Overexpressed PLAU and its potential prognostic value in head and neck squamous cell carcinoma

Identification of vilazodone as a novel plasminogen activator inhibitor to overcome Alzheimer's disease through virtual screening, molecular dynamics simulation, and biological evaluation

Urokinase-type plasminogen activator (PLAU), a member of the S1 serine peptidase family in Clan PA, plays a crucial role in the conversion of plasminogen into active plasmin. However, the precise role of PLAU in the central nervous system remains incompletely elucidated, particularly, in relation to Alzheimer's disease (AD). In this study, we successfully identified that PLAU could promote cell senescence in neurons, indicating it as a potential target for AD treatment through a systematic approach, which included both bioinformatics analysis and experimental verification. Subsequently, a structure-based virtual screening approach was employed to identify a potential PLAU inhibitor from the Food and Drug Administration-approved drug database. After analyzing docking scores and thoroughly examining the receptor-ligand complex interaction modes, vilazodone emerges as a highly promising PLAU inhibitor. Additionally, molecular docking and molecular dynamics simulations were performed to generate a complex structure between the relatively stable inhibitor vilazodone and PLAU. Of note, vilazodone exhibited superior cytotoxicity against senescent cells, showing a senolytic activity through targeting PLAU and ultimately producing an anti-AD effect. These findings suggest that targeting PLAU could represent a promising therapeutic strategy for AD. Furthermore, investigating the inhibitory potential and structural modifications based on vilazodone may provide valuable insights for future drug development targeting PLAU in AD disorders.

Identification of drug targets for Sjögren's syndrome: multi-omics Mendelian randomization and colocalization analyses

Background

Targeted therapy for Sjögren's syndrome (SS) has become an important focus for clinicians. Multi-omics-wide Mendelian randomization (MR) analyses have provided new ideas for identifying potential drug targets.

Methods

We conducted summary-data-based Mendelian randomization (SMR) analysis to evaluate therapeutic targets associated with SS by integrating DNA methylation, gene expression and protein quantitative trait loci (mQTL, eQTL, and pQTL, respectively). Genetic associations with SS were derived from the FinnGen study (discovery) and the GWAS catalog (replication). Colocalization analyses were employed to determine whether two potentially relevant phenotypes share the same genetic factors in a given region. Moreover, to delve deeper into potential regulation among DNA methylation, gene expression, and protein abundance, we conducted MR analysis to explore the causal relationship between candidate gene methylation and expression, as well as between gene expression and protein abundance. Drug prediction and molecular docking were further employed to validate the pharmacological activity of the candidate drug targets.

Results

Upon integrating the multi-omics data, we identified three genes associated with SS risk: TNFAIP3, BTN3A1, and PLAU. The methylation of cg22068371 in BTN3A1 was positively associated with protein levels, consistent with the negative effect of cg22068371 methylation on the risk of SS. Additionally, positive correlations were observed between the gene methylation of PLAU (cg04939496) and expression, as well as between expression and protein levels. This consistency elucidates the promotional effects of PLAU on SS risk at the DNA methylation, gene expression, and protein levels. At the protein level, genetically predicted TNFAIP3 (OR 2.47, 95% CI 1.56-3.92) was positively associated with SS risk, while BTN3A1 (OR 2.96E-03, 95% CI 2.63E-04-3.33E-02) was negatively associated with SS risk. Molecular docking showed stable binding for candidate drugs and target proteins.

Conclusion

Our study reveals promising therapeutic targets for the treatment of SS, providing valuable insights into targeted therapy for SS. However, further validation through future experiments is warranted.

Oral squamous cell carcinoma gene patterns connected with RNA methylation for prognostic prediction

OBJECTIVES

To determine whether m6A/m1A/m5C/m7G/m6Am/Ψ-related genes influence the prognosis of a patient with oral squamous cell carcinoma.

MATERIALS AND METHODS

We investigated the changes in regulatory genes using publicly available data from The Cancer Genome Atlas. Consensus clustering by RNA methylation-related regulators was used to describe oral squamous cell carcinomas (OSCCs). Then, we developed the prediction model. The tumor microenvironment was investigated using ESTIMATE. Gene set enrichment analysis was used to determine whether pathways or cell types were enriched in different groups. The association between the model and immune-related risk scores was investigated using correlation analysis.

