Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Exploring the prognostic landscape of oral squamous cell carcinoma through mitochondrial damage-related genes

Oral squamous cell carcinoma (OSCC), the most prevalent form of oral cancer, poses significant challenges to the medical community due to its high recurrence rate and low survival rate. Mitochondrial Damage-Related Genes (MDGs) have been closely associated with the occurrence, metastasis, and progression of OSCC. Consequently, we constructed a prognostic model for OSCC based on MDGs and identified potential mitochondrial damage-related biomarkers. Gene expression profiles and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Differential analysis was conducted to identify MDGs associated with OSCC. COX analysis was employed to screen seven prognosis-related MDGs and build a prognostic prediction model for OSCC. Cases were categorized into low-risk or high-risk groups based on the optimal risk score threshold. Kaplan-Meier (KM) analysis revealed significant survival differences (P < 0.05). Additionally, the area under the ROC curve (AUC) for patient survival at 1 year, 3 years, and 5 years were 0.687, 0.704, and 0.70, respectively, indicating a high long-term predictive accuracy of the prognostic model. To enhance predictive accuracy, age, gender, risk score, and TN staging were incorporated into a nomogram and verified using calibration curves. Risk scoring based on MDGs was identified as a potential independent prognostic biomarker. Furthermore, BID and SLC25A20 were identified as two potential independent mitochondrial damage-related prognostic biomarkers, offering new therapeutic targets for OSCC.

A nomogram prediction of implant apical non-coverage on bone-added transcrestal sinus floor elevation: A retrospective cohort study

OBJECTIVES

To identify the risk indicators and develop and validate a nomogram prediction model of implant apical non-coverage by comprehensively analyzing clinical and radiographic factors in bone-added transcrestal sinus floor elevation (TSFE).

MATERIAL AND METHODS

A total of 260 implants in 195 patients receiving bone-added TSFE were included in the study. The population was divided into a development (180 implants) and a validation (80 implants) cohort. According to 6 months post-surgery radiographic images, implants were categorized as "apical non-coverage" or "apical covered." The association of risk factors including clinical and radiographic parameters with implant apical non-coverage was assessed using regression analyses. A nomogram prediction model was developed, and its validation and discriminatory ability were analyzed.

RESULTS

The nomogram predicting bone-added TSFE's simultaneously placed implant's apex non-coverage after 6 months. This study revealed that sinus angle, endo-sinus bone gain, implant protrusion length, graft contact walls, and distal angle were predictors of implant apical non-coverage. The generated nomogram showed a strong predictive capability (area under the curve [AUC] = 0.845), confirmed by internal validation using 10-fold cross-validation (Median AUC of 0.870) and temporal validation (AUC = 0.854). The calibration curve and decision curve analysis demonstrated good performance and high net benefit of the nomogram, respectively.

CONCLUSIONS

The clinical implementation of the present nomogram is suitable for predicting the apex non-coverage of implants placed simultaneously with bone-added TSFE after 6 months.

Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma

Objective: Epithelial-mesenchymal transition (EMT) is linked to an unfavorable prognosis in oral squamous cell carcinoma (OSCC). Here, we aimed to develop an EMT gene signature for OSCC prognosis. Methods: In TCGA dataset, prognosis-related EMT genes with p < 0.05 were screened in OSCC. An EMT gene signature was then conducted with LASSO method. The efficacy of this signature in predicting prognosis was externally verified in the GSE41613 dataset. Correlations between this signature and stromal/immune scores and immune cell infiltration were assessed by ESTIMATE and CIBERSORT algorithms. GSEA was applied for exploring significant signaling pathways activated in high- and low-risk phenotypes. Expression of each gene was validated in 40 paired OSCC and normal tissues via RT-qPCR. Results: A prognostic 9-EMT gene signature was constructed in OSCC. High risk score predicted poorer clinical outcomes than low risk score. ROCs confirmed the well performance on predicting 1-, 3- and 5-year survival. Multivariate cox analysis revealed that this signature was independently predictive of OSCC prognosis. The well predictive efficacy was validated in the GSE41613 dataset. Furthermore, this signature was distinctly related to stromal/immune scores and immune cell infiltration in OSCC. Distinct pathways were activated in two subgroups. After validation, AREG, COL5A3, DKK1, GAS1, GPX7 and PLOD2 were distinctly upregulated and SFRP1 was downregulated in OSCC than normal tissues. Conclusion: Our data identified and verified a robust EMT gene signature in OSCC, which provided a novel clinical tool for predicting prognosis and several targets against OSCC therapy.

