Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

Cetuximab inhibits colorectal cancer development through inactivating the Wnt/β-catenin pathway and modulating PLCB3 expression

Colorectal cancer (CRC) often necessitates cetuximab (an EGFR-targeting monoclonal antibody) for treatment. Despite its clinical utility, the specific operative mechanism of cetuximab remains elusive. This research investigated the influence of PLCB3, a potential CRC oncogene, on cetuximab treatment. We extracted differentially expressed genes from the GSE140973, the overlapping genes combined with 151 Wnt/β-Catenin signaling pathway-related genes were identified. Then, we conducted bioinformatics analysis to pinpoint the hub gene. Subsequently, we investigated the clinical expression characteristics of this hub gene, through cell experimental, scrutinized the impact of cetuximab and PLCB3 on CRC cellular progression. The study identified 26 overlapping genes. High expression of PLCB3, correlated with poorer prognosis. PLCB3 emerged as a significant oncogene associated with patient prognosis. In vitro tests revealed that cetuximab exerted a cytotoxic effect on CRC cells, with PLCB3 knockdown inhibiting CRC cell progression. Furthermore, cetuximab treatment led to a reduction in both β-catenin and PLCB3 expression, while simultaneously augmenting E-cadherin expression. These findings revealed PLCB3 promoted cetuximab inhibition on Wnt/β-catenin signaling. Finally, simultaneous application of cetuximab with a Wnt activator (IM12) and PLCB3 demonstrated inhibited CRC proliferation, migration, and invasion. The study emphasized the pivotal role of PLCB3 in CRC and its potential to enhance the efficacy of cetuximab treatment. Furthermore, cetuximab suppressed Wnt/β-catenin pathway to modulate PLCB3 expression, thus inhibiting colorectal cancer progression. This study offered fresh perspectives on cetuximab mechanism in CRC.

Development and validation of a GRGPI model for predicting the prognostic and treatment outcomes in head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is among the most lethal and most prevalent malignant tumors. Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. Therefore, we aimed at identifying a glycolysis-related prognostic model for HNSCC and to analyze its relationship with tumor immune cell infiltrations.

Methods

The mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), while glycolysis-related genes were obtained from the Molecular Signature Database (MSigDB). Bioinformatics analysis included Univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to select optimal prognosis-related genes for constructing glycolysis-related gene prognostic index(GRGPI), as well as a nomogram for overall survival (OS) evaluation. GRGPI was validated using the Gene Expression Omnibus (GEO) database. A predictive nomogram was established based on the stepwise multivariate regression model. The immune status of GRGPI-defined subgroups was analyzed, and high and low immune groups were characterized. Prognostic effects of immune checkpoint inhibitor (ICI) treatment and chemotherapy were investigated by Tumor Immune Dysfunction and Exclusion (TIDE) scores and half inhibitory concentration (IC50) value. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to validate the model by analyzing the mRNA expression levels of the prognostic glycolysis-related genes in HNSCC tissues and adjacent non-tumorous tissues.

Results

Five glycolysis-related genes were used to construct GRGPI. The GRGPI and the nomogram model exhibited robust validity in prognostic prediction. Clinical correlation analysis revealed positive correlations between the risk score used to construct the GRGPI model and the clinical stage. Immune checkpoint analysis revealed that the risk model was associated with immune checkpoint-related biomarkers. Immune microenvironment and immune status analysis exhibited a strong correlation between risk score and infiltrating immune cells. Gene set enrichment analysis (GSEA) pathway enrichment analysis showed typical immune pathways. Furthermore, the GRGPIdel showed excellent predictive performance in ICI treatment and drug sensitivity analysis. RT-qPCR showed that compared with adjacent non-tumorous tissues, the expressions of five genes were significantly up-regulated in HNSCC tissues.

Conclusion

The model we constructed can not only be used as an important indicator for predicting the prognosis of patients but also had an important guiding role for clinical treatment.

Identification and Analysis of Senescence-Related Genes in Head and Neck Squamous Cell Carcinoma by a Comprehensive Bioinformatics Approach

