CGB and GNRH1 expression analysis as a method of tumor cells metastatic spread detection in patients with gynecological malignances

The Role of Circulating Tumor Cells in Ovarian Cancer Dissemination

Metastatic ovarian cancer is the main reason for treatment failures and consequent deaths. Ovarian cancer is predisposed to intraperitoneal dissemination. In comparison to the transcoelomic route, distant metastasis via lymph vessels and blood is less common. The mechanisms related to these two modes of cancer spread are poorly understood. Nevertheless, the presence of tumor cells circulating in the blood of OC patients is a well-established phenomenon confirming the significant role of lymphatic and hematogenous metastasis. Thus, the detection of CTCs may provide a minimally invasive tool for the identification of ovarian cancer, monitoring disease progression, and treatment effectiveness. This review focuses on the biology of ovarian CTCs and the role they may play in cancer diagnosis and therapy.

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

An Aging and Senescence-Related Gene Signature for Prognosis Prediction in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion in the kidney. This study aims to establish an aging and senescence-related mRNA model for risk assessment and prognosis prediction in ccRCC patients.

Methods: ccRCC data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. By applying univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, a new prognostic model based on aging and senescence-related genes (ASRGs) was established. Depending on the prognostic model, high- and low-risk groups were identified for further study. The reliability of the prediction was evaluated in the validation cohort. Pan-cancer analysis was conducted to explore the role of GNRH1 in tumors.

Results: A novel prognostic model was established based on eight ASRGs. This model was an independent risk factor and significantly correlated with the prognosis and clinicopathological features of ccRCC patients. The high- and low-risk groups exhibited distinct modes in the principal component analysis and different patterns in immune infiltration. Moreover, the nomogram combining risk score and other clinical factors showed excellent predictive ability, with AUC values for predicting 1-, 3-, and 5-year overall survival in the TCGA cohort equal to 0.88, 0.82, and 0.81, respectively.

Conclusion: The model and nomogram based on the eight ASRGs had a significant value for survival prediction and risk assessment for ccRCC patients, providing new insights into the roles of aging and senescence in ccRCC.

GNRH1 and LTB4R might be novel immune-related prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

Background

Clear cell renal cell carcinoma (ccRCC) occupied most of renal cell carcinoma (RCC), which associated with poor prognosis. The purpose of this study is to screen novel and prognostic biomarkers for patients with ccRCC.

Methods and results

Firstly, Gene Expression Omnibus database was used to collect microarray data for weighted gene co-expression network construction. Gene modules related to prognosis which interest us most were picked out. 90 hub genes were further chosen in the key modules, two of which including gonadotropin releasing hormone 1 (GNRH1) and leukotriene B4 receptor (LTB4R) were screened and validated as immune-related prognostic biomarkers. Based on several public databases and ccRCC tissues collected by ourselves, we performed survival analysis, spearman correlation analysis, receiver operating characteristic (ROC) analysis, quantitative real-time PCR (qRT-PCR), western blotting, immunofluorescence (IF) and immunohistochemistry (IHC) staining for the validation of immune-related prognostic biomarkers. We further explored the relationship between immune-related prognostic biomarker expressions and immunocytes. Finally, gene set enrichment analysis (GSEA) demonstrated that the two immune-related prognostic biomarkers were significantly correlated with cell cycle.

Conclusions

Generally speaking, the present study has identified two novel prognostic biomarkers for patients with ccRCC, which showed strong correlation with prognosis of patients with ccRCC, could further be used as potential prognostic biomarkers in ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02052-1.

