Novel Therapies for Tongue Squamous Cell Carcinoma Patients with High-Grade Tumors

Lin, Hao; Chen, Lu; Li, Weizhong; Chen, Zihao

doi:10.3390/life11080813

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…Although immunotherapies are promising drug candidates for patients with TNBC (12), there are some limitations including low response rates, the risk of side effects, and the lack of robust biomarkers. The approximate response rate to ICB was 20% in most solid tumors (13). Without candidate selection, only 10% of TNBC patients in the Atezolizumab-Chemotherapy group achieved a complete response (11).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

et al. 2021

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BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

et al. 2021

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“…We have indicated that REEP6 might serve as an effective diagnostic or prognostic biomarker for TSCC patients, but there are possible limitations in the study as outlined below. (1) The specificity of commercial polyclonal REEP6 antibody is not high enough, since only a small fraction of REEP6 in IHC staining and Western blot was detected. According to our data, we probably could demonstrate the relative protein levels of REEP6 between normal and tumor tissues in TSCC patients.…”

Section: Discussionmentioning

confidence: 99%

“…Tongue squamous cell carcinoma (TSCC) is one of the major subsites of oral squamous cell carcinoma (OSCC) and a tumor with poor prognosis [1]. The incidence of TSCC is increasing around the world.…”

Section: Introductionmentioning

confidence: 99%

The Clinical and Biological Effects of Receptor Expression-Enhancing Protein 6 in Tongue Squamous Cell Carcinoma

et al. 2023

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There are currently no effective biomarkers for the diagnosis and treatment of tongue squamous cell carcinoma (TSCC), which causes a poor 5-year overall survival rate. Thus, it is crucial to identify more effective diagnostic/prognostic biomarkers and therapeutic targets for TSCC patients. The receptor expression-enhancing protein 6 (REEP6), a transmembrane endoplasmic reticulum resident protein, controls the expression or transport of a subset of proteins or receptors. Although it was reported that REEP6 plays a role in lung and colon cancers, its clinical impact and biological role in TSCC are still unknown. The present study aimed to identify a novel effective biomarker and therapeutic target for TSCC patients. Expression levels of REEP6 in specimens from TSCC patients were determined with immunohistochemistry. Gene knockdown was used to evaluate the effects of REEP6 in cancer malignancy (colony/tumorsphere formation, cell cycle regulation, migration, drug resistance and cancer stemness) of TSCC cells. The clinical impact of REEP6 expression and gene co-expression on prognosis were analyzed in oral cancer patients including TSCC patients from The Cancer Genome Atlas database. Tumor tissues had higher levels of REEP6 compared to normal tissues in TSCC patients. Higher REEP6 expression was related to shorter disease-free survival (DFS) in oral cancer patients with poorly differentiated tumor cells. REEP6-knocked-down TSCC cells showed diminished colony/tumorsphere formation, and they also caused G1 arrest and decreased migration, drug resistance and cancer stemness. A high co-expression of REEP6/epithelial–mesenchymal transition or cancer stemness markers also resulted in poor DFS in oral cancer patients. Thus, REEP6 is involved in the malignancy of TSCC and might serve as a potential diagnostic/prognostic biomarker and therapeutic target for TSCC patients.

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“…Moreover, hug genes, including CCNB2, PLK1, CDC20, AURKA, MAD2L1, BIRC5, CCNB1, and TPX2, participated. In TSCC, CCNB2 and CDC20 were novel therapies for TSCC Patients with High-Grade Tumors ( 51 ). BIRC5 was inversely associated with the characterization of antigen processing machinery and survival expression in tonsillar squamous cell carcinoma ( 52 ).…”

Section: Discussionmentioning

confidence: 99%

“…Although the mechanism of this action is not completely clear, the detection of sensitive and specific biomarkers related to HPV in human TSCC has been compared and clarified, among which HPV16, E6 and E7 have been studied and discussed most frequently (12). HPV16 plays a leading role in HPV16, 18,31,45,33,52,58,35,59,56,51,6,39,68,82, 66, 70 and 73, and is injected into 46-63% of squamous cell carcinomas, followed by HPV18 (10-14%) (13). HPV-positive oropharyngeal squamous cell carcinoma (OPSCCs) seem to have a better prognosis than HPV-negative ones, as they are less likely to recur locally and are more radiosensitive (14,15).…”

Section: Introductionmentioning

confidence: 99%

Oncogenic KPNA2 Serves as a Biomarker and Immune Infiltration in Patients With HPV Positive Tongue Squamous Cell Carcinoma

Gao

Liu

et al. 2022

Front. Oncol.

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Human tongue squamous cell carcinoma (TSCC), the most prevalent type of oral cancer, is associated with human papillomavirus (HPV) infection. Our previous work showed Karyopherin α2 (KPNA2), as an oncogene of TSCC, by relegating the p53/autophagy signaling pathway. Nevertheless, the significance of KPNA2 in TSCC pathogenesis has not been established. KPNA2 levels were evaluated via the TCGA database, and its effects on survival outcomes were assessed by LASSO, Kaplan‐Meier, and COX regression analyses. CIBERSORT and ESTIMATE investigated the relationships between KPNA2 and immune infiltration. At the same time, KPNA2 and HPV infection was analyzed by immunohistochemistry. In addition, the association between downstream molecular regulation pathways and KPNA2 levels was determined by GO, GSEA, and WGCNA. In TSCC, KPNA2 levels were associated with clinical prognosis and tumor grade. Moreover, KPNA2 may be involved in cancer cell differentiation and facilitates tumor-related genes and signaling pathways, such as Cell Cycle, Mitotic G1 phase, G1/S transition, DNA Repair, and Transcriptional Regulation TP53 signaling pathways. Nevertheless, regulatory B cells, follicular helper B cells, and immune and stromal scores between low- and high-KPNA2 expression groups were insignificant. These results imply that KPNA2 is highly involved in tumor grade and prognosis of TSCC. KPNA2 levels correct with HPV 16 markedly regulated cell differentiation, several oncogenes, and cancer‐related pathways.

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Novel Therapies for Tongue Squamous Cell Carcinoma Patients with High-Grade Tumors

Cited by 7 publications

References 47 publications

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype

The Clinical and Biological Effects of Receptor Expression-Enhancing Protein 6 in Tongue Squamous Cell Carcinoma

Oncogenic KPNA2 Serves as a Biomarker and Immune Infiltration in Patients With HPV Positive Tongue Squamous Cell Carcinoma

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