Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma

Systematic analysis of expression and prognostic significance for MCM family in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor in the world and has a poor prognosis. The family of minichromosome maintenance proteins (MCM) improves the stability of genome replication by inhibiting the rate of DNA replication in eukaryotic cells, thus, small changes in physiological MCM levels would increase the instability of gene replication and increase the incidence of tumor formation, most of which are significantly elevated in multiple cancers. However, the expression of different MCM families in HNSC and their prognostic value remain unclear.

METHODS

ONCOMINE and GEPIA databases were used to analyze the expression of MCMs in HNSC. The Kaplan-Meier plotter database was used to identify molecules with prognostic values. We collected 77 HNSC tissues and 50 normal tissues to validate the results of the bioinformatics analysis by immunohistochemical staining.

RESULTS

The expression of MCM3, MCM5 and MCM6 in mRNA and protein levels were higher in HNSC. Moreover, the increased expression of MCM3, MCM5 and MCM6 in mRNA and protein levels predicted better prognosis of HNSC patients. Furthermore, multivariate analysis showed that high expressions of MCM3, MCM5 and MCM6 in protein level may be independent prognostic factors for HNSC patients.

CONCLUSION

The results of this study indicated that MCM3, MCM5 and MCM6 play an important role in occurrence and development in HNSC and might be risk factors for the survival of HNSC patients.

Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment

Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recurrence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2-year survival (P=0.025) compared with non-diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recurrence and improved survival compared with non-diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treat diabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigate the impact of diabetes in other SCCHN subtypes.

A novel immune-related prognostic model with surgical status to predict tumor immune cell infiltration and drug sensitivity in head and neck squamous cell carcinoma

Tumor-infiltrating immune cells (TICs) affect tumorigenesis and tumor development in head and neck squamous cell carcinoma (HNSCC). We constructed a novel predictive model for HNSCC based on immune-related genes (IRGs) from The Cancer Genome Atlas and the Immunology Database and Analysis Portal. After identifying the IRGs, a predictive model involving 13 IRGs with high stratification value of overall survival (OS) was constructed by multiple support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression. We explored the relationship between the risk score (RS) and clinical characteristics. The nomogram showed high concordance and good agreement in OS. Four TICs affected the OS and were in agreement with the abundance analysis of the RS levels. Furthermore, the low-risk HNSCC group showed higher expression of PD-1, CTLA4, and TIGIT, while the high-risk group showed higher expression of EGFR. The high-risk HNSCC showed high sensitivity to eight drugs.

Special transcriptome landscape and molecular prognostic signature of non-smoking head and neck cancer patients

As a well-known behavioral risk factor for human health, smoking is involved in carcinogenesis, tumor progression, and therapeutic interventions of head and neck squamous cell carcinoma (HNSCC). The stratification of disease subtypes according to tobacco use is expressively needed for HNSCC precision therapy. High-throughput transcriptome profiling by RNA sequencing (RNA-seq) from The Cancer Genome Atlas (TCGA) was collected and collated for differential expression analysis and pathway enrichment analysis to characterize the molecular landscape for non-smoking HNSCC patients. Molecular prognostic signatures specific to non-smoking HNSCC patients were identified by the least absolute shrinkage and selection operator (LASSO) analysis and were then verified via internal and external validation cohorts. While proceeding to immune cell infiltration and after drug sensitivity analysis was further carried out, a proprietary nomogram was finally developed for their respective clinical applications. In what it relates to the non-smoking cohort, the enrichment analysis pointed to human papillomavirus (HPV) infection and PI3K-Akt signaling pathway, with the prognostic signature consisting of another ten prognostic genes (COL22A1, ADIPOQ, RAG1, GREM1, APBA2, SPINK9, SPP1, ARMC4, C6, and F2RL2). These signatures showed to be independent factors, and the related nomograms were, thus, constructed for their further and respective clinical applications. While the molecular landscapes and proprietary prognostic signature were characterized based on non-smoking HNSCC patients, a clinical nomogram was constructed to provide better HNSCC patient classification and guide treatment for non-smoking HNSCC patients. Nonetheless, there are still significant challenges in the recognition, diagnosis, treatment, and understanding of the potentially efficient mechanisms of HNSCC with no tobacco use.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer

Background

Esophageal cancer (EC) had the sixth-highest mortality rate of all cancers due to its poor prognosis. Immune cells and mutation genes influenced the prognosis of EC, but their combined effect on predicting EC prognosis was unknown. In this study, we comprehensively analyzed the immune cell infiltration (ICI) and mutation genes and their combined effects for predicting prognosis in EC.

