Hub gene of disulfidptosis-related immune checkpoints in breast cancer.

Disulfidptosis and immune checkpoint genes play an important role in tumor treatment. But there has been less research on the relationship between disulfidptosis and immune checkpoint of breast cancer. The objective of this study was to identify the hub genes of disulfidptosis- related immune checkpoints in breast cancer. We downloaded breast cancer expression data from The Cancer Genome Atlas database. The expression matrix of disulfidptosis-related immune checkpoints genes was established by mathematical method. A protein-protein interaction networks was established based on this expression matrix, and differential expression analysis was performed between normal and tumor samples. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to functionally annotate putative diferentially expressed genes. Two hub genes CD80 and CD276 were obtained by mathematical statistics and machine learning. Differential expression of these two genes, prognostic survival analysis, combined diagnostic ROC curve and immune results all showed that they were closely related to the occurrence, development and death of breast tumors. The results of this study open up a new way to explore immunotherapy for breast cancer.

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms.

BACKGROUND/AIMS: Hepatocellular carcinoma, a highly malignant tumor, is difficult to diagnose, treat, and predict the prognosis. Notch signaling pathway can affect hepatocellular carcinoma. We aimed to predict the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms. MATERIALS AND METHODS: We downloaded hepatocellular carcinoma data from the Cancer Genome Atlas and Gene Expression Omnibus databases and used machine learning methods to screen the hub Notch signal-related genes. Machine learning classification was used to construct a prediction model for the classification and diagnosis of hepatocellular carcinoma cancer. Bioinformatics methods were applied to explore the expression of these hub genes in the hepatocellular carcinoma tumor immune microenvironment. RESULTS: We identified 4 hub genes, namely, LAMA4, POLA2, RAD51, and TYMS, which were used as the final variables, and found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of hepatocellular carcinoma. The area under curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500, and 0.932; respectively. The area under curves were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489, and 0.926. The area under curve in the external validation set was 0.934. Immune cell infiltration was related to the expression of 4 hub genes. Patients in the low-risk group of hepatocellular carcinoma were more likely to have an immune escape. CONCLUSION: The Notch signaling pathway was closely related to the occurrence and development of hepatocellular carcinoma. The hepatocellular carcinoma classification and diagnosis model established based on this had a high degree of reliability and stability.

Development and Validation of an Inflammatory Response-Related Gene Signature for Predicting the Prognosis of Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly dangerous malignant tumor of the digestive tract, and difficult to diagnose, treat, and predict the prognosis. As we all know, tumor and inflammation can affect each other, and thus the inflammatory response in the microenvironment can be used to affect the prognosis. So far, the prognostic value of inflammatory response-related genes in PAAD is still unclear. Therefore, this study aimed to explore the inflammatory response-related genes for predicting the prognosis of PAAD. In this study, the mRNA expression profiles of PAAD patients and the corresponding clinical characteristics data of PAAD patients were downloaded from the public database. The least absolute shrinkage and selection operator (LASSO) Cox analysis model was used to identify and construct the prognostic gene signature in The Cancer Genome Atlas (TCGA) cohort. The PAAD patients used for verification are from the International Cancer Genome Consortium (ICGC) cohort. The Kaplan-Meier method was used to compare the overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were performed to identify the independent predictors of OS. Gene set enrichment analysis (GSEA) was performed to obtain gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and the correlation between gene expression and immune infiltrates was investigated via single sample gene set enrichment analysis (ssGSEA). The GEPIA database was performed to examine prognostic genes in PAAD. LASSO Cox regression analysis was used to construct a model of inflammatory response-related gene signature. Compared with the low-risk group, patients in the high-risk group had significantly lower OS. The receiver operating characteristic curve (ROC) analysis confirmed the signature's predictive capacity. Multivariate Cox analysis showed that risk score is an independent predictor of OS. Functional analysis shows that the immune status between the two risk groups is significantly different, and the cancer-related pathways were abundant in the high-risk group. Moreover, the risk score is significantly related to tumor grade, stage, and immune infiltration types. It was also obtained that the expression level of prognostic genes was significantly correlated with the sensitivity of cancer cells to anti-tumor drugs. In addition, there are significant differences in the expression of PAAD tissues and adjacent non-tumor tissues. The novel signature constructed from five inflammatory response-related genes can be used to predict prognosis and affect the immune status of PAAD. In addition, suppressing these genes may be a treatment option.

