NDUFA4L2 is associated with clear cell renal cell carcinoma malignancy and is regulated by ELK1

Wang, Lei; Peng, Zhiqiang; Wang, Kaizhen; Qi, Yijun; Yang, Ying; Zhang, Yue; An, Xinyuan; Luo, Shudong; Zheng, Junfang

doi:10.7717/peerj.4065

Cited by 28 publications

(25 citation statements)

References 25 publications

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“…Furthermore, NDUFA4L2 overexpressed was reported to be associated with renal clear cell carcinoma malignancy. And its expression was regulated by ELK1 in renal clear cell carcinoma cells [ 25 ]. Combined with the above outcomes, we could suppose that SHMT2 and its co-expressed gene NDUFA4L2 may modulate the initiation and development of kidney cancer and influence its prognosis, which may serve as the potential targets for kidney cancer treatment.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the clinical significance of SHMT2 and its co-expressed gene in human kidney cancer

Wang

Chong

et al. 2020

Biol Res

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Background Kidney cancer is one of the most common cancers in the world. It is necessary to clarify its underlying mechanism and find its prognostic biomarkers. Current studies showed that SHMT2 may be participated in several kinds of cancer. Methods Our studies investigated the expression of SHMT2 in kidney cancer by Oncomine, Human Protein Atlas database and ULCAN database. Meanwhile, we found its co-expression gene by cBioPortal online tool and validated their relationship in A498 and ACHN cells by cell transfection, western blot and qRT-PCR. Besides these, we also explored their prognostic values via the Kaplan–Meier plotter database in different types of kidney cancer patients. Results SHMT2 was found to be increased in 7 kidney cancer datasets, compared to normal renal tissues. For the cancer stages, ages and races, there existed significant difference in the expression of SHMT2 among different groups by mining of the UALCAN database. High SHMT2 expression is associated with poor overall survival in patients with kidney cancer. Among all co-expressed genes, NDUFA4L2 and SHMT2 had a high co-expression efficient. SHMT2 overexpression led to the increased expression of NDUFA4L2 at both mRNA and protein levels. Like SHMT2, overexpressed NDUFA4L2 also was associated with worse overall survival in patients with kidney cancer. Conclusion Based on above results, overexpressed SHMT2 and its co-expressed gene NDUFA4L2 were all correlated with the prognosis in kidney cancer. The present study might be benefit for better understanding the clinical significance of SHMT2 and provided a potential therapeutic target for kidney cancer in future.

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Section: Discussionmentioning

confidence: 99%

Evaluating the clinical significance of SHMT2 and its co-expressed gene in human kidney cancer

Wang

Chong

et al. 2020

Biol Res

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“…Moreover, KRAS or ELK1 overexpression inhibited the sensitivity enhancement of HeLa CR Cells to cisplatin which was induced by tanshinone I. ELK1 is one member of the ETS oncogene family of transcription activators in different human tumors, and many studies showed that downregulated ELK1 expression has antitumor activities against various type of cancer. For instance, Wang et al reported that elevated expression of NDUFA4L2 had positive correlation with clear cell renal cell carcinoma malignancy and was modulated by ELK1 . Zhang et al demonstrated that miR‐326 had suppressive activities on cervical cancer via targeting ELK1 .…”

Section: Discussionmentioning

confidence: 99%

Tanshinone I attenuates proliferation and chemoresistance of cervical cancer in a KRAS‐dependent manner

Dun

Gao

2018

J Biochem & Molecular Tox

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Chemoresistance is a common occurrence during advanced or recurrent cervical cancer therapy when treated by conventional treatment, platinum‐based chemotherapy. This study aimed to investigate the effect and underlying mechanism of tanshinone I on attenuating proliferation and chemoresistance of cervical cancer cells. In cervical cancer cells, cell proliferation was examined by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT), cell count, and soft‐agar colony‐formation assay. rVista analysis and luciferase reporter assay were used to explore the upstream regulator of KRAS, and the expression levels of key genes were also detected. Western blot analysis showed that tanshinone I significantly suppressed KRAS expression and inhibited AKT phosphorylation. rVista analysis and luciferase reporter assay demonstrated that ELK1 can binds directly to KRAS promoter and positively regulates KRAS expression. MTT assay showed that KRAS or ELK1 overexpression significantly attenuated the suppressive effects of tanshinone I on HeLa cells proliferation. In addition, tanshinone I recovered the cisplatin sensitivity of HeLa CR cells, whereas KRAS or ELK1 overexpression significantly inhibited this phenomenon. Our results suggested that tanshinone I had anticancer effects on cervical cancer cells via inhibiting ELK1 and downregulating KRAS‐AKT axis, which subsequently suppressed the proliferation and cisplatin resistance of cervical cancer cells.

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“…As mentioned earlier, eight of the top 10 genes in the differential gene network of KIRC except C3 and C4A have been reported to be implicated in renal cell carcinoma (RCC). Specifically, NDUFA4L2 is overexpressed in clear cell RCC (ccRCC); its mRNA level is correlated with tumor stage and overall survival time [50, 51]; and the association of NDUFA4L2 with ccRCC is regulated by ELK1 [52]. UMOD expression is downregulated in RCC [53, 54], and the homozygous genotype of UMOD is associated with more aggressive RCC [55].…”

Section: Discussionmentioning

confidence: 99%

Efficient proximal gradient algorithm for inference of differential gene networks

Wang¹,

Gao²,

Giannakis³

et al. 2019

BMC Bioinformatics

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Background Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. Results In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1 . Conclusion With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions. Electronic supplementary material The online version of this article (10.1186/s12859-019-2749-x) contains supplementary material, which is available to authorized users.

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NDUFA4L2 is associated with clear cell renal cell carcinoma malignancy and is regulated by ELK1

Cited by 28 publications

References 25 publications

Evaluating the clinical significance of SHMT2 and its co-expressed gene in human kidney cancer

Evaluating the clinical significance of SHMT2 and its co-expressed gene in human kidney cancer

Tanshinone I attenuates proliferation and chemoresistance of cervical cancer in a KRAS‐dependent manner

Efficient proximal gradient algorithm for inference of differential gene networks

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