Characterization of the m6A regulator-mediated methylation modification patterns in oral squamous cell carcinoma

N⁶-methyladenosine (m⁶A) is a form of posttranscriptional modification that plays important roles in cancer including oral squamous cell carcinoma (OSCC). Most studies to date have focused on a limited number of regulators and oncogenic pathways, thus failing to provide comprehensive insight into the dynamic effects of m⁶A modification. In addition, the role of m⁶A modification in shaping immune cell infiltration in OSCC has yet to be clarified. This study was designed to assess m⁶A modification dynamics in OSCC and to understand how such modifications influence clinical immunotherapeutic treatment outcomes. m⁶A modification patterns linked with 23 m⁶A regulators were analyzed in 437 OSCC patients from TCGA and GEO cohorts. These patterns were then quantified through m⁶A score based on algorithms derived from a principal component analysis (PCA) approach. The m⁶A modification patterns of OSCC samples were grouped into two clusters based on the m⁶A regulators expression, and immune cell infiltration was linked with the 5-year survival outcomes of patients in these clusters. 1575 genes associated with OSCC patient prognosis were identified and used to re-cluster these samples into two groups. Patients in clusters exhibiting higher levels of m⁶A regulator expression exhibited poorer overall survival (OS), whereas patients with high m⁶A scores survived for longer (p < 0.001). The overall mortality rates in the groups of patients with low and high m⁶A scores were 55% and 40%, respectively, and the m⁶A score distributions in clusters of patients grouped by m⁶A modification patterns and gene expression further supported the link between a high m⁶A score and better prognostic outcomes. Immunophenoscore (IPS) values for patients in different m⁶A score groups suggested that the use of PD-1-specific antibodies or CTLA-4 inhibitors alone or in combination would yield superior treatment outcomes in patients in the high-m⁶A score group relative to the low-m⁶A score group. m⁶A modification patterns are relevant to heterogeneity in OSCC. Detailed analyses of m⁶A modification patterns may thus offer novel insight regarding immune cell infiltration within the OSCC tumor microenvironment, guiding novel efforts to provide patients with more effective immunotherapeutic interventions.

Abnormal expression of HADH, an enzyme of fatty acid oxidation, affects tumor development and prognosis (Review)

Tumor occurrence and progression are closely associated with abnormal energy metabolism and energy metabolism associated with glucose, proteins and lipids. The reprogramming of energy metabolism is one of the hallmarks of cancer. As a form of energy metabolism, fatty acid metabolism includes fatty acid uptake, de novo synthesis and β‑oxidation. In recent years, the role of abnormal fatty acid β‑oxidation in tumors has gradually been recognized. Mitochondrial trifunctional protein (MTP) serves an important role in fatty acid β‑oxidation and HADH (two subtypes: α subunit, HADHA and β subunit, HADHB) are important subunits of MTP. HADH participates in the steps of 2, 3 and 4 fatty acid β‑oxidation. However, there is no review summarizing the specific role of HADH in tumors. Therefore, the present study focused on HADH as the main indicator to explore the changes in fatty acid β‑oxidation in several types of tumors. The present review summarized the changes in HADH in 11 organs (cerebrum, oral cavity, esophagus, liver, pancreas, stomach, colorectum, lymph, lung, breast, kidney), the effect of up‑ and downregulation and the relationship of HADH with prognosis. In summary, HADH can be either a suppressor or a promoter depending on where the tumor is located, which is closely associated with prognostic assessment. HADHA and HADHB have similar prognostic roles in known and comparable tumors.