Methods

The CIBERSORT and ESTIMATE algorithms were used to analyse the ICI scape based on the TCGA and GEO databases. EC tissues and pathologic sections from Huai'an, China, were used to verify the key immune cells and mutation genes and their interactions.

Results

Stromal/immune score patterns and ICI/gene had no statistical significance in overall survival (OS) (p > 0.05). The combination of ICI and tumor mutation burden (TMB) showed that the high TMB and high ICI score group had the shortest OS (p = 0.004). We recognized that the key mutation gene NRF2 was significantly different in the high/low ICI score subgroups (p = 0.002) and positivity with mast cells (MCs) (p < 0.05). Through experimental validation, we found that the MCs and activated mast cells (AC-MCs) were more infiltration in stage II/III (p = 0.032; p = 0.013) of EC patients and that NRF2 expression was upregulated in EC (p = 0.045). AC-MCs combined with NRF2 had a poor prognosis, according to survival analysis (p = 0.056) and interactive analysis (p = 0.032).

Conclusions

We presume that NRF2 combined with AC-MCs could be a marker to predict prognosis and could influence immunotherapy through regulating PD-L1 in the EC.

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.

A prognostic signature model for unveiling tumor progression in lung adenocarcinoma

A more accurate prognosis is important for clinical treatment of lung adenocarcinoma. However, due to the limitation of sample and technical bias, most prognostic signatures lacked reproducibility, and few were applied to clinical practice. In addition, understanding the molecular driving mechanism is indispensable for developing more promising therapies for lung adenocarcinoma. Here, we built an unbiased prognostic significance model to perform an integrative analysis, including differentially expressed genes and clinical data with lung adenocarcinoma patients from TCGA. Multivariable Cox proportional hazards model with the Lasso penalty and 10-fold cross-validate were used to identify the best gene signature. We generated a 17-gene signature for prognostic risk prediction based on the overall survival time of lung adenocarcinoma patients. To further test the model's predictive ability, we have applied an independent GEO database to verify the predictive ability of prognostic signature. The model can more objectively describe several biological processes related to tumors and reveal important molecular mechanisms in tumor development by GO and KEGG analysis. Furthermore, differential expression analysis by GSEA revealed that tumor microenvironments such as ER stress, exosome, and immune microenvironment were enriched. Using single-cell RNA sequence technology, we found that risk score was positively correlated with lung adenocarcinoma marker genes and copy number variation but negatively correlated with lung epithelial marker genes. High-risk cell populations with the model had stronger cancer stemness and tumor-related pathway activation. As we expected, the risk score was in accordance with the malignancy of each cluster from tumor progression. In conclusion, the risking model established in this study is more reliable than others in evaluating the prognosis of LUAD patients.

Spermine synthase (SMS) serves as a prognostic biomarker in head and neck squamous cell carcinoma: a bioinformatics analysis

Background

Head and neck squamous cell carcinoma (HNSC) is an aggressive type of cancer that lacks early detection, and therefore, has a low 5-year survival rate. The spermine synthase (SMS) gene has been shown to be associated with Snyder-Robinson syndrome and poor prognosis of multiple cancers; however, its regulatory role in HNSC has never been investigated. Therefore, we explored the potential predictive value of SMS in HNSC.