Head and neck cancer is the sixth most frequent cancer all over the world, with the majority of subtypes of head and neck squamous cell carcinoma (HNSCC). Cellular senescence-associated genes have been confirmed to play a critical role in cancer and have the potential to be prognostic biomarkers for cancer. Clinical information of HNSCC samples and expression data were acquired from public databases. Expression profiles of genes related to cellular senescence were used to identify molecular subtypes by consensus clustering. To screen differentially expressed genes (DEGs) between different subtypes, differential analysis was performed. We used the univariate Cox regression to identify prognostic DEGs and performed least absolute shrinkage and selection operator (LASSO) to optimize and construct a prognostic model. CIBERSORT, ESTIMATE, and TIDE tools were applied to estimate immune characteristics. Four molecular subtypes were established based on cellular senescence-associated genes. Differential prognosis was observed among different subtypes with C4 having the longest overall survival and C1 having the worst prognosis. C4 subtype also showed the highest immune infiltration. We screened a total of eight cellular senescence prognosis-related genes and established a cellular senescence-related signature score (CSRS.Score) that could stratify samples into high-CSRS.Score and low-CSRS.Score groups. The high-CSRS.Score group had worse prognosis, lower immune infiltration, and lower response to immunotherapy. We further improved the prognostic model and survival prediction by combining CSRS.Score with clinicopathological features using a decision tree model, which had high predictive accuracy and survival prediction. This study demonstrated an important role of cellular senescence in HNSCC. The identified eight cellular senescence-associated genes have the potential to provide ideas for adjuvant treatment and personalized treatment of HNSCC patients.

A novel signature derived from metabolism-related genes GPT and SMS to predict prognosis of laryngeal squamous cell carcinoma

Background

A growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC.

Methods

We identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of The Cancer Genome Atlas (TCGA) and Gene set enrichment analysis (GSEA). After the screening of protein–protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan–Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in LSCC cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results.

Results

Among the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The prognostic signature was proven as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application.

Conclusions

Differentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified it via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02647-2.

Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes

Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.

Stanniocalcin 2 (STC2): a universal tumour biomarker and a potential therapeutical target

Stanniocalcin 2 (STC2) is a glycoprotein which is expressed in a broad spectrum of tumour cells and tumour tissues derived from human breast, colorectum, stomach, esophagus, prostate, kidney, liver, bone, ovary, lung and so forth. The expression of STC2 is regulated at both transcriptional and post-transcriptional levels; particularly, STC2 is significantly stimulated under various stress conditions like ER stress, hypoxia and nutrient deprivation. Biologically, STC2 facilitates cells dealing with stress conditions and prevents apoptosis. Importantly, STC2 also promotes the development of acquired resistance to chemo- and radio- therapies. In addition, multiple groups have reported that STC2 overexpression promotes cell proliferation, migration and immune response. Therefore, the overexpression of STC2 is positively correlated with tumour growth, invasion, metastasis and patients' prognosis, highlighting its potential as a biomarker and a therapeutic target. This review focuses on discussing the regulation, biological functions and clinical importance of STC2 in human cancers. Future perspectives in this field will also be discussed.

Inflammation-Related Gene Signature for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma

Purpose

The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC.

Patients and Methods

IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database.

Results

In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells.

Conclusion

Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.

Comprehensive Analysis of mTORC1 Signaling Pathway–Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy

Objective: The mammalian target of the rapamycin complex 1 (mTORC1) signaling pathway has emerged as a crucial player in the oncogenesis and development of head and neck squamous cell carcinoma (HNSCC), however, to date, no relevant gene signature has been identified. Therefore, we aimed to construct a novel gene signature based on the mTORC1 pathway for predicting the outcomes of patients with HNSCC and their response to treatment.

Methods: The gene expression and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The key prognostic genes associated with the mTORC1 pathway were screened by univariate Cox regression analyses. A prognostic signature was then established based on significant factors identified in the multivariate Cox regression analysis. The performance of the multigene signature was evaluated by the Kaplan–Meier (K–M) survival analysis and receiver operating characteristic (ROC) analysis. Based on the median risk score, patients were categorized into high- and low-risk groups. Subsequently, a hybrid prognostic nomogram was constructed and estimated by a calibration plot and decision curve analysis. Furthermore, immune cell infiltration and therapeutic responses were compared between the two risk groups. Finally, we measured the expression levels of seven genes by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results: The mTORC1 pathway–based signature was constructed using the seven identified genes (SEC11A, CYB5B, HPRT1, SLC2A3, SC5D, CORO1A, and PIK3R3). Patients in the high-risk group exhibited a lower overall survival (OS) rate than those in the low-risk group in both datasets. Through the univariate and multivariate Cox regression analyses, this gene signature was confirmed to be an independent prognostic risk factor for HNSCC. The constructed nomogram based on age, American Joint Committee on Cancer (AJCC) stage, and the risk score exhibited satisfactory performance in predicting the OS. In addition, immune cell infiltration and chemotherapeutic and immunotherapeutic responses differed significantly between the two risk groups. The expression levels of SEC11A and CYB5B were higher in HNSCC tissues than in normal tissues.

Conclusion: Our study established and verified an mTORC1 signaling pathway–related gene signature that could be used as a novel prognostic factor for HNSCC.

A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.

METHODS

Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.

RESULTS

Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.

CONCLUSION

The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.