Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma
AJTR Copyright © 2023..
OBJECTIVES: In this study, we aimed to identify the metabolic genes associated with the metastasis and immunotherapy resistance of oral squamous cell carcinoma (OSCC) and to construct a metabolic gene-related predictive model for the prognosis of OSCC.
METHODS: RNA-seq data were download from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the modules related to EMT, stemness, and checkpoint signatures in OSCC. Univariate Cox and the least absolute shrinkage and selection operator (LASSO) methods were used to construct the metabolic gene signature. Furthermore, the scRNA-seq data were obtained from Gene Expression Omnibus (GEO) database and analyzed using "Seurat" and "CopyKAT" packages.
RESULTS: The risk prediction model was constructed using the 12 metabolic-related gene signature. Based on this model, risk score of each sample was calculated and used to divide the samples into low- and high-risk groups. Our model was effective as the risk score was significantly associated with clinical features and genetic mutations. Meanwhile, we found that lipid metabolism, glycolysis, amino acid metabolism, and drug metabolism differed between high- and low-risk groups. Pathways associated with malignant tumor and immunosuppression were enriched in high-risk group. Furthermore, low-risk group showed a more activated immune status and was predicted to have better response to immunotherapy. Finally, through single-cell transcriptome analysis, we assessed the expression of these 12 genes in tumor and non-tumor cells and verified the existence of two clusters of tumor cells with different degrees of malignancy at the cellular level.
CONCLUSIONS: Our study demonstrates the clinical significance of metabolic related gene signature for the treatment of OSCC and suggests potential therapeutic targets and pathways for OSCC.
| Medienart: |
Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
|---|---|
| Enthalten in: |
American journal of translational research - 15(2023), 1 vom: 03., Seite 373-391 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Haoran [VerfasserIn] |
|---|
| Themen: |
Immunotherapy resistance |
|---|
| Anmerkungen: |
Date Revised 14.02.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| Förderinstitution / Projekttitel: |
|
|---|
| PPN (Katalog-ID): |
NLM352872624 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM352872624 | ||
| 003 | DE-627 | ||
| 005 | 20231226054721.0 | ||
| 007 | tu | ||
| 008 | 231226s2023 xx ||||| 00| ||eng c | ||
| 028 | 5 | 2 | |a pubmed24n1176.xml |
| 035 | |a (DE-627)NLM352872624 | ||
| 035 | |a (NLM)36777871 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Haoran |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a Date Revised 14.02.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a AJTR Copyright © 2023. | ||
| 520 | |a OBJECTIVES: In this study, we aimed to identify the metabolic genes associated with the metastasis and immunotherapy resistance of oral squamous cell carcinoma (OSCC) and to construct a metabolic gene-related predictive model for the prognosis of OSCC | ||
| 520 | |a METHODS: RNA-seq data were download from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the modules related to EMT, stemness, and checkpoint signatures in OSCC. Univariate Cox and the least absolute shrinkage and selection operator (LASSO) methods were used to construct the metabolic gene signature. Furthermore, the scRNA-seq data were obtained from Gene Expression Omnibus (GEO) database and analyzed using "Seurat" and "CopyKAT" packages | ||
| 520 | |a RESULTS: The risk prediction model was constructed using the 12 metabolic-related gene signature. Based on this model, risk score of each sample was calculated and used to divide the samples into low- and high-risk groups. Our model was effective as the risk score was significantly associated with clinical features and genetic mutations. Meanwhile, we found that lipid metabolism, glycolysis, amino acid metabolism, and drug metabolism differed between high- and low-risk groups. Pathways associated with malignant tumor and immunosuppression were enriched in high-risk group. Furthermore, low-risk group showed a more activated immune status and was predicted to have better response to immunotherapy. Finally, through single-cell transcriptome analysis, we assessed the expression of these 12 genes in tumor and non-tumor cells and verified the existence of two clusters of tumor cells with different degrees of malignancy at the cellular level | ||
| 520 | |a CONCLUSIONS: Our study demonstrates the clinical significance of metabolic related gene signature for the treatment of OSCC and suggests potential therapeutic targets and pathways for OSCC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Oral squamous cell carcinoma | |
| 650 | 4 | |a WGCNA | |
| 650 | 4 | |a immunotherapy resistance | |
| 650 | 4 | |a metabolism | |
| 650 | 4 | |a metastasis | |
| 700 | 1 | |a Liu, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Miao |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Sisi |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t American journal of translational research |d 2009 |g 15(2023), 1 vom: 03., Seite 373-391 |w (DE-627)NLM193229463 |x 1943-8141 |7 nnns |
| 773 | 1 | 8 | |g volume:15 |g year:2023 |g number:1 |g day:03 |g pages:373-391 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 15 |j 2023 |e 1 |b 03 |h 373-391 | ||