RESULTS

We found 22 gene signatures in this analysis and then developed a predictive model that reveals the genes that are highly connected to the overall survival of OSCC patients. The survival and death rates were substantially different in the two groups (high and low risk) classified by the risk scores. The validation cohort verified the phenotypic diversity and prognostic effects of these genes.

CONCLUSION

Our data reveal that immune cell infiltration, genetic mutation, and survival potential in OSCC patients are linked to m6A/m1A/m5C/m7G/m6Am/Ψ-related genes, and we constructed a dependable prognostic model for OSCC patients.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan-Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

Transcriptomic analysis of Porphyromonas gingivalis-infected head and neck cancer cells: Identification of PLAU as a candidate prognostic biomarker

Periodontal disease is a risk factor for head and neck squamous cell carcinoma (HNSCC), and Porphyromonas gingivalis, a major periodontal pathogen, has been identified as a specific and potentially independent microbial factor that increases the risk of cancer mortality. Gene expression in HNSCC due to P. gingivalis infection and how changes in gene expression affect the prognosis of HNSCC patients are not clarified. When P. gingivalis was cultured with HNSCC cells, it efficiently adhered to these cells and enhanced their invasive ability. A transcriptome analysis of P. gingivalis -infected HNSCC cells showed that genes related to migration, including CCL20, CITED2, CTGF, C8orf44-SGK3, DUSP10, EGR3, FUZ, HBEGF, IL1B, IL24, JUN, PLAU, PTGS2, P2RY1, SEMA7A, SGK1 and SIX2, were highly up- or down-regulated. The expression of up-regulated genes was examined using the expression data of HNSCC patients obtained from The Cancer Genome Atlas (TCGA) database, and the expression of 5 genes, including PLAU, was found to be higher in cancer tissue than in solid normal tissue. An analysis of protein-protein interactions revealed that these 5 genes formed a dense network. A Cox regression analysis showed that high PLAU expression levels were associated with a poor prognosis in patients with TCGA-HNSCC. Furthermore, the prognostic impact correlated with tumour size and the presence or absence of lymph node metastasis. Collectively, these results suggest the potential of PLAU as a molecular prognostic marker in HNSCC patients. Further in vivo and in vitro studies are needed to verify the findings of this study.

Comprehensive Investigation of m6A Regulators for Prognosis in Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) has an important impact on the clinical prognosis. N6-methyladenosine (m6A) is involved in the post-transcriptional regulation of tumorigenesis and development. In this study, the prognosis and biological functions of m6A regulator targets in HNSCC were explored. RNA-Seq expression data and clinical information from TCGA-HNSCC and GSE23036 datasets were collected. The mRNA levels of IGF2BP2 and IGF2BP3 in tumor tissues were significantly up-regulated. Differential expression and functional enrichment analysis of potential targets for IGF2BP2 and IGF2BP3 obtained from the m6A2Target database showed that they were significantly enriched in cell cycle-related pathways. The Cox regression analysis was performed to establish a three-mRNA signature including PLAU, LPIN1 and AURKA. The prognostic effect was verified in the external dataset GSE41613. Further studies revealed that the three-mRNA signature was significantly associated with survival in the clinical subgroup. The ROC curve, Harrell consistency index and decision curve comparison used to compare the predictive effect of the three-mRNA signature and the other signatures in previous studies showed that the three-mRNA signature had better predictive effect on the prognosis of HNSCC patients. The three-mRNA signature expression were verified in HNSCC cell lines with qRT-PCR and Western blot. Sequence analysis showed that m6A-modification sites existed on PLAU, LPIN1 and AURKA genes. In conclusion, the three-mRNA signature has been proved to be useful on evaluating the prognosis and contributing to the personalized treatment of HNSCC, and IGB2BP2/3 were related to the cell cycle in HNSCC.

Integrated analysis of Dendrobium nobile extract Dendrobin A against pancreatic ductal adenocarcinoma based on network pharmacology, bioinformatics, and validation experiments

Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Identification of therapeutically potential targets and their ligands for the treatment of OSCC

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.