Low Expression of AGPAT5 Is Associated With Clinical Stage and Poor Prognosis in Colorectal Cancer and Contributes to Tumour Progression

Background:

Colorectal cancer (CRC) has a high prevalence and poor prognosis. This study aimed to identify biomarkers related to the clinical stage (I-IV) of CRC.

Methods:

The LinkedOmics database was used as the discovery cohort, and two Gene Expression Omnibus (GEO) databases (GSE41258 and GSE422848) served as validation cohorts. The trend test of genes related to clinical stage (I-IV) of CRC patients was identified by the Jonckheere-Terpstra test. The cBioPortal database, Gene Expression Profiling Interactive Analysis (GEPIA) and PrognoScan databases were used to explore the expression change and prognostic value of clinical stage-related genes in CRC patients. CRC cells overexpressed AGPAT5 were constructed and used for cell counting kit-8 (CCK-8), flow cytometric, and wound healing assays in vitro.

Results:

We identified four clinical stage-related genes, GSR, AGPAT5, CRLF1, and NPR3, in CRC. The CNA frequencies of GSR, CRLF1, AGPAT5, and NPR3 occurred in 11%, 2.4%, 13%, and 3% of patients, respectively. The expression of GSR and AGPAT5 tended to decrease with CRC stage (I-IV) progression, and the expression of CRLF1 and NPR3 tended to increase with CRC stage (I-IV) progression. Compared with the normal group, AGPAT5 expression was markedly decreased in stage IV CRC. Higher GSR and AGPAT5 expression levels were associated with better overall survival (OS) and disease-free survival (DFS) in CRC patients. Lower CRLF1 and NPR3 expression levels were associated with better OS and DFS in CRC. GSR, CRLF1, AGPAT5, and NPR3 expression were related to CRC progression, microsatellite instability, and tumour purity in CRC. Furthermore, AGPAT5 was downregulated in CRC cell lines, and overexpression of AGPAT5 inhibited cell proliferation and migration and promoted cell apoptosis in CRC cells.

Conclusion:

Low AGPAT5 expression may serve as a poor prognostic factor and clinical stage biomarker in CRC. In addition, AGPAT5 acts as a tumour suppressor in CRC progression.

Hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex subunit beta gene as a tumour suppressor in stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is the most common type of gastric cancer. In this study, the functions and potential mechanisms of hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex subunit beta (HADHB) in STAD were explored.

METHODS

Different bioinformatics analyses were performed to confirm HADHB expression in STAD. HADHB expression in STAD tissues and cells was also evaluated using western blot, qRT-PCR, and immunohistochemistry. Further, the viability, proliferation, colony formation, cell cycle determination, migration, and wound healing capacity were assessed, and the effects of HADHB on tumour growth, cell apoptosis, and proliferation in nude mice were determined. The upstream effector of HADHB was examined using bioinformatics analysis and dual luciferase reporter assay. GSEA was also employed for pathway enrichment analysis and the expression of Hippo-YAP pathway-related proteins was detected.

RESULTS

The expression of HADHB was found to be low in STAD tissues and cells. The upregulation of HADHB distinctly repressed the viability, proliferation, colony formation, cell cycle progression, migration, invasion, and wound healing of HGC27 cells, while knockdown of HADHB led to opposite effects. HADHB upregulation impeded tumour growth and cell proliferation, and enhanced apoptosis in nude mice. KLF4, whose expression was low in STAD, was identified as an upstream regulator of HADHB. KLF4 upregulation abolished the HADHB knockdown-induced tumour promoting effects in AGS cells. Further, HADHB regulates the Hippo-YAP pathway, which was validated using a pathway rescue assay. Low expression of KLF4 led to HADHB downregulation in STAD.

CONCLUSION

HADHB might function as a tumour suppressor gene in STAD by regulation the Hippo-YAP pathway.

