Upregulated expression of PTTG1 is associated with progression of pancreatic cancer.

Background: Pancreatic cancer (PC) is an aggressive disease with a very poor prognosis. The insidious onset, rapid progression, and resistance to conventional therapies mark the imperious need for novel biomarkers and therapeutic targets. The pituitary tumor transforming gene 1 (PTTG1), implicated in tumorigenesis and cellular transformation, has been studied in various cancers, however, its role and mechanisms in PC remain to be elucidated for better understanding the disease pathology and in enhancing patient management strategies. Methods: The present study examined the PTTG1 messenger RNA (mRNA) expression levels and clinical significance through meta-analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Immunohistochemistry (IHC) was used to measure PTTG1 protein levels in PC and adjacent non-cancerous tissues. A correlation was observed between PTTG1 expression and some clinical characteristics based on the TCGA and IHC data. Univariate and multivariate Cox regressions were used to identify independent prognostic factors. Kaplan-Meier (KM) survival analysis was performed. The co-expressed genes of PTTG1 were determined by integrating online tools, and the enrichment analyses were performed to determine PTTG1-related pathways and hub co-expressed genes. Results: PTTG1 was highly expressed in PC tissues based on the TCGA, GEO, and IHC data. The combined standard mean difference (SMD) values of PTTG1 expression based on TCGA and GEO databases was 1.02 [95% confidence interval (CI): 0.74-1.30]. The area under the curve (AUC) based on the summary receiver operating characteristic (sROC) curve was 0.93 (95% CI: 0.90-0.95). PTTG1 overexpression was remarkably correlated with an inferior overall survival (OS). A total of 367 genes were identified as co-expressed genes of PTTG1 in PC and were mainly involved in the cell cycle pathway. The four identified core genes were CDK1, CCNA2, CDC20, and MAD2L1. Conclusions: The upregulated expression of PTTG1 plays an essential role in PC's progression as a biomarker.

ADNP is associated with immune infiltration and radiosensitivity in hepatocellular carcinoma for predicting the prognosis.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most lethal diseases due to its high faculty of invasiveness and metastasis. Activity-dependent neuroprotective protein (ADNP) has been regarded as an oncogene in bladder cancer and ovarian cancer. However, the role of ADNP in the regulation of tumor immune response, development, and treatment resistance in HCC remains unknown and is worth exploring. METHODS: The correlation between ADNP and prognosis, immune cell infiltration, immune checkpoints, chemokines, tumor mutation burden, microsatellite instability, and genomic mutation of pan-cancer cohorts in The Cancer Genome Atlas was analyzed. ADNP expression in HCC cell lines, HCC and the adjacent normal tissues was measured by western blotting and immunochemistry. Nomogram was constructed to predict the survival of patients with HCC based on the ADNP expression and significant clinical characteristics. The potential biological functions and impacts on radiotherapy of ADNP in HCC cell lines were verified by vitro experiments. RESULTS: ADNP was upregulated in most cancers and patients with elevated ADNP expression were related to poor survival in several types of cancers including HCC. Functional enrichment analysis showed ADNP participated in the pathways correlated with coagulation cascades and DNA double strand break repair. Further, ADNP exhibited a negative correlation with the immune score, stromal score, estimated score, and chemokines, and a positive correlation with cancer-associated fibroblasts, myeloid-derived suppressor cells, neutrophils, regulatory T cells, and endothelial cells. Immunochemistry and western blotting results demonstrated ADNP was up-regulated in HCC. Vitro experiments verified that suppressing the ADNP expression significantly inhibited the proliferation, invasion and migration and elevated the radiosensitivity via decreasing DNA damage repair in HCC. CONCLUSION: ADNP might play an oncogene and immunosuppression role in tumor immune infiltration and response, thus influencing the prognosis. Its downregulation could attenuate the proliferation, invasion, migration, radioresistance of HCC. Our results indicated the potential of ADNP as a promising biomarker to predict the survival of HCC patients, providing a theoretical basis for novel integrative strategies.

A web-based nomogram model for predicting the overall survival of hepatocellular carcinoma patients with external beam radiation therapy: A population study based on SEER database and a Chinese cohort.

Background: External beam radiation therapy (EBRT) for hepatocellular carcinoma (HCC) is rarely used in clinical practice. This study aims to develop and validate a prognostic nomogram model to predict overall survival (OS) in HCC patients treated with EBRT. Method: We extracted eligible data of HCC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients were randomly divided into a training cohort (n=1004) and an internal validation cohort (n=429), and an external validation cohort composed of a Chinese cohort (n=95). A nomogram was established based on the independent prognostic variables identified from univariate and multivariate Cox regression analyses. The effective performance of the nomogram was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA). Results: T stage, N stage, M stage, AFP, tumor size, surgery, and chemotherapy were independent prognostic risk factors that were all included in the nomogram to predict OS in HCC patients with EBRT. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the prediction model was 0.728 (95% confidence interval (CI): 0.716-0.740), 0.725 (95% CI:0.701-0.750), and 0.696 (95% CI:0.629-0.763), respectively. The 6-, 12-,18- and 24- month areas under the curves (AUC) of ROC in the training cohort were 0.835 、0.823 、0.810, and 0.801, respectively; and 0.821 、0.809 、0.813 and 0.804 in the internal validation cohort, respectively; and 0.749 、0.754 、0.791 and 0.798 in the external validation cohort, respectively. The calibration curves indicated that the predicted value of the prediction model performed well. The DCA curves showed better clinical practicability. In addition, based on the nomogram, we established a web-based nomogram to predict the OS of these patients visually. Conclusion: Based on the SEER database and an independent external cohort from China, we established and validated a nomogram to predict OS in HCC patients treated with EBRT. In addition, for the first time, a web-based nomogram model can help clinicians judge the prognoses of these patients and make better clinical decisions.