A novel prognostic signature of seven genes for the prediction in patients with thymoma

The Molecular Landscape of Thymic Epithelial Tumors: A Comprehensive Review

Thymic epithelial tumors (TETs) are characterized by their extreme rarity and variable clinical presentation, with the inadequacy of the use of histological classification alone to distinguish biologically indolent from aggressive cases. The utilization of Next Generation Sequencing (NGS) to unravel the intricate genetic landscape of TETs could offer us a comprehensive understanding that is crucial for precise diagnoses, prognoses, and potential therapeutic strategies. Despite the low tumor mutational burden of TETS, NGS allows for exploration of specific genetic signatures contributing to TET onset and progression. Thymomas exhibit a limited mutational load, with prevalent GTF2I and HRAS mutations. On the other hand, thymic carcinomas (TCs) exhibit an elevated mutational burden, marked by frequent mutations in TP53 and genes associated with epigenetic regulation. Moreover, signaling pathway analyses highlight dysregulation in crucial cellular functions and pathways. Targeted therapies, and ongoing clinical trials show promising results, addressing challenges rooted in the scarcity of actionable mutations and limited genomic understanding. International collaborations and data-sharing initiatives are crucial for breakthroughs in TETs research.

Prognostic Model of Eleven Genes Based on the Immune Microenvironment in Patients With Thymoma

Purpose: The pathogenesis of thymoma (THYM) remains unclear, and there is no uniform measurement standard for the complexity of THYM derived from different thymic epithelial cells. Consequently, it is necessary to develop novel biomarkers of prognosis estimation for patients with THYM.

Methods: Consensus clustering and single-sample gene-set enrichment analysis were used to divide THYM samples into different immunotypes. Differentially expressed genes (DEGs) between those immunotypes were used to do the Kyoto Encyclopedia of Genes and Genomes analysis, Gene Ontology annotations, and protein-protein interaction network. Furthermore, the survival-related DEGs were used to construct prognostic model with lasso regression. The model was verified by survival analysis, receiver operating characteristic curve, and principal component analysis. Furthermore, the correlation coefficients of stemness index and riskscore, tumor mutation burden (TMB) and riskscore, drug sensitivity and gene expression were calculated with Spearman method.

Results: THYM samples were divided into immunotype A and immunotype B. A total of 707 DEGs were enriched in various cancer-related or immune-related pathways. An 11-genes signature prognostic model (CELF5, ODZ1, CD1C, DRP2, PTCRA, TSHR, HKDC1, KCTD19, RFX8, UGT3A2, and PRKCG) was constructed from 177 survival-related DEGs. The prognostic model was significantly related to overall survival, clinical features, immune cells, TMB, and stemness index. The expression of some genes were significantly related to drug sensitivity.

Conclusion: For the first time, a prognostic model of 11 genes was identified based on the immune microenvironment in patients with THYM, which may be helpful for diagnosis and prediction. The associated factors (immune microenvironment, mutation status, and stemness) may be useful for exploring the mechanisms of THYM.

Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis

BACKGROUND

Thymoma is a heterogeneous tumor originated from thymic epithelial cells. The molecular mechanism of thymoma remains unclear.

METHODS

The expression profile, methylation, and mutation data of thymoma were obtained from TCGA database. The coexpression network was constructed using the variance of gene expression through WGCNA. Enrichment analysis using clusterProfiler R package and overall survival (OS) analysis by Kaplan-Meier method were carried out for the intersection of differential expression genes (DEGs) screened by limma R package and important module genes. PPI network was constructed based on STRING database for genes with significant impact on survival. The impact of key genes on the prognosis of thymoma was evaluated by ROC curve and Cox regression model. Finally, the immune cell infiltration, methylation modification, and gene mutation were calculated.

RESULTS

We obtained eleven coexpression modules, and three of them were higher positively correlated with thymoma. DEGs in these three modules mainly involved in MAPK cascade and PPAR pathway. LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF were identified as key genes through the PPI network. AUC values of LIPE were the highest. Cox regression analysis showed that low expression of LIPE was a prognostic risk factor for thymoma. In addition, there was a high correlation between LIPE and T cells. Importantly, the expression of LIPE was modified by methylation. Among all the mutated genes, GTF2I had the highest mutation frequency.