Methods

We explored the association between SMS expression and clinicopathological parameters of HNSC patients by using data from The Cancer Genome Atlas datasets (TCGA). The prognostic value of SMS was evaluated using the Kaplan-Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA) 2 and univariate and multivariate Cox regression analyses. We further used gene set enrichment analysis (GESA) to investigate the potential roles of SMS in HNSC prognosis and Tumor Immunity Estimation Resource 2.0 (TIMER2.0) to analyze the correlation between immune cell infiltration and SMS expression. Finally, starBase was used to screen out prognosis-associated non-coding RNA genes to constructed the competing endogenous RNA (ceRNA) network. Co-expression and survival analyses were used to identify the ceRNA network's effect on HNSC prognosis.

Results

We found that SMS expression was increased in HNSC compared with normal tissues (P<0.05). In addition, SMS expression was associated with tumor grade (P=0.006), N stage (P=0.001), and prognosis. Survival analysis revealed that high expression of SMS showed worse overall survival (OS) (HR =1.4, P=0.01) and worse disease-free survival (DFS) (HR =1.5, P=0.014). Multivariate Cox analysis further supported the prognostic value of SMS in HNSC (HR =1.006636, P=0.0056). GESA showed that SMS was involved in metabolism- and immune-related pathways. The immune infiltration analyses results showed a decrease in the landscape of immune cell infiltration with high SMS expression and SMS deletion in HNSC. Finally, a ceRNA network (SMS/hsa-miR-23b-3p/KTN1-AS1 and VPS9D1-AS axis) was constructed based on the co-expression and survival analyses in HNSC.

Conclusions

Our findings first revealed that SMS functioned as a potential prognostic biomarker and provide insights into the molecular mechanisms of its function in HNSC. The use of SMS may be powerful for determining worse prognosis HNSC patients.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

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.

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Background

Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.

Methods

In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.

Results

We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.

Conclusions

Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02493-2.

Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

Background

Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.

Methods

Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.

Results

A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.

Conclusion

A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.

Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images

OBJECTIVE

This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer.

METHODS

Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7.

CONCLUSIONS

Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.

Identification of Autophagy- and Ferroptosis-Related lncRNAs Functioned through Immune-Related Pathways in Head and Neck Squamous Carcinoma

The interplay between autophagy and ferroptosis has been highlighted as an important event to decide cancer cell fate. However, the underlying mechanisms remain largely unclear. In this study, we systematically explored the expression, prognostic value and functional roles of lncRNA in autophagy and ferroptosis. By a set of bioinformatics analyses, we identified 363 autophagy- and ferroptosis-related lncRNAs (AF-lncRNAs) and found 17 of them are dramatically related to the prognosis of head and neck squamous cell carcinoma (HNSC) patients, named as prognosis-related AF-lncRNAs (PAF-lncRNAs). Based on six key PAF-lncRNAs, a risk score model was developed and used to categorize the TCGA-retrieved HNSC patients into two groups (high-risk vs. low-risk). Functional analysis showed the differentially expressed genes (DEGs) between the two groups were mainly enriched in immune-related pathways and regulated by a PAF-lncRNA-directed ceRNA (competitive endogenous RNA) network. Combined with a variety of immune infiltration analyses, we also found a decreased landscape of immune cell infiltration in high-risk groups. Together, by revealing PAF-lncRNAs with tumor prognostic features functioned through immune-related pathways, our work would contribute to show the pathogenesis of a lncRNA-directed interplay among autophagy, ferroptosis and tumor immunity in HNSC and to develop potential prognostic biomarkers and targets for tumor immunotherapy.

An Epithelial-Mesenchymal Transition Hallmark Gene-Based Risk Score System in Head and Neck Squamous-Cell Carcinoma

Background

Epithelial-to-mesenchymal transition (EMT) program plays a critical role in cancer. Thus, we attempted to generate a risk score system according to the expression pattern of different EMT hallmark genes in head and neck squamous-cell carcinoma (HNSC).

Methods

Differentially expressed EMT hallmark genes were screened to generate a risk score (RS) on TCGA HNSC dataset. The relative prognostic value of the RS compared to clinicopathological characteristics was explored using multivariable Cox analysis. Functional enrichment analysis was performed to reveal the biological characteristics. An external dataset was applied to validate the prognostic value of the RS.

