A transcriptional metabolic gene-set based prognostic signature is associated with clinical and mutational features in head and neck squamous cell carcinoma

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Generation of novel lipid metabolism-based signatures to predict prognosis and immunotherapy response for colorectal adenocarcinoma

Lipid metabolism reprogramming involves in epithelial-mesenchymal transition (EMT), cancer stemness and immune checkpoints (ICs), which influence the metastasis of cancer. This study aimed to generate lipid metabolism-based signatures to predict prognosis, immunotherapy and chemotherapy response for colorectal adenocarcinoma (COAD). Transcriptome data and clinical information of COAD patients were collected from the cancer genome atlas (TCGA) database. The expression of EMT-, stem cell-, and IC-related genes were assessed between COAD and control samples. Modules and genes correlated EMT, ICs and stemness signatures were identified through weighted gene co-expression network analysis (WGCNA). Prognostic signatures were generated and then the distribution of risk genes was evaluated using single-cell RNA sequencing (scRNA-seq) data from GSE132465 dataset. COAD patients exhibited increased EMT score and stemness along with decreased ICs. Next, 12 hub genes (PIK3CG, ALOX5AP, PIK3R5, TNFAIP8L2, DPEP2, PIK3CD, PIK3R6, GGT5, ELOVL4, PTGIS, CYP7B1 and PRKD1) were found within green and yellow modules correlated with EMT, stemness and ICs. Lipid metabolism-based prognostic signatures were generated based on PIK3CG, GGT5 and PTGIS. Patients with high-risk group had poor prognosis, elevated ESTIMATEScore and StromalScore, 100% mutation rate and higher TIDE score. Samples in low-risk group had more immunogenicity on ICIs. Notably, PIK3CG was expressed in B cells, while GGT5 and PTGIS were expressed in stromal cells. This study generates lipid metabolism-based signatures correlated with EMT, stemness and ICs for predicting prognosis of COAD, and provides potential therapeutic targets for immunotherapy in COAD.

Dysregulation of base excision repair factors associated with low tumor immunogenicity in head and neck cancer: implication for immunotherapy

Background

Head and neck squamous carcinoma (HNSCC) is caused by different exogenous risk factors including smoking cigarettes, alcohol consumption, and HPV infection. Base excision repair (BER) is the frontline to repair oxidative DNA damage, which is initiated by the DNA N-glycosylase proteins (OGG1) and other BER factors including DNA polymerase β (POLB).

Objective

Explore whether BER genes' (OGG1, POLB) overexpression in HNSCC alters genomic integrity, immunogenicity, and its role in prognostic value.

Design

RNA sequencing (RNA-Seq) and clinical information (age, gender, histological grade, survival status, and stage) of 530 patients of HNSCC were retrieved from the Cancer Genome Atlas. Patients' data are categorized HPV positive or negative to analyze the tumor data including the tumor stage, POLB, and OGG1 gene expression.

Methods

RNA-Seq of HNSCC data retrieved and mutation count and aneuploidy score were compared using an unpaired t-test. The TIMER algorithm was used to calculate the tumor abundance of six infiltrating immune cells (CD4+ T cells, CD8+ T cells, B cells, neutrophils, macrophages, and dendritic cells) based on RNA-Seq expression profile data. The correlation between the POLB, OGG1, and immune cells was calculated by Spearman correlation analysis using TIMER 2.0.

Results

Our data analysis reveals that BER genes frequently overexpressed in HNSCC tumors and increase mutation count. In addition, OGG1 and POLB overexpression are associated with low infiltration of immune cells, low immune checkpoint gene expression (PD-1, cytotoxic T-lymphocyte antigen 4, program death ligand 1, and program death ligand 2), and innate immune signaling genes. Furthermore, dysregulated BER factors in Human papillomavirus (HPV) positive tumors had better overall survival.

Conclusion

Our analysis suggests that dysregulation of the BER genes panel might be a potential prognosis marker and/or an attractive target for an immune checkpoint blockade in HNSCC cancers. However, our observation still requires further experimental-based scientific validation studies.

Construction and validation of a folate metabolism-related gene signature for predicting prognosis in HNSCC

PURPOSE

Metabolic reprogramming is currently considered a hallmark of tumor and immune development. It is obviously of interest to identify metabolic enzymes that are associated with clinical prognosis in head and neck squamous cell carcinomas (HNSCC).

METHODS

Candidate genes were screened to construct folate metabolism scores by Cox regression analysis. Functional enrichment between high- and low-folate metabolism groups was explored by GO, KEGG, GSVA, and ssGSEA. EPIC, MCPcounter, and xCell were utilized to explore immune cell infiltration between high- and low-folate metabolism groups. Relevant metabolic scores were calculated and visually analyzed by the "IOBR" software package.