CONCLUSION

These results suggested that the molecular mechanism of thymoma may be related to immune inflammation. LIPE may be the key genes affecting prognosis of thymoma. Our findings will help to elucidate the pathogenesis and therapeutic targets of thymoma.

Prognostic Correlation of Glycolysis-Related Gene Signature in Patients with Laryngeal Cancer

BACKGROUND

Aerobic glycolysis is one of the metabolic characteristics of tumor cells, which is regulated by many genes. The aim of our study was to construct glycolysis-related gene signature to accurately predict the prognosis of laryngeal cancer (LC) patients.

METHODS

We analyzed the mRNA expression profiles of LC patients from The Cancer Genome Atlas (TCGA). Eleven glycolysis-related gene sets were analyzed by gene set enrichment analysis (GSEA). In order to acquire the gene signature related to prognosis, we used univariate and multivariate Cox regression analysis.

RESULTS

We confirmed that a gene signature composed of two genes (STC2, LHPP) can predict the overall survival (OS) of patients with LC. Based on each patient's risk score, we found that the survival results of patients in the high-risk group were significantly lower than those in the low-risk group (log-rank test P-value=0.002). Multivariate Cox regression analysis confirmed that gene signature could independently predict OS in LC patients (HR = 1.981, 95% CI 1.446-2.714 P<0.001). In addition, a nomogram including the age, sex, grade and risk score was constructed. The nomogram demonstrated good accuracy for OS prediction, with a C-index of 0.752.

CONCLUSION

The glycolysis-related two-gene risk score model could be used as a biomarker for LC prognosis.

Relationship of possible biomarkers with malignancy of thymic tumors: a meta-analysis

Background

Role of biomarkers for promotion of tumor proliferation (BPTPs) and for promotion of apoptosis (BPAs) in thymic malignant tumors is still unclear. The purpose of this study was to evaluate the relationship between BPTPs and/or BPAs and malignancy of thymic malignant tumors.

Methods

Studies on thymic malignant tumors and biomarkers were searched in PubMed, ISI Web of Knowledge, and Embase databases, and all statistical analyses were conducted using Review Manager.

Results

Twelve articles related to biomarkers and thymic malignant tumors were selected and analyzed. A relationship between BPAs and Masaoka stage was demonstrated for four markers, namely Bax, p73, Casp-9 and Bcl-2, included 138 stage I/II patients and 74 stage III/IV patients, and BPAs were significantly correlated with high Masaoka staging (P = 0.03). We further found a relationship between BPAs and degree of malignancy for four markers, namely Bax, p73, Casp-9 and Bcl-2, included 176 thymoma patients and 36 thymic carcinoma patients, and BPAs were significantly correlated with thymic carcinoma (P = 0.010). In addition, a relationship between BPTP and Masaoka staging was demonstrated for seven markers, namely Podoplanin, Glut-1, Muc-1, Egfr, Igf1r, c-Jun, and n-Ras, included 373 patients with stage I/II and 212 patients with stage III/IV, and BPTPs were significantly correlated with high Masaoka staging (P < 0.001). We also found a relationship between BPTPs and degree of malignancy for ten markers, namely Mesothelin, c-Kit (CD117), Egfr, Lat-1, Muc-1,Ema, Glut-1, Igf1r, c-Jun, and n-Ras, included 748 thymoma patients and 280 thymic carcinoma patients, and BPTPs were significantly correlated with thymic carcinoma (P < 0.001).

Conclusion

These findings show that high levels of BPTPs or BPAs are more closely related to thymic carcinoma and Masaoka stage III/IV, suggesting that BPTPs and BPAs may play an important role in the occurrence and development of thymic malignant tumors.

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.

