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pmid:37231440

Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment.

Zhifang Tang · Hanzhen Feng · Longjun Shu · Minzheng Guo · Baochuang Qi · Luqiao Pu · Hongxin Shi · Junxiao Ren · Chuan Li

BMC medical genomics · 2023 doi:10.1186/s12920-023-01553-4

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:37231440
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title
Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment.
doi
10.1186/s12920-023-01553-4
pmid
37231440
authors array · 9 items
[0]
Zhifang Tang
[1]
Hanzhen Feng
[2]
Longjun Shu
[3]
Minzheng Guo
[4]
Baochuang Qi
[5]
Luqiao Pu
[6]
Hongxin Shi
[7]
Junxiao Ren
[8]
Chuan Li
journal
BMC medical genomics
year
2023
abstract
OBJECTIVE: Dysregulated lipid metabolism enhances the development and advancement of many cancers, including osteosarcoma (OS); however, the underlying mechanisms are still largely unknown. Therefore, this investigation aimed to elucidate novel potential lipid metabolism-related long non-coding RNAs (lncRNAs) that regulate OS development and provide novel signatures for its prognosis and precise treatment. MATERIALS AND METHODS: The GEO datasets (GSE12865 and GSE16091) were downloaded and analyzed using R software packages. Immunohistochemistry (IHC) was used to evaluate protein levels in OS tissues while real-time qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability. RESULTS: Two lipid metabolism-associated lncRNAs (LM-lncRNAs), small nucleolar RNA host gene 17 (SNHG17) and LINC00837, were identified as efficient and independent prognostic indicators for OS. In addition, further experiments confirmed that SNHG17 and LINC00837 were significantly elevated in OS tissues and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically suppressed the viability of OS cells, whereas overexpression of the two lncRNAs promoted OS cell proliferation. Moreover, bioinformatics analysis was conducted to construct six novel SNHG17-microRNA-mRNA competing endogenous RNA (ceRNA) networks, and three lipid metabolism-associated genes (MIF, VDAC2, and CSNK2A2) were found to be abnormally upregulated in OS tissues, suggesting that they were potential effector genes of SNHG17. CONCLUSION: In summary, SNHG17 and LINC00837 were found to promote OS cell malignancy, suggesting their use as ideal biomarkers for OS prognosis and treatment.
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Differences in overall survival between the two groups were assessed by Kaplan-Meier curves and log-rank tests, and the predictive accuracy of the risk model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.
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sections[13] "Statistical analysis" paragraph 2
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Differences in overall survival between the two groups were assessed by Kaplan-Meier curves and log-rank tests, and the predictive accuracy of the risk model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.
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The gene expression dataset (GSE12865 [16]) was acquired from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/Geo/) of the National Center for Biotechnology Information (NCBI). GSE12865 included 12 pediatric OS samples and 2 healthy osteoblast cell samples from the GPL6244 platform (Affymetrix Human Gene 1.0 ST Array).
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sections[3] "Data acquisition" paragraph 2
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The gene expression dataset (GSE12865 [16]) was acquired from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/Geo/) of the National Center for Biotechnology Information (NCBI). GSE12865 included 12 pediatric OS samples and 2 healthy osteoblast cell samples from the GPL6244 platform (Affymetrix Human Gene 1.0 ST Array).
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Kaplan-Meier survival analysis showed that the high-risk group had the worst clinical outcomes (Fig. 2D), and ROC curves indicated significant accuracy of the pooled LM-lncRNA signatures in the training cohort in the prediction of overall survival (AUC = > 0.65) (Fig. 2E).
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sections[16] "Identification of prognostic lipid metabolism-related lncRNAs and risk model construction" paragraph 2
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Kaplan-Meier survival analysis showed that the high-risk group had the worst clinical outcomes (Fig. 2D), and ROC curves indicated significant accuracy of the pooled LM-lncRNA signatures in the training cohort in the prediction of overall survival (AUC = > 0.65) (Fig. 2E).
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Kaplan-Meier survival analysis showed that the high-risk group had the worst clinical outcomes (Fig. 2D), and ROC curves indicated significant accuracy of the pooled LM-lncRNA signatures in the training cohort in the prediction of overall survival (AUC = > 0.65) (Fig. 2E).
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qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability
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and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically supp
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ienced pathologists. Cell culture and vector transfection The human OS cell lines Saos2 and HOS and th
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d for the presence of mycoplasma. For vector transfection, overexpression and silencing vectors for SN
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) according to manufacturer’s protocols. The transfection efficiency was evaluated by RT-qPCR. Statist
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INC00837 were synthesized. OS cells were pre-cultured in 6-well plates overnight, and the vectors wer
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Differences in overall survival between the two groups were assessed by Kaplan-Meier curves and log-rank tests, and the predictive accuracy of the risk model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.
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Complete file
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Therefore, this investigation aimed to elucidate novel potential lipid metabolism-related long non-coding RNAs (lncRNAs) that regulate OS development and provide novel signatures for its prognosis and precise treatment.\nMATERIALS AND METHODS: The GEO datasets (GSE12865 and GSE16091) were downloaded and analyzed using R software packages. Immunohistochemistry (IHC) was used to evaluate protein levels in OS tissues while real-time qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability.\nRESULTS: Two lipid metabolism-associated lncRNAs (LM-lncRNAs), small nucleolar RNA host gene 17 (SNHG17) and LINC00837, were identified as efficient and independent prognostic indicators for OS. In addition, further experiments confirmed that SNHG17 and LINC00837 were significantly elevated in OS tissues and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically suppressed the viability of OS cells, whereas overexpression of the two lncRNAs promoted OS cell proliferation. Moreover, bioinformatics analysis was conducted to construct six novel SNHG17-microRNA-mRNA competing endogenous RNA (ceRNA) networks, and three lipid metabolism-associated genes (MIF, VDAC2, and CSNK2A2) were found to be abnormally upregulated in OS tissues, suggesting that they were potential effector genes of SNHG17.\nCONCLUSION: In summary, SNHG17 and LINC00837 were found to promote OS cell malignancy, suggesting their use as ideal biomarkers for OS prognosis and treatment.", "claims": [{"id": "claim-method", "statement": "Differences in overall survival between the two groups were assessed by Kaplan-Meier curves and log-rank tests, and the predictive accuracy of the risk model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37231440", "locator": "sections[13] \"Statistical analysis\" paragraph 2", "quote": "Differences in overall survival between the two groups were assessed by Kaplan-Meier curves and log-rank tests, and the predictive accuracy of the risk model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression dataset (GSE12865 [16]) was acquired from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/Geo/) of the National Center for Biotechnology Information (NCBI). 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