RESULTS

To investigate the mechanism behind metabolic reprogramming of HNSCC, 2886 human genes associated with 86 metabolic pathways were selected. Folate metabolism is significantly enriched in HNSCC, and that the six-gene (MTHFD1L, MTHFD2, SHMT2, ATIC, MTFMT, and MTHFS) folate score accurately predicts and differentiates folate metabolism levels. Reprogramming of folate metabolism affects CD8T cell infiltration and induces immune escape through the MIF signaling pathway. Further research found that SHMT2, an enzyme involved in folate metabolism, inhibits CD8T cell infiltration and induces immune escape by regulating the MIF/CD44 signaling axis, which in turn promotes HNSCC progression.

CONCLUSIONS

Our study identified a novel and robust folate metabolic signature. A folate metabolic signature comprising six genes was effective in assessing the prognosis and reflecting the immune status of HNSCC patients. The target molecule of folate metabolic reprogramming, SHMT2, probably plays a very important role in HNSCC development and immune escape.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes

Background

Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.

Method

Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.

Result

Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.

Conclusion

This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma

BACKGROUND

Dysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).

METHODS

Multiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.

RESULTS

A novel risk model was established using the LASSO regression algorithm, incorporating three genes (TFRC, SLC39A14, and ATP6V0D1).This model categorized patients into low and high-risk groups, and Kaplan-Meier analysis revealed significantly shorter progression-free survival for the high-risk group (P < 0.0001). The prognostic model's accuracy was additionally confirmed by employing time-dependent Receiver Operating Characteristic (ROC) curves and conducting Decision Curve Analysis (DCA). High-risk patients were found to correlate with advanced clinical stages, specific tumor microenvironment subtypes, and distinct morphologies. ESTIMATE analysis demonstrated a significant inverse relationship between increased immune, stromal, and ESTIMATE scores and lowered risk score. Immune analysis indicated a negative correlation between high-risk score and the abundance of most tumor-infiltrating immune cells, including dendritic cells, CD8+ T cells, CD4+ T cells, and B cells. This correlation extended to immune checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, and BTLA. The protein expression patterns of selected genes in clinical NPC samples were validated through immunohistochemistry.

CONCLUSION

This study presents a prognostic model utilizing IMRGs in NPC, which could assist in assessing patient prognosis and provide insights into new therapeutic targets for NPC.

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers.

METHODS

We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package.

RESULTS

Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633-0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420-2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17-2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness.

CONCLUSION

The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

Cancer metabolism and carcinogenesis

Metabolic reprogramming is an emerging hallmark of cancer cells, enabling them to meet increased nutrient and energy demands while withstanding the challenging microenvironment. Cancer cells can switch their metabolic pathways, allowing them to adapt to different microenvironments and therapeutic interventions. This refers to metabolic heterogeneity, in which different cell populations use different metabolic pathways to sustain their survival and proliferation and impact their response to conventional cancer therapies. Thus, targeting cancer metabolic heterogeneity represents an innovative therapeutic avenue with the potential to overcome treatment resistance and improve therapeutic outcomes. This review discusses the metabolic patterns of different cancer cell populations and developmental stages, summarizes the molecular mechanisms involved in the intricate interactions within cancer metabolism, and highlights the clinical potential of targeting metabolic vulnerabilities as a promising therapeutic regimen. We aim to unravel the complex of metabolic characteristics and develop personalized treatment approaches to address distinct metabolic traits, ultimately enhancing patient outcomes.

Unsupervised Hierarchical Clustering of Head and Neck Cancer Patients by Pre-Treatment Plasma Metabolomics Creates Prognostic Metabolic Subtypes

There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.

Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

BACKGROUND

Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic transcript-related CRC subtypes and relevant immune landscapes, and developed a metabolic risk score (MRS) for survival prediction.

METHODS

Metabolism-related genes were collected from the Molecular Signatures Database and metabolic subtypes were identified using an unsupervised clustering algorithm based on the expression profiles of survival-related metabolic genes in GSE39582. The ssGSEA and ESTIMATE methods were applied to estimate the immune infiltration among subtypes. The MRS model was developed using LASSO Cox regression in the GSE39582 dataset and independently validated in the TCGA CRC and GSE17537 datasets.

RESULTS

We identified two metabolism-related subtypes (cluster-A and cluster-B) of CRC based on the expression profiles of 539 survival-related metabolic genes with distinct immune profiles and notably different prognoses. The cluster-B subtype had a shorter OS and RFS than the cluster-A subtype. Eighteen metabolism-related genes that were mostly involved in lipid metabolism pathways were used to build the MRS in GSE39582. Patients with higher MRS had worse prognosis than those with lower MRS (HR 3.45, P < 0.001). The prognostic role of MRS was validated in the TCGA CRC (HR 2.12, P = 0.00017) and GSE17537 datasets (HR 2.67, P = 0.039). Time-dependent receiver operating characteristic curve and stratified analyses revealed the robust predictive ability of the MRS in each dataset. Multivariate Cox regression analysis indicted that the MRS could predict OS independent of TNM stage and age.