IRF1 association with tumor immune microenvironment and use as a diagnostic biomarker for colorectal cancer recurrence

Colorectal cancer (CRC) is considered to be one of the most lethal cancer types globally, and its recurrence is a major treatment challenge. Identifying the factors involved when determining the risk of CRC recurrence is required to improve personalized therapy for patients with CRC. Based on the GSE39582 dataset, the present study demonstrated that a higher ratio of M1 macrophages and activated memory CD4+ T cells indicated a better recurrence-free survival (RFS) time for CRC, using CIBERSORT and Pearson's correlation analysis. Through weighted correlation network analysis (WGCNA), an immune-associated module was identified that was significantly positively correlated with the ratio of M1 macrophages and activated memory CD4+ T cells. In this module, using WGCNA and a protein-protein interaction network, interferon regulatory factor 1 (IRF1), chemokine ligand 5, ubiquitin/ISG15-conjugating enzyme E2 L6, guanylate binding protein 1 and interleukin 2 receptor subunit beta were identified as hub genes. Among these genes, univariate Cox and multivariate Cox analysis revealed that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC. This was further validated using The Cancer Genome Atlas data. Gene set enrichment analysis demonstrated that IRF1 influenced the genes and pathways that are associated with immune cell recruitment and activation. Additionally, the DNA methylation of cg27587780 and cg15375424 CpG sites in the IRF1 gene region was indicated to be negatively correlated with IRF1 mRNA expression and positively correlated with the recurrence of CRC. Collectively, the results of the present study demonstrated that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC.

Expression of molecules of the Wnt pathway and of E-cadherin in the etiopathogenesis of human thymomas

The molecular pathogenesis of thymoma remains largely unknown. It has been recently demonstrated, that activation of Wnt signaling pathway leads to increased incidence of thymoma in murine models. The present study investigated the activation of molecules of the Wnt signaling pathway in human thymoma. A total of 112 thymoma cases with complete clinical and follow-up data and 8 controls were included in the present study. Patients with thymoma and controls were examined immunohistochemically for β-catenin and E-cadherin. The mRNA expression levels of CTNNB1, CCND1, MYC, AXIN2 and CDH1 were analyzed by reverse transcription-quantitative PCR. Immunohistochemically, β-catenin and E-cadherin were overexpressed in neoplastic cells of all thymomas. In type A, B1 and non-invasive type B2 thymoma, both molecules were located in the cytoplasm, in contrast to invasive type B2 and B3 thymoma, where membranous immunopositivities were observed. mRNA expression levels of genes involved in the Wnt pathway and of E-cadherin were significantly increased in both type A and B thymoma compared with controls; increasing gradually from type B1 to B3, and with higher stage of disease. In recurrent type B thymoma, the mRNA expression of the molecules was significantly higher. Despite the activation of Wnt pathway in indolent type A thymoma, the negative feedback of the pathway was preserved by overexpression of inhibitory molecule axin2, which was not overexpressed in type B thymoma. In summary, the Wnt pathway was activated in human thymoma and may contribute to oncogenesis. Detection of molecules of the Wnt pathway may be of diagnostic and prognostic value.

A three-gene novel predictor for improving the prognosis of cervical cancer

Cervical cancer is the most common gynecological malignancy, the third most common malignant tumor in women worldwide, and the most common malignant tumor among Chinese women. However, despite continuous improvement in medical treatment, the number of cervical cancer cases in China is on the increase annually, consistent with the general trend in global cervical cancer incidence. Therefore, it is particularly important to study the pathogenesis of cervical cancer at the genetic level in China. The aim of the present study was to use the TCGA database to identify potential genetic signatures that could predict the prognosis of patients with cervical cancer and provide evidence supporting clinical genetic intervention in cervical cancer. Primarily, an effective three-gene signature was found that predicts prognosis in patients with cervical cancer. This model can provide prima facie evidence for future assessment of patient risk and prognosis, but further testing is required to improve its accuracy. Our results also suggested that centromere protein M, methionine sulfoxide reductase B3 and Zic family member 2 could be promising biomarkers for the prognosis of cervical cancer.

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma

Purpose

Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.

Methods

We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model.

Results

GSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly a ssociated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into high‐ and low‐risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95% CI (confidence interval) =1.355–4.184), indicating that it can serve as an independent prognostic indicator.

Conclusion

We developed and identified a seven‐gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.