A novel prognostic marker of non-small cell lung cancer: chromosome 9 open reading frame 86 (C9orf86)

Yoshimura, Katsuhiro; Osman, Murat; Inoue, Yusuke; Suda, Takafumi; Sugimura, Haruhiko

doi:10.21037/jtd.2016.08.38

Cited by 11 publications

(11 citation statements)

References 16 publications

(21 reference statements)

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“…The CDC20 gene is an essential component of cell division, and the high CDC20 expression is reported to be associated with the poor survival of breast cancer patients ( Jiang et al , 2011 ; Karra et al , 2014 ). Overexpression of the C9orf86 gene, also known as RBEL1, is correlated with the survival of breast cancer patients ( Li et al , 2013 ; Yoshimura et al , 2016 ). The MAMDC2 gene, whose function is unknown, is reported to be highly correlated with the disease-free survival of breast cancer patients ( Mannelqvist et al , 2014 ; Meng et al , 2016 ).…”

Section: Resultsmentioning

confidence: 99%

Improved survival analysis by learning shared genomic information from pan-cancer data

Kim

Choe

et al. 2020

Bioinformatics

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Motivation Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning. Results We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples. Availability and implementation Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Improved survival analysis by learning shared genomic information from pan-cancer data

Kim

Choe

et al. 2020

Bioinformatics

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“…Several studies found elevated RABL6 expression in various human cancers, including pancreatic ductal adenocarcinomas, pancreatic neuroendocrine tumors and breast tumors. And RABL6 overexpression was associated with poor survival in those cancers [7][8][9]. However, its role in ESCC is yet to be discovered.…”

Section: Discussionmentioning

confidence: 99%

“…The GTPases have important effect in various cellular functions, such as cell proliferation, differentiation, survival, and so on [5]. Recently, non-small cell lung cancer (NSCLC), breast cancer and pancreatic ductal adenocarcinoma have been showed to overexpress RABL6, and that was closely correlated with poor prognosis [6][7][8][9]. In breast cancer cell lines SK-BR-3 and MCF-7, silencing of RABL6 by siRNA suppressed cell proliferation and invasion capabilities in vitro [7].…”

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confidence: 99%

High expression of RABL6 promotes cell proliferation and predicts poor prognosis in esophageal squamous cell carcinoma

et al. 2020

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Background: Esophageal squamous cell carcinoma (ESCC) is a common malignant carcinoma of digestive system with high mortality. RAB, member RAS oncogene family like 6 (RABL6), a member of the RAS subfamily, has been reported as an important molecule in several cancers. However, its potential role in ESCC still remains unclear. Methods: RABL6 mRNA expression was detected in 93 frozen ESCC samples using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Immunohistochemistry was applied to evaluate the RABL6 expression in tissue microarray containing 171 pairs of ESCC tissues and paired para-cancerous tissues. We evaluated RABL6 expression and its correlation with clinicopathological characteristics and survival. Subsequently, the impact of RABL6 knockdown on the ability of cell proliferation, apoptosis, migration and epithelial-mesenchymal transition (EMT) of ESCC cells was investigated by MTS, Focus formation, flow cytometry, Transwell assays, qRT-PCR, western blot, inverted microscope observation and phalloidin staining, respectively. Results: Compared to paired para-cancerous tissues, RABL6 was highly expressed in ESCC. The RABL6 highexpression was associated with worse prognosis. We also revealed silencing of RABL6 caused inhibition of cell proliferation, invasion and migration. Further experiments demonstrated that knockdown of RABL6 suppressed the aggressive biological activities of ESCC by suppressing EMT in ESCC cells. Conclusions: RABL6 functions as a tumor oncogene in ESCC. It would be a potential biomarker predicting prognosis, and a novelty target for ESCC therapy.

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“…Other ways of activating MDM2 besides genetic amplification also exist [ 33 , 34 ], including upregulation at the protein level by a novel RAB-like GTPase called RABL6A. RABL6A is a newly recognized oncoprotein that has been implicated in various human cancers, including pancreatic neuroendocrine tumors [ 35 , 36 ], breast cancer [ 37 , 38 ], colon cancer [ 37 ], non-small cell lung cancer [ 39 , 40 ], pancreatic ductal adenocarcinomas [ 41 ] and osteosarcoma [ 42 ]. In pancreatic neuroendocrine tumors, RABL6A activates Akt signaling through PP2A inactivation and drives G1 to S phase progression via inactivation of the retinoblastoma (RB1) tumor suppressor [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic and therapeutic value of the Hippo pathway, RABL6A, and p53-MDM2 axes in sarcomas

et al. 2021

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Additional prognostic and therapeutic biomarkers effective across different histological types of sarcoma are needed. Herein we evaluate expression of TAZ and YAP, the p53-MDM2 axis, and RABL6A, a novel oncoprotein with potential ties to both pathways, in sarcomas of different histological types. Immunohistochemical staining of a tissue microarray including 163 sarcomas and correlation with clinical data showed that elevated YAP and TAZ independently predict worse overall and progression-free survival, respectively. In the absence of p53 expression, combined TAZ and YAP expression adversely affect overall, progression free, and metastasis free survival more than TAZ or YAP activation alone. RABL6A independently predicted shorter time to metastasis and was positively correlated with p53, MDM2 and YAP expression, supporting a possible functional relationship between the biomarkers. Network analysis further showed that TAZ is positively correlated with MDM2 expression. The data implicate all five proteins as clinically relevant downstream players in the Hippo pathway. Finally, a novel inhibitor of MDM2 (MA242), effectively suppressed the survival of sarcoma cell lines from different histological types regardless of p53 status. These findings suggest both independent and cooperative roles for all five biomarkers across different histological types of sarcoma in predicting patient outcomes and potentially guiding future therapeutic approaches.

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A novel prognostic marker of non-small cell lung cancer: chromosome 9 open reading frame 86 (C9orf86)

Cited by 11 publications

References 16 publications

Improved survival analysis by learning shared genomic information from pan-cancer data

Improved survival analysis by learning shared genomic information from pan-cancer data

High expression of RABL6 promotes cell proliferation and predicts poor prognosis in esophageal squamous cell carcinoma

Prognostic and therapeutic value of the Hippo pathway, RABL6A, and p53-MDM2 axes in sarcomas

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