Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Xu, Ze-Jun; Zhang, Min; Chen, Lin; Yang, Xiaolei; Li, Xiaoyü; Liang, Qian; Tang, Yuqing; Liu, Jian

doi:10.1007/s10495-023-01832-6

Cited by 5 publications

(5 citation statements)

References 57 publications

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“…As a new cancer related lncRNA, The expression of SNHG17 significantly rises in multiple tumors, suggesting its potential involvement in carcinogenesis. Research has shown that SNHG17 is intimately connected to the growth, invasion, migration, resistance to chemicals, and apoptosis of tumor cells, and head and neck squamous cell carcinoma patients whose SNHG17 expression was high had a significantly unfavorable prognosis [17]. Studies showed that lncRNA AC103706.1 is associated with fat metabolism, copper apoptosis, and prognosis in ccRCC [51,52].…”

Section: Discussionmentioning

confidence: 99%

“…LncRNA is abnormally expressed in various cancers, it has be found that multiple lncRNAs can participate in metastasis, resistance to drugs, and recurrence of cancer by regulating the stemness of CSCs [14][15][16]. Studies have shown that the stem related lncRNAs (SRlncRNAs) were strongly correlated with the prognosis of individuals [17,18], however, Whether the SRlncRNAs would affect the prognosis of ccRCC is still unknown.…”

Section: Introductionmentioning

confidence: 99%

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Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Zhang,

Liang

et al. 2024

BMC Med Genomics

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Long non-coding RNAs (lncRNAs) and cancer stem cells (CSCs) are crucial for the growth, migration, recurrence, and medication resistance of tumors. However, the impact of lncRNAs related to stemness on the outcome and tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC) is still unclear. In this study, we aimed to predict the outcome and TIME of ccRCC by constructing a stem related lncRNAs (SRlncRNAs) signature. We firstly downloaded ccRCC patients’ clinical data and RNA sequencing data from UCSC and TCGA databases, and abtained the differentially expressed lncRNAs highly correlated with stem index in ccRCC through gene expression differential analysis and Pearson correlation analysis. Then, we selected suitable SRlncRNAs for constructing a prognostic signature of ccRCC patients by LASSO Cox regression. Further, we used nomogram and Kaplan Meier curves to evaluate the SRlncRNA signature for the prognose in ccRCC. At last, we used ssGSEA and GSVA to evaluate the correlation between the SRlncRNAs signature and TIME in ccRCC. Finally, We obtained a signtaure based on six SRlncRNAs, which are correlated with TIME and can effectively predict the ccRCC patients’ prognosis. The SRlncRNAs signature may be a noval prognostic indicator in ccRCC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Zhang,

Liang

et al. 2024

BMC Med Genomics

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“…Herein, we construct a risk model consisting of seven anoikis-related lncRNAs (AC015878.1, CYTOR, EMSLR, LINC01503, LINC02084, RAB11B-AS1, Z97200.1) based on the data presented by the Cox and LASSO regression analyses conducted for predicting the prognosis, immune response, immunotherapy and chemotherapy response for HNSCC patients. More specifically, AC015878.1 was seen to be a member of the stemness-related model for HNSCC [ 22 ], while EMSLR and Z97200.1 were seen to be important components of the prognostic signature used for bladder cancer and kidney renal clear cell carcinoma, respectively [ 23 , 24 ]. EMSLR regulated the cell proliferation and differentiation by repressing the promoter activity of LncPRESS1 in lung cancer cell [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting the prognosis, immune response, and immunotherapy in head and neck squamous cell carcinoma using a novel risk model based on anoikis-related lncRNAs

Deng,

Wei,

et al. 2023

Eur J Med Res

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Background Head and neck squamous cell carcinoma (HNSCC) is an extremely heterogeneous and metastatic disease. Anoikis, which is a specific type of programmed apoptosis, is involved in tumor metastasis, tissue homeostasis, and development. Herein, we constructed an anoikis-related long non-coding RNA (lncRNA) signature to predict the prognosis, immune responses, and therapeutic effects in HNSCC patients. Methods A total of 501 HNSCC samples were acquired from the TCGA database and randomly classified into the training and validation groups (1:1 ratio). Thereafter, the results derived from the training set were analyzed with the LASSO regression analysis, and a novel anoikis-related lncRNA risk model was constructed. Time-dependent ROC curves and Kaplan–Meier analysis were carried out to assess the diagnostic value and survival outcomes. A nomogram was utilized to predict the prognostic accuracy. Furthermore, we studied the tumor microenvironment, tumor mutation burden, enrichment pathways, and the response to chemotherapy and immunotherapy. Results Seven anoikis-related lncRNAs (AC015878.1, CYTOR, EMSLR, LINC01503, LINC02084, RAB11B-AS1, Z97200.1) were screened to design a novel risk model, which was recognized as the independent prognostic factor for HNSCC patients. The findings implied that low-risk patients showed significantly longer OS, PFS, and DSS compared to those high-risk patients. The two groups that were classified using the risk model showed significant differences in their immune landscape. The risk model also predicted that low-risk HNSCC patients could attain a better response to immunotherapy, while high-risk patients would be more sensitive to gemcitabine, docetaxel, and cisplatin. Conclusions We constructed a novel risk model that could be employed for effectively predicting patient prognosis with a good independent prognostic value for HNSCC patients. Furthermore, this model could be used for designing new immunotherapeutic and chemotherapeutic strategies, and it helps clinicians establish personalized and detailed strategies for HNSCC patients.

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“…Target modules were identified through weighted gene co-expression network analysis (WGCNA) using The R package “WGCNA” [ 33 ]. WGCNA is a widely used bioinformatics method in genomics and systems biology to identify patterns of gene expression and how these patterns correlate with external traits or conditions, which is extensively used in cancer-related study [ 13 ]. Following the filtration of RNA sequence data to mitigate outliers, a similarity matrix was incorporated.…”

Section: Methodsmentioning

confidence: 99%

“…Long non-coding RNAs (lncRNAs), despite their lack of coding capacity [11][12][13][14], play significant roles in various cellular processes, including the regulation of gene expression, facilitation of subcellular transport, modulation of protein degradation, and promotion of organelle biogenesis [15]. Research indicates that abnormal expression of lncRNAs significantly influences TNBC cell behavior, impacting proliferation, migration, metastasis, and tumorigenicity [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

Zhang,

Wang

et al. 2024

J Transl Med

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Background Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients. Methods Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan–Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients. Results The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker. Conclusion The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

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Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Cited by 5 publications

References 57 publications

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Stemness related lncRNAs signature for the prognosis and tumor immune microenvironment of ccRCC patients

Predicting the prognosis, immune response, and immunotherapy in head and neck squamous cell carcinoma using a novel risk model based on anoikis-related lncRNAs

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

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