Risk Stratification in Oral Cancer: A Novel Approach

Background

Oral squamous cell carcinoma (OSCC) is a common head and neck cancer with high morbidity and mortality. Currently, treatment decisions are guided by TNM staging, which omits important negative prognosticators such as lymphovascular invasion, perineural invasion (PNI), and histologic differentiation. We proposed nomogram models based on adverse pathological features to identify candidates suitable for treatment escalation within each risk group according to the National Comprehensive Cancer Network (NCCN) guidelines.

Methods

Anonymized clinicopathologic data of OSCC patients from 5 tertiary healthcare institutions in Asia were divided into 3 risk groups according to the NCCN guidelines. Within each risk group, nomograms were built to predict overall survival based on histologic differentiation, histologic margin involvement, depth of invasion (DOI), extranodal extension, PNI, lymphovascular, and bone invasion. Nomograms were internally validated with precision-recall analysis and the Kaplan-Meier survival analysis.

Results

Low-risk patients with positive pathological nodal involvement and/or positive PNI should be considered for adjuvant radiotherapy. Intermediate-risk patients with gross bone invasion may benefit from concurrent chemotherapy. High-risk patients with positive margins, high DOI, and a high composite score of histologic differentiation, PNI, and the American Joint Committee on Cancer (AJCC) 8th edition T staging should be considered for treatment escalation to experimental therapies in clinical trials.

Conclusion

Nomograms built based on prognostic adverse pathological features can be used within each NCCN risk group to fine-tune treatment decisions for OSCC patients.

Screening of periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms

BACKGROUND

Periodontitis is a common oral immune inflammatory disease and early detection plays an important role in its prevention and progression. However, there are no accurate biomarkers for early diagnosis.

OBJECTIVE

This study screened periodontitis-related diagnostic biomarkers based on weighted gene correlation network analysis and machine algorithms.

METHODS

Transcriptome data and sample information of periodontitis and normal samples were obtained from the Gene Expression Omnibus (GEO) database, and key genes of disease-related modules were obtained by bioinformatics. The key genes were subjected to Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and 5 machine algorithms: Logistic Regression (LR), Random Forest (RF), Gradient Boosting Decisio Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Expression and correlation analysis were performed after screening the optimal model and diagnostic biomarkers.

RESULTS

A total of 47 candidate genes were obtained, and the LR model had the best diagnostic efficiency. The COL15A1, ICAM2, SLC15A2, and PIP5K1B were diagnostic biomarkers for periodontitis, and all of which were upregulated in periodontitis samples. In addition, the high expression of periodontitis biomarkers promotes positive function with immune cells.

CONCLUSION

COL15A1, ICAM2, SLC15A2 and PIP5K1B are potential diagnostic biomarkers of periodontitis.

Development of a lncRNA-based prognostic signature for oral squamous cell carcinoma

BACKGROUND

We aimed to establish a long noncoding RNA (lncRNA)-based signature for accurately predicting prognosis and guiding the personalized clinical management of oral squamous cell carcinoma (OSCC).

METHODS

OSCC RNA sequencing profiles were acquired from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed to construct a lncRNA-based prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves and calibration curves were used to assess the effectiveness and accuracy of the signature. Additionally, we conducted single-sample gene-set enrichment analysis to infer the different degrees of immunocyte infiltration. Weighted correlation network analysis, enrichment analysis and Spearman's correlation analysis were implemented to screen immune-related genes that interact with the lncRNA signature.

RESULTS

In total, 14 lncRNAs were defined as potential prognostic biomarkers. Based on these lncRNAs, patients were divided into low- and high-risk subgroups with different survival times (p < 0.001). In addition, the reliability of the prognostic signature was verified by Kaplan-Meier analysis, ROC analysis and calibration curves. Patients in the low-risk group exhibited more significant immune cell infiltration. Simultaneously, a potential regulatory network consisting of 8 lncRNAs and 159 protein-coding genes in the top 10 immune-related biological process terms was constructed.

CONCLUSIONS

Our findings suggested that the 14-lncRNA signature has satisfactory performance in predicting the prognosis of OSCC, thereby providing new insights to the pathogenesis, clinical patient management and therapeutic intervention. The different immune cell infiltration statuses of OSCC patients may encourage immunotherapy.

