Multi-omics analysis of tumor mutational burden combined with prognostic assessment in epithelial ovarian cancer based on TCGA database.

Background: Tumor mutation burden (TMB) is considered as a novel biomarker of response to immunotherapy and correlated with survival outcomes in various malignancies. Here, TMB-related genes (TRGs) expression signatures were constructed to investigate the association between TMB and prognosis in epithelial ovarian cancer (EOC), and the potential mechanism in immunoregulation was also explored. Methods: Based on somatic mutation data of 436 EOC samples from The Cancer Genome Atlas database, we examined the relationship between TMB level and overall survival (OS), as well as disease-free survival (DFS). Next, the TRGs signatures were constructed and validated. Differential abundance of immune cell infiltration, expression levels of immunomodulators and functional enrichment in high- and low-risk groups were also analyzed. Results: Higher TMB level revealed better OS and DFS, and correlated with earlier clinical stages in EOCs (P = 2.796e-04). The OS-related prognostic model constructed based on seven TRGs (B3GALT1, LIN7B, ANGPT2, D2HGDH, TAF13, PFDN4 and DNAJC19) significantly stratified EOC patients into high- and low-risk groups (P < 0.001). The AUC values of the seven-gene prognostic signature at 1 year, 3 years, and 5 years were 0.703, 0.758 and 0.777. While the DFS-related prognostic model was constructed based on the 4 TRGs (LPIN3, PXYLP1, IGSF23 and B3GALT1), with AUCs of 0.617, 0.756, and 0.731, respectively. Functional analysis indicated that immune-related pathways were enriched in low-risk groups. When considering the infiltration patterns of immune cells, we found higher proportions of follicular helper T (Tfh) cell and M1 macrophage, while lower infiltration of M0 macrophage in low-risk groups (P < 0.05). Accordingly, TMB levels of low-risk patients were significantly higher both in OS and DFS model (P < 0.01). Conclusions: Our TRGs-based models are reliable predictive tools for OS and DFS. High TMB may confer with an immunogenic microenvironment and predict favorable outcomes in EOCs.

Liu J ，Xu W ，Li S ，Sun R ，Cheng W ... -  《International Journal of Medical Sciences》

Prognostic analysis of tumor mutation burden and immune infiltration in hepatocellular carcinoma based on TCGA data.

Liu S ，Tang Q ，Huang J ，Zhan M ，Zhao W ，Yang X ，Li Y ，Qiu L ，Zhang F ，Lu L ，He X ... -  《Aging-US》

Tumor Mutation Burden, Immune Cell Infiltration, and Construction of Immune-Related Genes Prognostic Model in Head and Neck Cancer.

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, and the prognosis of HNSCC remains bleak. Numerous studies revealed that the tumor mutation burden (TMB) could predict the survival outcomes of a variety of tumors. Objectives: This study aimed to investigate the TMB and immune cell infiltration in these patients and construct an immune-related genes (IRGs) prognostic model. Methods: The expression data of 546 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. All patients were divided into high- and low- TMB groups, and the relationship between TMB and clinical relevance was further analyzed. The differentially expressed genes (DEGs) were identified using the R software package, limma. Functional enrichment analyses were conducted to identify the significantly enriched pathways between two groups. CIBERSORT algorithm was adopted to calculate the abundance of 22 leukocyte subtypes. The IRGs prognostic model was constructed via the multivariate Cox regression analysis. Results: Missense mutation and single nucleotide variants (SNV) were the most predominant mutation types in HNSCC. TP53, TTN, and FAT1 were the most frequently mutated genes. Patients with high TMB were observed with worse survival outcomes. The functional analysis of TMB associated DEGs showed that the identified DEGs mainly involved in spliceosome, RNA degradation, proteasome, and RNA polymerase pathways. We observed that macrophages, T cells CD8, and T cells CD4 memory were the most commonly infiltrated subtypes of immune cells in HNSCC. Finally, an IRGs prognostic model was constructed, and the AUC of the ROC curve was 0.635. Conclusions: Our results suggest that high TMB is associated with poor prognosis in HNSCC patients. The constructed model has potential prognostic value for the prognosis of these individuals, and it needs to be further validated in large-scale and prospective studies.

Jiang AM ，Ren MD ，Liu N ，Gao H ，Wang JJ ，Zheng XQ ，Fu X ，Liang X ，Ruan ZP ，Tian T ，Yao Y ... -  《International Journal of Medical Sciences》

A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer.

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

Peng Y ，Wang H ，Huang Q ，Wu J ，Zhang M ... -  《Journal of Ovarian Research》

Acetylation model predicts prognosis of patients and affects immune microenvironment infiltration in epithelial ovarian carcinoma.

Epithelial ovarian carcinoma (EOC) is a prevalent gynaecological malignancy. The prognosis of patients with EOC is related to acetylation modifications and immune responses in the tumour microenvironment (TME). However, the relationships between acetylation-related genes, patient prognosis, and the tumour immune microenvironment (TIME) are not yet understood. Our research aims to investigate the link between acetylation and the tumour microenvironment, with the goal of identifying new biomarkers for estimating survival of patients with EOC. Using data downloaded from the tumour genome atlas (TCGA), genotypic tissue expression (GTEx), and gene expression master table (GEO), we comprehensively evaluated acetylation-related genes in 375 ovarian cancer specimens and identified molecular subtypes using unsupervised clustering. The prognosis, TIME, stem cell index and functional concentration analysis were compared among the three groups. A risk model based on differential expression of acetylation-related genes was established through minimum absolute contraction and selection operator (LASSO) regression analysis, and the predictive validity of this feature was validated using GEO data sets. A nomogram is used to predict a patient's likelihood of survival. In addition, different EOC risk groups were evaluated for timing, tumour immune dysfunction and exclusion (TIDE) score, stemness index, somatic mutation, and drug sensitivity. We used the mRNA levels of the differentially expressed genes related to acetylation to classify them into three distinct clusters. Patients with increased immune cell infiltration and lower stemness scores in cluster 2 (C2) exhibited poorer prognosis. Immunity and tumourigenesis-related pathways were highly abundant in cluster 3 (C3). We developed a prognostic model for ten differentially expressed acetylation-related genes. Kaplan-Meier analysis demonstrated significantly worse overall survival (OS) in high-risk patients. Furthermore, the TIME, tumour immune dysfunction and exclusion (TIDE) score, stemness index, tumour mutation burden (TMB), immunotherapy response, and drug sensitivity all showed significant correlations with the risk scores. Our study demonstrated a complex regulatory mechanism of acetylation in EOC. The assessment of acetylation patterns could provide new therapeutic strategies for EOC immunotherapy to improve the prognosis of patients.

Wang X ，Li X ，Wei L ，Yu Y ，Hazaisihan Y ，Tao L ，Jia W ... -  《Journal of Ovarian Research》