Bioinformatics Analyses Indicate That Cathepsin G (CTSG) is a Potential Immune-Related Biomarker in Oral Squamous Cell Carcinoma (OSCC)

Risk score constructed with neutrophil extracellular traps-related genes predicts prognosis and immune microenvironment in multiple myeloma

Background

Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication.

Methods

In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model's effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens.

Results

64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders.

Conclusion

This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

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.

Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer

Lysosome dysfunction has been shown to play an important role in cancer progression. However, few research studies have reported the role of lysosomes in head and neck squamous cell carcinoma (HNSCC) progression. Lysosome-related genes (LRGs) were collected from the Molecular Signatures Database. Differentially expressed lysosome-related genes (DELRGs) were identified from the TCGA-HNSCC dataset. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were used to identify the prognostic genes. The prognostic values and expression of hub DELRGs were further validated by GEO datasets. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data and the single-sample gene set enrichment analysis were applied to evaluate the correlation between cathepsin G (CTSG) and immune infiltrates. Twenty-two DELRGs were identified. Among them, CTSG was an independent prognostic biomarker for HNSCC patients. Gene set enrichment analysis indicated that the potential mechanism of CTSG in regulating HNSCC was associated with the immune- and inflammation-related pathways. CTSG expression was highly correlated with immune cell infiltration. Finally, two potential compounds (CH and MAN) targeting CTSG protein were identified, and their reliability was validated through molecular docking analysis. CTSG was associated with immune infiltration and had prognostic value in HNSCC patients, which may be a potential biomarker for predicting the outcome of immunotherapy.

CTSG Suppresses Colorectal Cancer Progression through Negative Regulation of Akt/mTOR/Bcl2 Signaling Pathway

Colorectal cancer (CRC) is the most common gastrointestinal tumor worldwide, which is a severe malignant disease that threatens mankind. Cathepsin G (CTSG) has been reported to be associated with tumorigenesis, whereas its role in CRC is still unclear. This investigation aims to determine the function of CTSG in CRC. Our results indicated that CTSG was inhibited in CRC tissues, and patients with CTSG low expression have poor overall survival. Functional experiments revealed that CTSG overexpression suppressed CRC cell progression in vitro and in vivo, whereas CTSG suppression supports CRC development cells in vitro and in vivo. Mechanistically, CTSG overexpression suppressed Akt/mTOR signaling mechanism and elevated apoptotic-associated markers, and CTSG silencing activated Akt/mTOR signaling mechanisms and inhibited apoptotic-associated markers. Furthermore, the Akt suppression signaling pathway by MK2206 abolishes CTSG-silenced expression-induced cell viability and Bcl2 up-regulation in vitro and in vivo. Altogether, these outcomes demonstrate that CTSG may act as a tumor suppressor gene via Akt/mTOR/Bcl2-mediated anti-apoptotic signaling inactivation, and CTSG represents a potential therapeutic target in CRC.

Identification and validation of molecular subtype and prognostic signature for lung adenocarcinoma based on neutrophil extracellular traps

Background: Neutrophil Extracellular Traps (NETs) are fibrous networks made of DNA-histone complexes and proteins protruded from activated neutrophils. Accumulating evidences have highlighted the vital role of NETs in tumor progression and diffusion. However, limited systematic studies regarding the role of NETs in LUAD have been performed. Methods: Differentially expressed NETs-related genes and their mutation landscape were identified with TCGA database. Consensus clustering analysis was performed to determine the NETs-related subtypes of LUAD. LASSO algorithm was employed to construct a prognostic signature. Moreover, GSE30219 and GSE31210 were used as independent validation. We also constructed a lncRNA-miRNA-mRNA regulatory axis with several miRNA and lncRNA databases. Results: Consensus clustering identified two NETs-related clusters in LUAD. High NETs score was correlated with a favorable overall survival, abundant immune cell infiltration, and high activity of immune response signal pathways. Six NET-related genes (G0S2, KCNJ15, S100A12, AKT2, CTSG, and HMGB1) with significant prognostic value were screened to develop a prognostic signature. LUAD patients with low-risk had a significantly favorable overall survival both in the training set and validation set. Moreover, NETs-related risk score and clinical stage could act as an independent prognostic factor for LUAD patients. Significant correlation was obtained between risk score and tumor immune microenvironment. We also identified lncRNA BCYRN1/miR-3664-5p/CTSG regulatory axis that may be involved in the progression of LUAD. Conclusion: We developed two molecular subtypes and a prognostic signature for LUAD based on NETs-related genes. This stratification could provide more evidences for estimating the prognosis and immunotherapy of LAUD patients.

A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

An Immune-Related Gene Signature Can Predict Clinical Outcomes and Immunotherapeutic Response in Oral Squamous Cell Carcinoma

Objective: Immune landscape is a key feature that affects cancer progression, survival, and treatment response. Herein, this study sought to comprehensively characterize the immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC) and conduct an immune-related risk score (IRS) model for prognosis and therapeutic response prediction.

