ARL4C is associated with initiation and progression of lung adenocarcinoma and represents a therapeutic target

Kimura, Kenji; Matsumoto, Shinji; Harada, Taishi; Morii, Eiichi; Nagatomo, Izumi; Shintani, Yasushi; Kikuchi, Akira

doi:10.1111/cas.14303

Cited by 29 publications

(43 citation statements)

References 41 publications

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“…In addition, evidence has shown that the identification of molecular biomarkers can provide prognostic value for LUADs ( Wang et al, 2019 ; Zhang, Zhang & Yu, 2019 ). For example, high ARL4C expression is an essential prognostic factor in LUAD ( Kimura et al, 2020 ). However, a single gene biomarker is insufficient to produce comprehensive predictive effects and immune prognostic models were proposed for LUADs recently ( Luo et al, 2020 ; Yang et al, 2019 ; Yue, Ma & Zhou, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

Yang

Hao

Cui

et al. 2021

PeerJ

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Background The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD. Methods With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset. Results The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival. Conclusion We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

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Section: Introductionmentioning

confidence: 99%

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

Yang

Hao

Cui

et al. 2021

PeerJ

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“…positive for ARL4C and additionally another biomarker, identi ed patients likely to be dead within 5 years. These results indicate that ARL4C may contributes to more aggressive behavior of the tumor including a possibility of metastasis, as demonstrated that high expression of ARL4C in lung, liver, and colorectal cancers was an independent indicator of relapse [23,24]. The other three biomarkers may promote the progression of metastatic tumors.…”

Section: Discussionmentioning

confidence: 54%

A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma.

Kubota

Yoshida

Kageyama

et al. 2020

Preprint

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Background: Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2 and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods: To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated.Results: Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions: Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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“…Conversely, by setting the cut-off point to 11, the high-(> 11) scored group, i.e., positive for ARL4C and additionally another biomarker, identified patients likely to be dead within 5 years. These results indicate that ARL4C may contributes to more aggressive behavior of the tumor including a possibility of metastasis, as demonstrated that high expression of ARL4C in lung, liver, and colorectal cancers was an independent indicator of relapse [ 23 , 24 ]. The other three biomarkers may promote the progression of metastatic tumors.…”

Section: Discussionmentioning

confidence: 89%

“…Shorter survival from the time of diagnosis of metastasis is seen in cases with higher expression levels of ARL4C (log-rank test, a p = 0.006, chi-square value = 7.53), SOD2 (log-rank test, c p = 0.011, chi-square value = 6.54), and STEAP3 (log-rank test, d p = 0.003, chi-square value = 8.95). e The patients were divided into high-and low-risk scored groups under our model of risk stratification by setting the cut-off value to 17 points; the high-(> 17) risk score predicted likely demise within 2 years more accurately than each individual biomarker (log-rank test, e p < 0.001, chi-square value = 21.66) Compared to the highest-risk scored group, high SOD2 expression could accurately predict those patients likely to be dead within 30 months of initiation of MTT a possibility of metastasis, as demonstrated that high expression of ARL4C in lung, liver, and colorectal cancers was an independent indicator of relapse [23,24]. The other three biomarkers may promote the progression of metastatic tumors.…”

Section: Discussionmentioning

confidence: 97%

A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

et al. 2020

View full text Add to dashboard Cite

Background Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. Results Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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ARL4C is associated with initiation and progression of lung adenocarcinoma and represents a therapeutic target

Cited by 29 publications

References 41 publications

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma.

A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

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