Expression of HIF-1α and HIF-2α correlates to biological and clinical significance in papillary thyroid carcinoma.

BACKGROUND: The aim of this study was to detect the expression of hypoxia-inducible factor (HIF)-1α and HIF-2α in papillary thyroid carcinoma (PTC) compared with normal thyroid tissues. METHODS: The mRNA levels and protein levels of HIF-1α and HIF-2α were detected by real-time PCR and Western blot separately in 30 pairs of PTCs and normal thyroid cases. The protein levels were also detected by immunohistochemistry (IHC) using 92 samples of PTC group and 46 normal samples as control group for analyzing the biological and clinical significance of the expression of HIF-1α/HIF-2α. RESULTS: Real-time PCR results showed the mRNA level of HIF-1α and HIF-2α were significantly higher in PTC than normal group (P<0.001). Also, significantly higher positive rates (73%/65%) of HIF-1α and HIF-2α were observed in PTC compared with the control group (27%/35%) by IHC (P<0.01); the consistent results were gotten with Western blot. Although we did not find a significant correlation between the expression of HIF-1α and HIF-2α with gender, age, calcification, or Hashimoto's disease in the present study (P>0.05), both of their expressions were correlated to lymph node metastasis (P<0.05), capsular invasion (P<0.05), and TNM stage (P<0.05). CONCLUSIONS: Overexpression of HIF-1α and HIF-2α are associated with the carcinogenesis of PTC, served as potential biomarkers of PTC.

A newly identified pyroptosis-related gene signature for predicting prognosis of patients with hepatocellular cancer.

Background: Hepatocellular carcinoma (HCC) is a very heterogeneous illness, making prognosis prediction a huge problem. Pyroptosis, which has recently been shown to be an inflammatory type of programmed cell death, is involved in HCC. Nevertheless, the role of pyroptosis-related genes in HCC has not been fully elucidated. Thus, this study aimed to construct a prognostic signature based on pyroptosis-related genes for HCC. Methods: The messenger RNA expression patterns of HCC patients, as well as the accompanying clinical information, were retrieved from The Cancer Genome Atlas (TCGA) database for this research. After differentially expressed pyroptosis-related Gene in tumor and normal groups were identified, Cox regression analyses were performed to construct a prognostic signature which was then assessed through independent prognostic analysis. Results: A signature consisting of four genes (CASP8, GSDME, NOD2, and PLCG1) was constructed to predict overall survival (OS) for HCC. The signature was identified to be independent by the cox regression analysis and obtained the largest area under the receiver operating characteristic (ROC) curve (AUC) was 0.691, 0.628, and 0.632 for survival at 1, 2, and 3 years, respectively. Conclusions: We discovered that the levels of pyroptosis-related genes expression differed across HCC patients and were associated with both survival and prognosis. This suggested that targeting pyroptosis as a treatment strategy for HCC may be a viable option.

To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.

A new clinical prognostic nomogram for liver cancer based on immune score.

BACKGROUND: Increased attention is being paid to the relationship between the immune status of the tumor microenvironment and tumor prognosis. The application of immune scoring in evaluating the clinical prognosis of liver cancer patients has not yet been explored. This study sought to clarify the association between immune score and prognosis and construct a clinical nomogram to predict the survival of patients with liver cancer. METHODS: A total of 346 patients were included in our analysis datasets downloaded from The Cancer Genome Atlas (TCGA) dataset. A Cox proportional-hazards regression model was used to estimate the adjusted hazard ratios (HRs). A nomogram was built based on the results of multivariate analysis and was subjected to bootstrap internal validation. The predictive accuracy and discriminative ability were measured by the concordance index (C-index) and the calibration curve. Through the functional analysis of differential expression of genes with different immune scores, the target genes were screened out. RESULTS: In comparison with patients with low immune scores, those with intermediate and high immune scores had significantly improved survival time [HR and 95% confidence interval (CI): 0.54 (0.30-0.97) and 0.51 (0.27-0.97), respectively]. The C-index for survival time prediction was 0.66 (95% CI: 0.60-0.71). The calibration plot for the probability of survival at three or five years showed good agreement between prediction by the nomogram and actual observations. The top 10 hub genes were CXCL8(chemokine (C-X-C motif) ligand 8), SYK(spleen tyrosine kinase), CXCL12(chemokine (C-X-C motif) ligand 12), CXCL10 (chemokine (C-X-C motif) ligand10), CXCL1(chemokine (C-X-C motif) ligand1), CCL5(chemokine (C-C motif) ligand 5), CCL20(chemokine (C-C motif) ligand 20), LCK, CXCL11(chemokine (C-X-C motif) ligand 11), CCR5(chemokine (C-C motif) receptor 5). More importantly, we found that the high expression of CXCL8 and CXCL1 were related to the prognosis. CONCLUSIONS: High and/or intermediate immune scores are significantly correlated with better survival time in patients with liver cancer. Moreover, nomograms for predicting prognosis may help to estimate the survival of patients. We also propose that CXCL8 and CXCL1 may be a potential therapeutic target for liver cancer treatment.