Results

Nine genes constituted the EMT hallmark gene-based RS, which is significantly associated with poor prognosis and could successfully divide patients with HNSC into high- and low-risk groups. The RS was also an independent prognostic indicator compared to routine clinical factors.

Conclusion

We proposed and validated a nine-EMT hallmark gene-based risk score system in HNSC.

Tumor and Peripheral Immune Status in Soft Tissue Sarcoma: Implications for Immunotherapy

Simple Summary

Soft Tissue Sarcomas are a rare and heterogeneous group of tumors, which have a characteristic complexity, leading to a difficult diagnosis and a lack of response to treatment. The aim of this review is to summarize the role of immune cells, soluble plasmatic factors, immune checkpoints; and the expression of immune-related genes predicting survival, response to therapy, and potential immunotherapeutic agents or targets in Soft Tissue Sarcomas.

Abstract

Soft Tissue Sarcomas (STS) are a heterogeneous and rare group of tumors. Immune cells, soluble factors, and immune checkpoints are key elements of the complex tumor microenvironment. Monitoring these elements could be used to predict the outcome of the disease, the response to therapy, and lead to the development of new immunotherapeutic approaches. Tumor-infiltrating B cells, Natural Killer (NK) cells, tumor-associated neutrophils (TANs), and dendritic cells (DCs) were associated with a better outcome. On the contrary, tumor-associated macrophages (TAMs) were correlated with a poor outcome. The evaluation of peripheral blood immunological status in STS could also be important and is still underexplored. The increased lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR), higher levels of monocytic myeloid-derived suppressor cells (M-MDSCs), and Tim-3 positive CD8 T cells appear to be negative prognostic markers. Meanwhile, NKG2D-positive CD8 T cells were correlated with a better outcome. Some soluble factors, such as cytokines, chemokines, growth factors, and immune checkpoints were associated with the prognosis. Similarly, the expression of immune-related genes in STS was also reviewed. Despite these efforts, only very little is known, and much research is still needed to clarify the role of the immune system in STS.

Establishment and Validation of a Comprehensive Prognostic Model for Patients With HNSCC Metastasis

Objective

To identify biomarkers related to head and neck squamous cell carcinoma (HNSCC) metastasis and establish a prognostic model for patients with HNSCC.

Methods

HNSCC mRNA expression data of metastasis and non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. After screening the differentially expressed genes (DEGs) in the two datasets, a prognostic model, including clinical factors and biomarkers, was established, and verified in 36 samples of HNSCC by quantitative real-time transcription (qRT)-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene sets enrichment analysis (GSEA) were consulted to explore the functions of the DEGs.

Results

In total, 108 DEGs were identified. GSEA, GO, and KEGG analyses showed that these DEGs were mainly involved in the proliferation and metastasis of HNSCC. Six genes that were significantly related to metastasis, immune cell infiltration and prognosis were further identified to construct a prognostic gene signature. The reliability of the gene signature was verified in 36 samples of HNSCC. A prognostic model, including tumor stage, risk level, and a nomogram for prediction were further established. Receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), C-index, and calibration plots showed that the model and nomogram perform well.

Conclusion

We constructed a six-gene signature and a nomogram with high performance in predicting the prognosis of patients with HNSCC metastasis.

Immunotherapy Advances in Locally Advanced and Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma and Its Relationship With Human Papillomavirus

Head and neck cancer (HNC) is the sixth most common malignancy worldwide; head and neck squamous cell carcinoma (HNSCC) account for the most cases of HNC. Past smoking and alcohol consumption are common risk factors of HNSCC; however, an increasing number of cases associated with human papillomavirus (HPV) infection have been reported in recent years. The treatment of HNSCC is integrated and multimodal including traditional surgery, radiotherapy, chemotherapy, and targeted therapy. Since pembrolizumab was approved in 2016, an increasing number of studies have focused on immunotherapy. However, not all of HNSCC patients have a better outcome on immunotherapy. Immunotherapy has been reported to be more effective in HPV-positive patients, but its molecular mechanism is still unclear. Some researchers have proposed that the high proportion of infiltrating immune cells in HPV-positive tumors and the difference in immune checkpoint expression level may be the reasons for their better response. As a result, a series of individualized immunotherapy trials have also been conducted in HPV-positive patients. This paper summarizes the current status of HNSCC immunotherapy, individualized immunotherapy in HPV-positive patients, and immune differences in HPV-positive tumors to provide new insights into HNSCC immunotherapy and try to identify patients who may benefit from immunotherapy.