circ_LRIG3 contributes to the progression of hepatocellular carcinoma by elevating RNF38 via sponging miR-449a.

Circular RNAs (circRNAs) play crucial roles in multiple cancers, including hepatocellular carcinoma (HCC). However, the effects and molecular mechanisms of circ_LRIG3 in HCC remain barely unknown. qRT-PCR assay was employed to detect the levels of circ_LRIG3, LRIG3, miR-449a and ring finger protein 38 (RNF38). RNase R assay and Actinomycin D assay were performed to analyze the characteristics of circ_LRIG3. Colony formation assay and MTT assay were used to evaluate cell proliferation. Flow cytometry analysis and transwell assay were adopted for cell apoptosis and metastasis, respectively. Western blot assay was carried out for the protein levels of Ki67, Snail, E-cadherin, RNF38, Smad2/3 and p-Smad2/3. Murine xenograft model assay was used to explore the role of circ_LRIG3 in vivo. circ_LRIG3 expression was upregulated in HCC tissues and cells. Knockdown of circ_LRIG3 suppressed proliferation, migration and invasion and facilitated cell apoptosis in HCC cells in vitro and blocked tumor growth of HCC in vivo. RNF38 overexpression reversed the effects of circ_LRIG3 knockdown on the malignant behaviors of HCC cells. Moreover, circ_LRIG3 could sponge miR-449a to positively modulate RNF38 expression in HCC cells. circ_LRIG3 knockdown inhibited the progression of HCC cells by sponging miR-449a. In addition, circ_LRIG3 silencing might inhibit the Smad2/3 pathway. circ_LRIG3 facilitated HCC progression by modulation of miR-449a/LRIG3 axis, which might provide a novel method for HCC therapy.

Elevated pretreatment platelet distribution width and platelet count predict poor prognosis in nasopharyngeal carcinoma.

BACKGROUND: Previous studies have demonstrated that platelets play a multifaceted role in cancer progression and metastasis. However, the value of platelet indices for predicting survival in nasopharyngeal carcinoma (NPC) patients remains unknown. The aim of this study was to evaluate the predictive significance of platelet indices in NPC cases. MATERIALS AND METHODS: A total of 168 patients who were diagnosed with NPC between January 2011 and June 2012 were recruited. The optimal cut-off values for the platelet indices were determined using a receiver operating characteristic curve. The Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact of the potential predictors. RESULTS: Of the 168 patients, high platelet distribution width (PDW) and platelet count (PLT) levels were observed in 81 (48.21%) and 68 (40.48%) of the patients, respectively. An increased PDW was associated with the depth of invasion (T stage, P = 0.019), lymph node metastasis (N stage, P = 0.026), and clinical stage (P < 0.001). Moreover, the survival analysis showed that the overall survival of the patients with a PDW > 16.3 fL or platelet count > 266 × 109/L was associated with a poorer prognosis (both P < 0.001). In the multivariate Cox regression model, the PDW (P < 0.001), PLT (P = 0.001), T stage (P < 0.001), N stage (P = 0.006), clinical stage (P = 0.005), and Epstein-Barr virus DNA (P = 0.039) were independent prognostic factors for the overall survival. CONCLUSIONS: The PDW and PLT are easily available via a routine blood test, and our study showed that the PDW and PLT could be prognostic predictors in NPC patients. However, further studies are required to confirm this conclusion.