Molecular Subtypes Based on Cell Differentiation Trajectories in Head and Neck Squamous Cell Carcinoma: Differential Prognosis and Immunotherapeutic Responses

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the most common and lethal malignant tumors. We aimed to investigate the HNSCC cell differentiation trajectories and the corresponding clinical relevance.

Methods

Based on HNSCC cell differentiation-related genes (HDRGs) identified by single-cell sequencing analysis, the molecular subtypes and corresponding immunity, metabolism, and stemness characteristics of 866 HNSCC cases were comprehensively analyzed. Machine-learning strategies were used to develop a HNSCC cell differentiation score (HCDscore) in order to quantify the unique heterogeneity of individual samples. We also assessed the prognostic value and biological characteristics of HCDscore using the multi-omics data.

Results

HNSCCs were stratified into three distinct molecular subtypes based on HDRGs: active stroma (Cluster-A), active metabolism (Cluster-B), and active immune (Cluster-C) types. The three molecular subtypes had different characteristics in terms of biological phenotype, genome and epigenetics, prognosis, immunotherapy and chemotherapy responses. We then demonstrated the correlations between HCDscore and the immune microenvironment, subtypes, carcinogenic biological processes, genetic variation, and prognosis. The low-HCDscore group was characterized by activation of immunity, enhanced response to anti-PD-1/PD-L1 immunotherapy, and better survival compared to the high-HCDscore group. Finally, by integrating the HCDscore with prognostic clinicopathological characteristics, a nomogram with strong predictive performance and high accuracy was constructed.

Conclusions

This study revealed that the cell differentiation trajectories in HNSCC played a nonnegligible role in patient prognosis, biological characteristics, and immune responses. Evaluating cancer cell differentiation will help to develop more effective immunotherapy, metabolic therapy, and chemotherapy strategies.

Lipid Metabolism in Cancer: The Role of Acylglycerolphosphate Acyltransferases (AGPATs)

Simple Summary

Rapidly proliferating cancer cells reprogram lipid metabolism to keep the balance between fatty acid uptake, synthesis, consumption, and storage as triacylglycerides (TAG). Acylglycerolphosphate acyltransferases (AGPATs)/lysophosphatidic acid acyltransferases (LPAATs) are a family of enzymes that catalyze the synthesis of phosphatidic acid (PA), an intermediate in TAG synthesis, a signaling molecule, and a precursor of phospholipids. Importantly, the expression of AGPATs has been linked to diverse physiological and pathological phenotypes, including cancer. In this review, we present an overview of lipid metabolism reprogramming in cancer cells and give insight into the expression of AGPAT isoforms as well as their association with cancers, parameters of tumor biology, patient classification, and prognosis.

Abstract

Altered lipid metabolism is an emerging hallmark of aggressive tumors, as rapidly proliferating cancer cells reprogram fatty acid (FA) uptake, synthesis, storage, and usage to meet their increased energy demands. Central to these adaptive changes, is the conversion of excess FA to neutral triacylglycerides (TAG) and their storage in lipid droplets (LDs). Acylglycerolphosphate acyltransferases (AGPATs), also known as lysophosphatidic acid acyltransferases (LPAATs), are a family of five enzymes that catalyze the conversion of lysophosphatidic acid (LPA) to phosphatidic acid (PA), the second step of the TAG biosynthesis pathway. PA, apart from its role as an intermediate in TAG synthesis, is also a precursor of glycerophospholipids and a cell signaling molecule. Although the different AGPAT isoforms catalyze the same reaction, they appear to have unique non-overlapping roles possibly determined by their distinct tissue expression and substrate specificity. This is best exemplified by the role of AGPAT2 in the development of type 1 congenital generalized lipodystrophy (CGL) and is also manifested by recent studies highlighting the involvement of AGPATs in the physiology and pathology of various tissues and organs. Importantly, AGPAT isoform expression has been shown to enhance proliferation and chemoresistance of cancer cells and correlates with increased risk of tumor development or aggressive phenotypes of several types of tumors.