Development and validation of a novel ferroptosis-related gene signature for predicting prognosis and immune microenvironment in head and neck squamous cell carcinoma

Ferroptosis plays an important role across variable cancer types. However, few studies have focused on the prognostic patterns of ferroptosis-related genes in HNSCC. Cohorts with mRNA expression profiles, as well as corresponding clinical data of HNSCC patients from published studies, were collected and consolidated from public databases. We performed random survival forest analysis, Kaplan-Meier (KM) analysis of best combinations, and Cox regression analysis on 231 ferroptosis-related genes to construct a gene signature in the discovery cohort (TCGA), and later validated it in the validation cohort (GEO). The 7-gene signature was constructed to stratify patients into two groups according to their level of risk. Poorer overall survival (OS) was detected in the high risk (HRisk) group than in the low risk (LRisk) group in both the TCGA cohort (P < 0.0001, HR = 1.71, 95%CI:1.41-2.07) and the GEO cohort (P < 0.001, HR = 1.68, 95%CI:1.32-2.13). The risk score was identified as an independent predictive factor of OS in multivariate Cox regression analyses (HR > 1, P < 0.0001) in both cohorts. The signature's predictive capacity was proven by the time-dependent receiver operating characteristic (ROC) curve analysis and nomogram analysis. Functional enrichment analysis revealed that immunosuppressive pathways such as matrix extracellular space, and (transforming growth factor-β)TGF-β were enriched. The HRisk group was strongly associated with upregulation of both cancer-related pathways and stromal scores, while higher proportions of anti-tumor immune cells and immune signatures were enriched in the LRisk group. In conclusion, the signature based on 7 ferroptosis-related genes could be applicable for predicting the prognosis of HNSCC, indicating that ferroptosis may be a potential therapeutic target for HNSCC.

Immune-Related Mutational Landscape and Gene Signatures: Prognostic Value and Therapeutic Impact for Head and Neck Cancer

Simple Summary

Immunotherapy has emerged as a standard-of-care for most human malignancies, including head and neck cancer, but only a limited number of patients exhibit a durable clinical benefit. An urgent medical need is the establishment of accurate response predictors, which is handicapped by the growing body of molecular, cellular and clinical variables that modify the complex nature of an effective anti-tumor immune response. This review summarizes more recent efforts to elucidate immune-related mutational landscapes and gene expression signatures by integrative analysis of multi-omics data, and highlights their potential therapeutic impact for head and neck cancer. A better knowledge of the underlying principles and relevant interactions could pave the way for rational therapeutic combinations to improve the efficacy of immunotherapy, in particular for those cancer patients at a higher risk for treatment failure.

Abstract

Immunotherapy by immune checkpoint inhibition has become a main pillar in the armamentarium to treat head and neck cancer and is based on the premise that the host immune system can be reactivated to successfully eliminate cancer cells. However, the response rate remains low and only a small subset of head and neck cancer patients achieves a durable clinical benefit. The availability of multi-omics data and emerging computational technologies facilitate not only a deeper understanding of the cellular composition in the tumor immune microenvironment but also enables the study of molecular principles in the complex regulation of immune surveillance versus tolerance. These knowledges will pave the way to apply immunotherapy more precisely and effectively. This review aims to provide a holistic view on how the immune landscape dictates the tumor fate and vice versa, and how integrative analysis of multi-omics data contribute to our current knowledge on the accuracy of predictive biomarkers and on a broad range of factors influencing the response to immunotherapy in head and neck cancer.