CONCLUSIONS

Our study provides novel insight into metabolic heterogeneity and its relationship with immune landscape in CRC. The MRS was identified as a robust prognostic marker and may facilitate individualized therapy for CRC patients.

A Novel Prognostic Signature Based on Metabolism-Related Genes to Predict Survival and Guide Personalized Treatment for Head and Neck Squamous Carcinoma

Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients’ prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group (p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group (p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score (p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.

A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. The TNM stage is not satisfactory for individualized prognostic assessment and it does not predict response to therapy. In the present study, we downloaded the gene expression profiles from TCGA database to establish a training set and GEO database for a validation set. In the training set, we developed an 10 immune-related genes signature which had superior predictive value compared with TNM stage. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature might act as an independent factor for predicting the survival of HNSCC patients. As for the immune microenvironment, our results showed higher immune checkpoint expression (CLTA-4 and PD-1) in low-risk group which might reflect a positive immunotherapy response. Thus, our signature not only provided a promising biomarker for survival prediction, but might be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.

Identification of a Transcriptional Prognostic Signature From Five Metabolic Pathways in Oral Squamous Cell Carcinoma

Dysregulated metabolic pathways have been appreciated to be intimately associated with tumorigenesis and patient prognosis. Here, we sought to develop a novel prognostic signature based on metabolic pathways in patients with primary oral squamous cell carcinoma (OSCC). The original RNA-seq data of OSCC from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) database were transformed into a metabolic pathway enrichment score matrix by single-sample gene set enrichment analysis (ssGSEA). A novel prognostic signature based on metabolic pathways was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in both testing and validation cohorts. The optimal cut-off value was obtained using the Youden index by receiver operating characteristic (ROC) curve. The overall survival curves were plotted by the Kaplan-Meier method. A time-dependent ROC curve analysis with 1, 3, 5 years as the defining point was performed to evaluate the predictive value of this prognostic signature. A 5-metabolic pathways prognostic signature (5MPS) for OSCC was constructed which stratified patients into subgroups with favorable or inferior survival. It served as an independent prognostic factor for patient survival and had a satisfactory predictive performance for OSCC. Our results developed a novel prognostic signature based on dysregulated metabolic pathways in OSCC and provided support for aberrant metabolism underlying OSCC tumorigenesis.

Gene Expression Clustering and Selected Head and Neck Cancer Gene Signatures Highlight Risk Probability Differences in Oral Premalignant Lesions

BACKGROUND

Oral premalignant lesions (OPLs) represent the most common oral precancerous conditions. One of the major challenges in this field is the identification of OPLs at higher risk for oral squamous cell cancer (OSCC) development, by discovering molecular pathways deregulated in the early steps of malignant transformation. Analysis of deregulated levels of single genes and pathways has been successfully applied to head and neck squamous cell cancers (HNSCC) and OSCC with prognostic/predictive implications. Exploiting the availability of gene expression profile and clinical follow-up information of a well-characterized cohort of OPL patients, we aim to dissect tissue OPL gene expression to identify molecular clusters/signatures associated with oral cancer free survival (OCFS).

MATERIALS AND METHODS

The gene expression data of 86 OPL patients were challenged with: an HNSCC specific 6 molecular subtypes model (Immune related: HPV related, Defense Response and Immunoreactive; Mesenchymal, Hypoxia and Classical); one OSCC-specific signature (13 genes); two metabolism-related signatures (3 genes and signatures raised from 6 metabolic pathways associated with prognosis in HNSCC and OSCC, respectively); a hypoxia gene signature. The molecular stratification and high versus low expression of the signatures were correlated with OCFS by Kaplan-Meier analyses. The association of gene expression profiles among the tested biological models and clinical covariates was tested through variance partition analysis.

RESULTS

Patients with Mesenchymal, Hypoxia and Classical clusters showed an higher risk of malignant transformation in comparison with immune-related ones (log-rank test, p = 0.0052) and they expressed four enriched hallmarks: "TGF beta signaling" "angiogenesis", "unfolded protein response", "apical junction". Overall, 54 cases entered in the immune related clusters, while the remaining 32 cases belonged to the other clusters. No other signatures showed association with OCFS. Our variance partition analysis proved that clinical and molecular features are able to explain only 21% of gene expression data variability, while the remaining 79% refers to residuals independent of known parameters.

CONCLUSIONS

Applying the existing signatures derived from HNSCC to OPL, we identified only a protective effect for immune-related signatures. Other gene expression profiles derived from overt cancers were not able to identify the risk of malignant transformation, possibly because they are linked to later stages of cancer progression. The availability of a new well-characterized set of OPL patients and further research is needed to improve the identification of adequate prognosticators in OPLs.