Methylation-Driven Genes Identified as Novel Prognostic Indicators for Thyroid Carcinoma.

BACKGROUND: Aberrant DNA methylation plays an crucial role in tumorigenesis through regulating gene expression. Nevertheless, the exact role of methylation in the carcinogenesis of thyroid cancer and its association with prognosis remains unclear. The purpose of this study is to explore the DNA methylation-driven genes in thyroid cancer by integrative bioinformatics analysis. METHODS: The transcriptome profiling data and DNA methylation data of thyroid cancer were downloaded from The Cancer Genome Atlas (TCGA) database. The methylmix R package was used to screen DNA methylation-driven genes in thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to annotate the function of methylation-driven genes. Univariate Cox regression analyses was performed to distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses was utilized to build a prognostic multi-gene signature. A survival analysis was carried out to determine the individual prognostic significance of this multi-gene signature. RESULTS: A total of 51 methylation-driven genes were identified. The functional analysis indicated that these genes were significantly enriched in diverse biological processes (BP) and pathways related to the malignancy processes. Four of these genes (RDH5, TREM1, BIRC7, and SLC26A7) were selected to construct the risk evaluation model. Patients in the low-risk group had an conspicuously better overall survival (OS) than those in high-risk group (p < 0.001). The area under the receiver operating characteristic (ROC) curve for this model was 0.836, suggesting a good specificity and sensitivity. Subsequent survival analysis revealed that this four-gene signature served as an independent indicator for the prognosis of thyroid cancer. Moreover, the prognostic signature was well validated in a external thyroid cancer cohort. CONCLUSION: We identified methylation-driven genes in thyroid cancer with independent prognostic value, which may offer new insight into molecular mechanisms of thyroid cancer and provide new possibility for individualized treatment of thyroid cancer patients.

Identification of key genes as potential biomarkers for triple­negative breast cancer using integrating genomics analysis.

Triple­negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome­wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non­TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top­ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan­Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

PIG3 plays an oncogenic role in papillary thyroid cancer by activating the PI3K/AKT/PTEN pathway.

The p53-inducible gene 3 (PIG3 or TP53I3) is a downstream gene of p53, which can be involved in the process of apoptosis induced by p53 via the production of reactive oxygen species (ROS). However, the functional significance of PIG3 in cancer remains to be determined. This aim of this study was to examine the mRNA and protein expression of PIG3 in papillary thyroid carcinoma (PTC) and normal thyroid tissues, assess the relationship between PIG3 expression and clinicopathological parameters in PTC and examine its role in the proliferation of PTC cell lines. The results showed that PIG3 was aberrantly overexpressed in the majority of specimens of PTC while the expression of p53 was lower in PTC compared with normal thyroid tissues. Anti-PIG3 immuno-reactivity positively correlated with TNM grade. In the PTC cell lines, PIG3 silencing using small interfering RNA (siRNAs) impaired their ability of proliferation and decreased the activity of the PI3K/AKT/PTEN pathway. The results suggested that PIG3 plays an oncogenic role in PTC via the regulation of the PI3K/AKT/PTEN pathway and support the exploration of PIG3 as a novel biomarker for patients with PTC.