted worse overall survival in ccRCC. In conclusion, our HOX gene-based signature was a favorable indicator to predict the prognosis of ccRCC cases and associated with immune cell infiltration. HOXD8 might be a tumor suppressor gene in ccRCC and a potential predictor of tumor progression.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Oncogenic role and potential regulatory mechanism of topoisomerase IIα in a pan-cancer analysis

Topoisomerase IIα (TOP2A) plays an oncogenic role in multiple tumor types. However, no pan-cancer analysis about the function and the upstream molecular mechanism of TOP2A is available. For the first time, we analyzed potential oncogenic roles of TOP2A in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of TOP2A was existed in almost all cancer types, and related to poor prognosis and advanced pathological stages in most cases. Besides, the high frequency of TOP2A genetic alterations was observed in several cancer types, and related to prognosis in some cases. Moreover, we conduct upstream miRNAs and lncRNAs of TOP2A to establish ceRNA networks in kidney renal clear cell carcinoma (SNHG3-miR-139-5p), kidney renal papillary cell carcinoma (TMEM147-AS1/N4BP2L2-IT2/THUMPD3-AS1/ERICD/TTN-AS1/SH3BP5-AS1/THRB-IT1/SNHG3/NEAT1-miR-139-5p), liver hepatocellular carcinoma (SNHG3/THUMPD3-AS1/NUTM2B-AS1/NUTM2A-AS1-miR-139-5p and SNHG6/GSEC/SNHG1/SNHG14/LINC00265/MIR3142HG-miR-101-3p) and lung adenocarcinoma (TYMSOS/HELLPAR/SNHG1/GSEC/SNHG6-miR-101-3p). TOP2A expression was generally positively correlated with cancer associated fibroblasts, M0 and M1 macrophages in most cancer types. Furthermore, TOP2A was positively associated with expression of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT) in most cancer types. Our first TOP2A pan-cancer study contributes to understanding the prognostic roles, immunological roles and potential upstream molecular mechanism of TOP2A in different cancers.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.