A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. The TNM stage is not satisfactory for individualized prognostic assessment and it does not predict response to therapy. In the present study, we downloaded the gene expression profiles from TCGA database to establish a training set and GEO database for a validation set. In the training set, we developed an 10 immune-related genes signature which had superior predictive value compared with TNM stage. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature might act as an independent factor for predicting the survival of HNSCC patients. As for the immune microenvironment, our results showed higher immune checkpoint expression (CLTA-4 and PD-1) in low-risk group which might reflect a positive immunotherapy response. Thus, our signature not only provided a promising biomarker for survival prediction, but might be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

Comprehensive immunogenomic landscape analysis of prognosis-related genes in head and neck cancer

Head and neck cancer is the sixth most common malignancy around the world, and 90% of cases are squamous cell carcinomas. In this study, we performed a systematic investigation of the immunogenomic landscape to identify prognostic biomarkers for head and neck squamous cell carcinoma (HNSCC). We analyzed the expression profiles of immune-related genes (IRGs) and clinical characteristics by interrogating RNA-seq data from 527 HNSCC patients in the cancer genome atlas (TCGA) dataset, including 41 HPV+ and 486 HPV- samples. We found that differentially expressed immune genes were closely associated with patient prognosis in HNSCC by comparing the differences in gene expression between cancer and normal samples and performing survival analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the biological functions of the differentially expressed immunogenomic prognosis-related genes. Two additional cohorts from the Oncomine database were used for validation. 65, 56 differentially expressed IRGs was associated with clinical prognosis in total and HPV- samples, respectively. Furthermore, we extracted 10, 11 prognosis-related IRGs from 65, 56 differentially expressed IRGs, respectively. They were significantly correlated with clinical prognosis and used to construct the prognosis prediction models. The multivariable ROC curves (specifically, the AUC) were used to measure the accuracy of the prognostic models. These genes were mainly enriched in several gene ontology (GO) terms related to immunocyte migration and receptor and ligand activity. KEGG pathway analysis revealed enrichment of pathways related to cytokine-cytokine receptor interactions, which are primarily involved in biological processes. In addition, we identified 63 differentially expressed transcription factors (TFs) from 4784 differentially expressed genes, and 16 edges involving 18 nodes were formed in the regulatory network between differentially expressed TFs and the high-risk survival-associated IRGs. B cell and CD4 T cell infiltration levels were significantly negatively correlated with the expression of prognosis-related immune genes regardless of HPV status. In conclusion, this comprehensive analysis identified the prognostic IRGs as potential biomarkers, and the model generated in this study may enable an accurate prediction of survival.

Nicotinamide phosphorybosiltransferase overexpression in thyroid malignancies and its correlation with tumor stage and with survivin/survivin DEx3 expression

Nicotinamide phosphorybosiltransferase (NAMPT) plays an important role in the regulation of cellular growth, angiogenesis, and apoptosis in mammalian cells. NAMPT overexpression has been recently found in colorectal, breast, prostatic, gastric, esophageal, pancreatic cancers, and specific NAMPT inhibitors might be adjuvant therapeutic modalities. In this study, we analyzed NAMPT expression in 40 malignant and in 67 benign thyroid tissue samples using qPCR. We also investigated relationships between NAMPT expression and survivin/survivin splicing variants DEx3 and 2B expressions. NAMPT expression was significantly higher in thyroid cancers (P < 0.0001), and it was positively correlated with tumor stage (P = 0.0012; r = 0.493). NAMPT expression was significantly higher in tumors staged pT3 or pT4 (16 cases) than in tumors staged pT1 or pT2 (24 cases) (P = 0.0106). Metastases to the lymph nodes were found in 12 out of 40 cases, and NAMPT expression was higher in the metastatic group (P = 0.0258). Multifocality was not associated with higher NAMPT expression (P = 0.3451). NAMPT expression in thyroid cancers significantly correlated with survivin and with survivin splice variant DEx3 expressions (P < 0.0001; r = 0.624 and P = 0.0239; r = 0.357, respectively). There was no correlation between NAMPT and survivin 2B expressions (P = 0.3508). This is the first study demonstrating NAMPT overexpression in thyroid malignancies using quantitative RT-PCR. Moreover, it shows that NAMPT is upregulated in patients with more advanced tumor stage and metastatic disease which may prove to be clinically relevant. Further studies are needed to explain the role of NAMPT in thyroid cancer biology and the possible use of NAMPT inhibitors in thyroid cancer.