Identification of Molecular Targets and Potential Mechanisms of Yinchen Wuling San Against Head and Neck Squamous Cell Carcinoma by Network Pharmacology and Molecular Docking

Head and neck squamous cell carcinoma (HNSCC) represents one of the most malignant and heterogeneous tumors, and the patients have low 5-year survival. Traditional Chinese medicine (TCM) has been demonstrated as an effective complementary and/or alternative therapy for advanced malignancies including HNSCC. It has been noted that several herbs that are used for preparing Yinchen Wuling San (YWLS) have anti-tumor activities, whereas their mechanisms of action remain elusive. In this study, network pharmacology and molecular docking studies were employed to explore the underlying mechanisms of action of YWLS against HNSCC. The 58 active ingredients from six herbs used for YWLS and their 506 potential targets were screened from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and SwissTargetPrediction database. A total of 2,173 targets associated with HNSCC were mainly identified from the DisGeNET and GeneCards databases. An active components-targets-disease network was constructed in the Cytoscape. Top 20 hub targets, such as AKT1, EGFR, TNF, ESR1, SRC, HSP90AA1, MAPK3, ERBB2, and CCND1, were identified by a degree in the protein–protein interaction (PPI) network. Gene functional enrichment analysis showed that PI3K-AKT, MAPK, Ras, TNF, and EGFR were the main signaling pathways of YWLS in treating HNSCC. There were 48 intersected targets such as EGFR, AKT1, and TNF that were associated with patients’ outcomes by the univariate Cox analysis, and most of them had increased expression in the tumor as compared to normal tissues. The area under curves of receiver operating characteristic indicated their diagnostic potential. Inhibition of these survival-related targets and/or combination with EGFR or AKT inhibitors were promising therapeutic options in HNSCC. The partial active components of YWLS exhibited good binding with the hub targets, and ADME analysis further evaluated the drug-likeness of the active components. These compounds and targets identified in this study might provide novel treatment strategies for HNSCC patients, and the subsequent work is essential to verify the underlying mechanisms of YWLS against HNSCC.

Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis

Background

Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood.

Methods

Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman’s analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype.

Result

Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein–protein interaction network construction, and Spearman’s correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis.

Conclusion

We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.

Transcriptomic Biomarker Signatures for Discrimination of Oral Cancer Surgical Margins

Relapse after surgery for oral squamous cell carcinoma (OSCC) contributes significantly to morbidity, mortality and poor outcomes. The current histopathological diagnostic techniques are insufficiently sensitive for the detection of oral cancer and minimal residual disease in surgical margins. We used whole-transcriptome gene expression and small noncoding RNA profiles from tumour, close margin and distant margin biopsies from 18 patients undergoing surgical resection for OSCC. By applying multivariate regression algorithms (sPLS-DA) suitable for higher dimension data, we objectively identified biomarker signatures for tumour and marginal tissue zones. We were able to define molecular signatures that discriminated tumours from the marginal zones and between the close and distant margins. These signatures included genes not previously associated with OSCC, such as MAMDC2, SYNPO2 and ARMH4. For discrimination of the normal and tumour sampling zones, we were able to derive an effective gene-based classifying model for molecular abnormality based on a panel of eight genes (MMP1, MMP12, MYO1B, TNFRSF12A, WDR66, LAMC2, SLC16A1 and PLAU). We demonstrated the classification performance of these gene signatures in an independent validation dataset of OSCC tumour and marginal gene expression profiles. These biomarker signatures may contribute to the earlier detection of tumour cells and complement existing surgical and histopathological techniques used to determine clear surgical margins.

The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Identification of Immune-Related Risk Signatures for the Prognostic Prediction in Oral Squamous Cell Carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, which remains a major cause of morbidity and mortality in patients with head and neck cancers. However, the critical immune-related signatures and their prognostic values have rarely been investigated.

Materials and Methods

Gene differential analysis was used to measure the differences of gene expression between the groups. Correlation analysis was used to assess the association between the gene expression levels and immune-related risk score/DNA methylation levels. The gene set enrichment analysis (GSEA) was used to identify the pathways or cell types enriched by those identified differentially expressed genes (DEGs).

