Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients
Abstract Background Radiotherapy (RT) is an important means of local treatment of solid tumors, and radioresistance is the main reason for RT failure for tumors, especially pancreatic cancer (PC). It is urgent to distinguish key genes and mechanisms of radioresistance in PC. Methods We acquired the...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-024-13231-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846136962918907904 |
|---|---|
| author | Dandan Wang Jun Cao Yanhui Chen Lisha Zhang Chan Zhou Litao Huang Yanliang Chen |
| author_facet | Dandan Wang Jun Cao Yanhui Chen Lisha Zhang Chan Zhou Litao Huang Yanliang Chen |
| author_sort | Dandan Wang |
| collection | DOAJ |
| description | Abstract Background Radiotherapy (RT) is an important means of local treatment of solid tumors, and radioresistance is the main reason for RT failure for tumors, especially pancreatic cancer (PC). It is urgent to distinguish key genes and mechanisms of radioresistance in PC. Methods We acquired the data from The Cancer Genome Atlas (TCGA), obtained the gene modules associated with radioresistance by weighted gene coexpression network analysis (WGCNA), and identified differentially expressed genes (DEGs) between normal and tumor samples. Radioresistance-related genes (RRRGs) were determined with the intersection of WGCNA and DEGs. The hub RRRGs associated with prognosis were distinguished by the least absolute shrinkage and selection operator (LASSO) regression. We established a risk score model using multivariate Cox regression. Immune cell infiltration and drug sensitivity were evaluated through the CIBERSORT algorithm and the “OncoPredict” software package, respectively. The association of the key gene RIC3 and PC clinical features was verified in public databases, and its biological behaviors were explored in vitro. Results The intersection of DEGs and WGCNA confirmed 14 RRRGs, then six hub RRRGs were identified using LASSO. A key four genes (DUSP4, ADORA2B, SCGB2A1, and RIC3) risk score model was constructed and proved to be capable of independently estimating the prognosis of PC. There is no significant difference between risk score groups in various immune cell infiltration and response to immunotherapy. Although the low-risk group seemed to exhibit greater sensitivity to antitumor drugs, the four drugs (5-fluorouracil [5-FU], leucovorin, irinotecan, and oxaliplatin) currently used for PC patients had no statistical difference for the low- and high- group. The overexpression of RIC3 had a synergy effect with irradiation on inhibited malignant biological properties of PC cells, which was verified by detecting the proliferation ability, apoptosis rate, cell cycle distribution, and migration ability of PANC-1 cells. Conclusions We herein presented signature genes correlated with radioresistance in PC and established a risk score model competent in estimating patients’ clinical outcomes and response to antitumor drugs. The above evidence could contribute to comprehending the mechanisms of radioresistance and identifying the underlying therapy targeting. |
| format | Article |
| id | doaj-art-210d609a45e946c59c00f81e9ccdc703 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-210d609a45e946c59c00f81e9ccdc7032024-12-08T12:33:33ZengBMCBMC Cancer1471-24072024-12-0124111910.1186/s12885-024-13231-4Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patientsDandan Wang0Jun Cao1Yanhui Chen2Lisha Zhang3Chan Zhou4Litao Huang5Yanliang Chen6The First School of Clinical Medicine, Lanzhou UniversityDepartment of Outpatient, Taihe Hospital, Hubei University of MedicineDepartment of Neuroscience and Endocrinology, Tangshan Caofeidian District HospitalDepartment of Obstetrics, Tangshan Caofeidian District HospitalDepartment of Geriatrics, Taihe Hospital, Hubei University of MedicineDepartment of Clinical Research Management, West China Hospital of Sichuan UniversityThe First School of Clinical Medicine, Lanzhou UniversityAbstract Background Radiotherapy (RT) is an important means of local treatment of solid tumors, and radioresistance is the main reason for RT failure for tumors, especially pancreatic cancer (PC). It is urgent to distinguish key genes and mechanisms of radioresistance in PC. Methods We acquired the data from The Cancer Genome Atlas (TCGA), obtained the gene modules associated with radioresistance by weighted gene coexpression network analysis (WGCNA), and identified differentially expressed genes (DEGs) between normal and tumor samples. Radioresistance-related genes (RRRGs) were determined with the intersection of WGCNA and DEGs. The hub RRRGs associated with prognosis were distinguished by the least absolute shrinkage and selection operator (LASSO) regression. We established a risk score model using multivariate Cox regression. Immune cell infiltration and drug sensitivity were evaluated through the CIBERSORT algorithm and the “OncoPredict” software package, respectively. The association of the key gene RIC3 and PC clinical features was verified in public databases, and its biological behaviors were explored in vitro. Results The intersection of DEGs and WGCNA confirmed 14 RRRGs, then six hub RRRGs were identified using LASSO. A key four genes (DUSP4, ADORA2B, SCGB2A1, and RIC3) risk score model was constructed and proved to be capable of independently estimating the prognosis of PC. There is no significant difference between risk score groups in various immune cell infiltration and response to immunotherapy. Although the low-risk group seemed to exhibit greater sensitivity to antitumor drugs, the four drugs (5-fluorouracil [5-FU], leucovorin, irinotecan, and oxaliplatin) currently used for PC patients had no statistical difference for the low- and high- group. The overexpression of RIC3 had a synergy effect with irradiation on inhibited malignant biological properties of PC cells, which was verified by detecting the proliferation ability, apoptosis rate, cell cycle distribution, and migration ability of PANC-1 cells. Conclusions We herein presented signature genes correlated with radioresistance in PC and established a risk score model competent in estimating patients’ clinical outcomes and response to antitumor drugs. The above evidence could contribute to comprehending the mechanisms of radioresistance and identifying the underlying therapy targeting.https://doi.org/10.1186/s12885-024-13231-4Pancreatic cancerRadioresistanceGene signatureRisk score modelImmune landscape |
| spellingShingle | Dandan Wang Jun Cao Yanhui Chen Lisha Zhang Chan Zhou Litao Huang Yanliang Chen Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients BMC Cancer Pancreatic cancer Radioresistance Gene signature Risk score model Immune landscape |
| title | Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| title_full | Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| title_fullStr | Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| title_full_unstemmed | Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| title_short | Radioresistance-related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| title_sort | radioresistance related gene signatures identified by transcriptomics characterize the prognosis and immune landscape of pancreatic cancer patients |
| topic | Pancreatic cancer Radioresistance Gene signature Risk score model Immune landscape |
| url | https://doi.org/10.1186/s12885-024-13231-4 |
| work_keys_str_mv | AT dandanwang radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT juncao radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT yanhuichen radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT lishazhang radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT chanzhou radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT litaohuang radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients AT yanliangchen radioresistancerelatedgenesignaturesidentifiedbytranscriptomicscharacterizetheprognosisandimmunelandscapeofpancreaticcancerpatients |