Significance of SALL4 as a drug-resistant factor in lung cancer

Yanagihara, Nozomi; Kobayashi, Daisuke; Kuribayashi, Kageaki; Tanaka, Maki; Hasegawa, Tadashi; Watanabe, Naoki

doi:10.3892/ijo.2015.2866

Cited by 26 publications

(28 citation statements)

References 22 publications

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“…For example, the shRNA knockdown of CHI3L1, a gene identified for etoposide and cisplatin response in every tissue, has been shown to sensitize glioma cells to these two drugs, while its overexpression reduced their sensitivity [41]. As another example, the knockdown of SALL4 (identified in all tissues) in cancer cell lines has been shown to increase the sensitivity of lung cancer cells [42] and esophageal squamous cell carcinoma cells [43] to cisplatin. Supplementary Table S7 summarizes some of the evidence we curated from literature for the role of different genes identified by TG-LASSO in all tissue types for cisplatin, as an illustration.…”

Section: Resultsmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Huang

Bhope

Lim

et al. 2019

Preprint

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1Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and 2 molecular profiles obtained prior to administration of the drug, can play a significant role 3 in individualized medicine. Machine learning models have the potential to address this 4 issue, but training them requires data from a large number of patients treated with each 5 drug, limiting their feasibility. While large databases of drug response and molecular 6 profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear 7 whether preclinical samples can be used to predict CDR of real patients. 8 9We designed a systematic approach to evaluate how well different algorithms, trained on 10 gene expression and drug response of CCLs, can predict CDR of patients. Using data from 11 two large databases, we evaluated various linear and non-linear algorithms, some of 12 which utilized information on gene interactions. Then, we developed a new algorithm 13 called TG-LASSO that explicitly integrates information on samples' tissue of origin with 14 gene expression profiles to improve prediction performance. Our results showed that 15 regularized regression methods provide significantly accurate prediction. However, 16including the network information or common methods of including information on the 17 tissue of origin did not improve the results. On the other hand, TG-LASSO improved the 18 predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. 19Additionally, TG-LASSO identified genes associated with the drug response, including 20 known targets and pathways involved in the drugs' mechanism of action. Moreover, 21 utilizes a new approach for explicitly incorporating the tissue of origin of samples in the 43 prediction task. Our results show that TG-LASSO outperforms all other algorithms and can 44 accurately distinguish resistant and sensitive patients for the majority of the tested drugs. 45Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity 46 in each cancer type. 47

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Section: Resultsmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Huang

Bhope

Lim

et al. 2019

Preprint

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“…13,16 It has been reported that SALL4 has a functional role in drug resistance in aggressive endometrial cancer. 10 Yanagihara et al 34 also showed that in lung cancer pretherapy SALL4 expression is significantly higher in patients with recurrence than those in remission. However, based on a previously published 30% cut-off value, 35 we found that SALL4 expression in HB did not correlate statistically with percentage of viable tumour after chemotherapy.…”

Section: Discussionmentioning

confidence: 96%

“…It has been reported that SALL4 has a functional role in drug resistance in aggressive endometrial cancer . Yanagihara et al . also showed that in lung cancer pretherapy SALL4 expression is significantly higher in patients with recurrence than those in remission.…”

Section: Discussionmentioning

confidence: 97%

The diagnostic and prognostic value of SALL4 in hepatoblastoma

et al. 2016

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“…In addition to hematopoietic malignancies, SALL4 is also found to be upregulated in solid tumours including liver cancer, 13, 14 colon cancer, 15, 16 breast cancer, 17, 18 endometrial cancer, 19, 20 lung cancer 21, 22 and glioma. 23 The recent studies also suggest an oncogenic role of SALL4 in solid tumours.…”

Section: Introductionmentioning

confidence: 99%

SALL4 promotes gastric cancer progression through activating CD44 expression

Yuan

Zhang

et al. 2016

Oncogenesis

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The stem cell factor SALL4 (Sal-like protein 4) plays important roles in the development and progression of cancer. SALL4 is critically involved in tumour growth, metastasis and therapy resistance. However, the underlying mechanisms responsible for the oncogenic roles of SALL4 have not been well characterized. In this study, we demonstrated that SALL4 knockdown by short hairpin RNA greatly inhibited the proliferation, migration and invasion of gastric cancer cells. We further confirmed the inhibitory effects of SALL4 knockdown on gastric cancer cells by using a tetracycline-inducible system. Mechanistically, SALL4 knockdown downregulated the expression of CD44. The results of luciferase assay and chromatin immunoprecipitation study showed that SALL4 bound to CD44 promoter region and transcriptionally activated CD44. The results of rescue study revealed that CD44 overexpression antagonized SALL4 knockdown-mediated inhibition of gastric cancer cell proliferation, migration, and invasion in vitro and gastric cancer growth in vivo. Collectively, our findings indicate that SALL4 promotes gastric cancer progression through directly activating CD44 expression, which suggests a novel mechanism for the oncogenic roles of SALL4 in gastric cancer and represents a new target for gastric cancer therapy.

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Significance of SALL4 as a drug-resistant factor in lung cancer

Cited by 26 publications

References 22 publications

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

The diagnostic and prognostic value of SALL4 in hepatoblastoma

SALL4 promotes gastric cancer progression through activating CD44 expression

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