Results

In this study, we identified four immune-related gene signatures, including CTSG, TNFRSF4, LCORL, and PLAU, that were significantly associated with the overall survival in OSCC patients from the Cancer Genome Atlas (TCGA) OSCC cohort. Moreover, these four immune-related signatures were differentially expressed between the OSCC and nontumor tissues. The two groups (high and low risk) stratified by the immune-related risk scores had significantly different OS and mortality rates. The gene expression patterns and prognostic values of these immune-related signatures were also verified in two independent validation cohorts. Furthermore, the downregulated genes in the high-risk group (which were also upregulated in the low-risk group) were significantly enriched in the cell type-specific signatures of type 2 T helper cell (Th2), plasmacytoid dendritic cell (pDC), and memory B cell. In contrast, the upregulated genes in the high-score group were enriched in growth factor receptor-related signaling pathways, such as the VEGFA-VEGFR2 signaling pathway, PI3K-Akt signaling pathway, focal adhesion-PI3K-Akt-mTOR signaling pathway, and PDGF pathway, suggesting that those pathways were inversely correlated with immune cell infiltration.

Conclusion

In summary, the immune-related signatures had the potential for predicting the risk of OSCC patients. Moreover, the present study also improved our understanding of the association between the growth factor receptor pathways and immune cell infiltration in OSCC.

Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis

Background

Tumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).

Methods

“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value <0.01, LogFC>2 or <-2 were included for further analyses. The function analysis of 250 overlapping mRNAs was shown by DAVID and Metascape software. By UALCAN, Oncomine and R packages, we explored the expression levels, survival analyses of CDK2 in 33 cancers. “Survival,” “survminer,” “rms” R packages were used to construct a Nomogram model of age, gender, stage, T, M, N. Univariate and multivariate Cox regression were used to establish prognosis-related immune forecast model in LUAD. CeRNA network was constructed by various online databases. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore correlations between CDK2 expression and IC50 of anti-tumor drugs.

Results

A total of 250 differentially expressed genes (DEGs) were identified to participate in many cancer-related pathways, such as activation of immune response, cell adhesion, migration, P13K-AKT signaling pathway. The target molecule CDK2 had prognostic value for the survival of patients in LUAD (P = 5.8e-15). Through Oncomine, TIMER, UALCAN, PrognoScan databases, the expression level of CDK2 in LUAD was higher than normal tissues. Pan-cancer analysis revealed that the expression, stage and survival of CDK2 in 33 cancers, which were statistically significant. Through TISIDB database, we selected 13 immunodepressants, 21 immunostimulants associated with CDK2 and explored 48 genes related to these 34 immunomodulators in cBioProtal database (P < 0.05). Gene Set Enrichment Analysis (GSEA) and Metascape indicated that 49 mRNAs were involved in PUJANA ATM PCC NETWORK (ES = 0.557, P = 0, FDR = 0), SIGNAL TRANSDUCTION (ES = –0.459, P = 0, FDR = 0), immune system process, cell proliferation. Forest map and Nomogram model showed the prognosis of patients with LUAD (Log-Rank = 1.399e-08, Concordance Index = 0.7). Cox regression showed that four mRNAs (SIT1, SNAI3, ASB2, and CDK2) were used to construct the forecast model to predict the prognosis of patients (P < 0.05). LUAD patients were divided into two different risk groups (low and high) had a statistical significance (P = 6.223e-04). By “survival ROC” R package, the total risk score of this prognostic model was AUC = 0.729 (SIT1 = 0.484, SNAI3 = 0.485, ASB2 = 0.267, CDK2 = 0.579). CytoHubba selected ceRNA mechanism medicated by potential biomarkers, 6 lncRNAs-7miRNAs-CDK2. The expression of CDK2 was associated with IC50 of 89 antitumor drugs, and we showed the top 20 drugs with P < 0.05.

Conclusion

In conclusion, our study identified CDK2 related immune forecast model, Nomogram model, forest map, ceRNA network, IC50 of anti-tumor drugs, to predict the prognosis and guide targeted therapy for LUAD patients.

A Novel Immune-Related Prognostic Signature in Head and Neck Squamous Cell Carcinoma

The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients’ OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.

Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

The study of IRGPs to construct the prognostic signature in head and neck squamous cell carcinoma (HNSCC) has not yet elucidated. The objective of this study was to explore a novel model to predict the prognosis of HNSCC patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were set as training and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) and time-dependent ROC were employed to screen the highest frequency immune-related gene pairs (IRGPs) and their best cut-off value. Survival analysis, Cox regression analysis were applied to discover the effects of selected IRGPs signature on survival outcomes. The immune cell proportions were deconvoluted by the CIBERSORT method. After a couple of filtering, we obtained 22 highest frequency IRGPs. The overall survival time of HNSCC patients with a high score of IRGPs was shorter as compared to the ones with a low score in two independent datasets (P < 0.001). Six kinds of immune cells were found to be differentially distributed in the two different risk groups of HNSCC patients (P < 0.001). GO and GSEA analysis showed these differentially expressed genes enriched in multiple molecular functions. The new IRGPs signature probably confers a new insight into the prognosis prediction of HNSCC patients.

PLAU Promotes Cell Proliferation and Epithelial-Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma

Plasminogen activator, urokinase (uPA) is a secreted serine protease whose Dysregulation is often accompanied by various cancers. However, the biological functions and potential mechanisms of PLAU in head and neck squamous cell carcinoma (HNSCC) remain undetermined. Here, the expression, prognosis, function, and coexpression genetic networks of PLAU in HNSCC were investigated by a series of public bioinformatics tools. A Higher PLAU level predicted a poorer clinical outcome. Meanwhile, functional network analysis implied that PLAU and associated genes mainly regulated cell-substrate adhesion, tissue migration, and extracellular matrix binding. The top 4 significantly associated genes are C10orf55, ITGA5, SERPINE1, and TNFRSF12A. Pathway enrichment analysis indicated that PLAU might activate the epithelial-to-mesenchymal transition (EMT) process, which could explain the poor prognosis in HNSCC. Besides, genes associated with PLAU were also enriched in EMT pathways. We further validated the bioinformatics analysis results by in vivo and in vitro experiments. Then, we found that much more PLAU was detected in HNSCC tissues, and the silencing of PLAU inhibit the proliferation, migration, and EMT process of CAL27 cell lines. Notably, the downregulation of PLAU decreased the expression of TNFRSF12A. Moreover, knockdown TNFRSF12A also inhibits cell proliferation and migration. In vivo experiment results indicated that PLAU inhibition could suppress tumor growth. Collectively, PLAU is necessary for tumor progression and can be a diagnostic and prognostic biomarker in HNSCC.

A Combined Prediction Model for Lymph Node Metastasis Based on a Molecular Panel and Clinicopathological Factors in Oral Squamous Cell Carcinoma

Objective

Lymph node metastasis is the most important factor influencing the prognosis of oral squamous cell carcinoma (OSCC) patients. However, there is no proper method for predicting lymph node metastasis. This study aimed to construct and validate a preoperative prediction model for lymph node metastasis and guide personalized neck management based on the gene expression profile and clinicopathological parameters of OSCC.

Methods

Based on a previous study of related genes in OSCC, the mRNA expression of candidate genes was evaluated by real-time PCR in OSCC specimens. In this retrospective study, the gene expression profile and clinicopathological parameters of 112 OSCC patients were combined to construct the best prediction model for lymph node metastasis of OSCC. The model was validated with 95 OSCC samples in this study. Logistic regression analysis was used. The area under the curve (AUC) ultimately determined the diagnostic value of the prediction model.

Results

The two genes CDKN2A + PLAU were closely related to lymph node metastasis of oral squamous cell carcinoma. The model with the combination of CDKN2A, PLAU, T stage and pathological grade was the best in predicting lymph node metastasis (AUC = 0.807, 95% CI: 0.713-0.881, P=0.0001). The prediction model had a specificity of 96% and sensitivity of 72.73% for stage T1 and T2 OSCC (AUC = 0.855, 95% CI: 0.697-0.949, P=0.0001).

Conclusions

High expression of CDKN2A and PLAU was associated with lymph node metastasis in OSCC. The prediction model including CDKN2A, PLAU, T stage and pathological grade can be used as the best diagnostic model for lymph node metastasis in OSCC.