Methods: Transcriptome profiles and follow-up data of OSCC cohorts were curated from TCGA, GSE41613, and IMvigor210 datasets. An IRS model was established through univariate Cox, Random Survival Forest, and multivariate Cox analyses. Prognostic significance was evaluated with Kaplan–Meier curves, ROC, uni- and multivariate Cox, and subgroup analyses. A nomogram was conducted and assessed with C-index, ROC, calibration curves, and decision curve analyses. Immune cell infiltration and immune response were estimated with ESTIMATE and ssGSEA methods.

Results: An IRS model was constructed for predicting the overall survival and disease-free survival of OSCC, containing MASP1, HBEGF, CCL22, CTSG, LBP, and PLAU. High-risk patients displayed undesirable prognosis, and the predictive efficacy of this model was more accurate than conventional clinicopathological indicators. Multivariate Cox analyses demonstrated that this model was an independent risk factor. The nomogram combining IRS, stage, and age possessed high clinical application values. The IRS was positively associated with a nonflamed tumor microenvironment. Moreover, this signature enabled to predict immunotherapeutic response and sensitivity to chemotherapeutic agents (methotrexate and paclitaxel).

Conclusion: Collectively, our study developed a robust IRS model with machine learning method to stratify OSCC patients into subgroups with distinct prognosis and benefits from immunotherapy, which might assist identify biomarkers and targets for immunotherapeutic schemes.

The feasibility of proteomics sequencing based immune-related prognostic signature for predicting clinical outcomes of bladder cancer patients

Background

Bladder cancer (BCa) shows its potential immunogenity in current immune-checkpoint inhibitor related immunotherapies. However, its therapeutic effects are improvable and could be affected by tumor immune microenvironment. Hence it is interesting to find some more prognostic indicators for BCa patients concerning immunotherapies.

Methods

In the present study, we retrospect 129 muscle-invasive BCa (MIBC) patients with radical cystectomy in Shanghai General Hospital during 2007 to 2018. Based on the results of proteomics sequencing from 9 pairs of MIBC tissue from Shanghai General Hospital, we focused on 13 immune-related differential expression proteins and their related genes. An immune-related prognostic signature (IRPS) was constructed according to Cancer Genome Atlas (TCGA) dataset. The IRPS was verified in ArrayExpress (E-MTAB-4321) cohort and Shanghai General Hospital (General) cohort, separately. A total of 1010 BCa patients were involved in the study, including 405 BCa patients in TCGA cohort, 476 BCa patients in E-MTAB-4321 cohort and 129 MIBC patients in General cohort.

Result

It can be indicated that high IRPS score was related to poor 5-year overall survival and disease-free survival. The IRPS score was also evaluated its immune infiltration. We found that the IRPS score was adversely associated with GZMB, IFN-γ, PD-1, PD-L1. Additionally, higher IRPS score was significantly associated with more M2 macrophage and resting mast cell infiltration.

Conclusion

The study revealed a novel BCa prognostic signature based on IRPS score, which may be useful for BCa immunotherapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09783-y.

A prognostic model for oral squamous cell carcinoma using 7 genes related to tumor mutational burden

Background

Oral squamous cell carcinoma (OSCC) is a rising problem in global public health. The traditional physical and imageological examinations are invasive and radioactive. There is a need for less harmful new biomarkers. Tumor mutational burden (TMB) is a novel prognostic biomarker for various cancers. We intended to explore the relationship between TMB-related genes and the prognosis of OSCC and to construct a prognostic model.

Methods

TMB-related differential expressed genes (DEGs) were screened by differential analysis and optimized via the univariate Cox and LASSO Cox analyses. Risk Score model was constructed by expression values of screened genes multiplying coefficient of LASSO Cox.

Results

Seven TMB-related DEGs (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were screened. Patients in high-risk group (Risk Score >  − 0.684511507) had worse prognosis compared to the low-risk group (Risk Score <  − 0.684511507). Survival rates of patients in the high-risk group were lower in the gender, age and degrees of differentiation subgroups compared to the low-risk group.

Conclusions

The Risk Score model constructed by 7 TMB-related genes may be a reliable biomarker for predicting the prognosis of OSCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02193-3.

Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma

Background. Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. Methods. The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. Results. A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones. Conclusion. The hypoxia-immune-based gene signature has prognostic potential in OSCC.

Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan-Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

Artificial Neural Networks Predicted the Overall Survival and Molecular Subtypes of Diffuse Large B-Cell Lymphoma Using a Pancancer Immune-Oncology Panel

Simple Summary

This research predicted the overall survival of patients and cell-of-origin molecular subtypes of diffuse large B-cell lymphoma from Tokai University using gene expression data. A pancancer immune profiling panel was analyzed using artificial neural networks, and high accuracy of prediction was found. Additionally, the results were explained with other machine learning techniques and conventional bioinformatics analyses.

Abstract

Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune-oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis, ARG1, TNFSF12, REL, and NRP1 correlated with favorable survival (hazard risks: 0.3–0.5), and IFNA8, CASP1, and CTSG, with poor survival (hazard risks = 1.0–2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (CD163), and MYD88 expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and MYC translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1). STAT6, TREM2, and REL were associated with the germinal center B-cell (GCB) subtype, and CD37, GNLY, CD46, and IL17B were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of MAPK3 with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.

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.