Genetic variants in NPAS2 gene and clinical outcomes of resectable non-small-cell lung cancer

Prediction of lung adenocarcinoma prognosis and diagnosis with a novel model anchored in circadian clock-related genes

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients' data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

NPAS2, transcriptionally activated by ARRB1, promotes the malignant behaviours of lung adenocarcinoma cells and regulates the reprogramming of glucose metabolism

Lung adenocarcinoma (LUAD) is a serious threat to public health and is accompanied by increased morbidity and mortality worldwide. Neuronal PAS domain protein2 (NPAS2) has been confirmed as an oncogene in LUAD; however, little is known about its molecular mechanism. Here, the expression level of NPAS2 was detected in LUAD cell lines and 16HBE cells. Gain- and loss-of-function experiments were performed. Cell Counting Kit-8, colony formation, flow cytometry, wound-healing and Transwell assays were conducted to assess cell proliferation, apoptosis, migration and invasion, respectively. Reprogramming of glucose metabolism was evaluated via oxygen consumption rate (OCR), complexes activities, lactic production and glucose consumption. The expression of critical proteins was examined by western blot. We demonstrated aberrant upregulation of NPAS2 and β-arrestin-1 (ARRB1) in LUAD cell lines. ARRB1 was found to be a critical transcription factor of NPAS2 with binding sites within the promoter region of NPAS2, thereby causing its transcriptional activation. Functional experiments revealed that NPAS2 depletion significantly inhibited the malignant behaviours of A549 cells by suppressing cell proliferation, migration, invasion and epithelial-mesenchymal transition and promoting cell apoptosis. Meanwhile, NPAS2 depletion increased OCR and activities of complexes (I, II, III and V), and reduced lactic acid production and glucose uptake in A549 cells, indicating that NPAS2 depletion inhibited aerobic glycolysis, accompanied by reduced expression of glycolytic enzymes. However, the changes caused by NPAS2 knockdown were partly restored by ARRB1 overexpression. In conclusion, our study suggests that ARRB1 could transcriptionally activate NPAS2, facilitating malignant activities and glycolysis, and ultimately promoting the progression of LUAD, proving a novel therapeutic strategy for the treatment of LUAD.

High expression of the circadian clock gene NPAS2 is associated with progression and poor prognosis of gastric cancer: A single-center study

BACKGROUND

The prognostic assessment of patients after surgical resection of gastric cancer (GC) patients is critical. However, the role of the circadian clock gene NPAS2 expression in GC remains unknown.

AIM

To explore the relationship between NPAS2 and the survival prognosis of GC patients and clarify its role in evaluating GC prognosis.

METHODS

The tumor tissues and clinical data of 101 patients with GC were collected retrospectively. Immunohistochemical staining (IHC) was used to detect the expression of NPAS2 protein in GC and adjacent tissues. Univariate and multivariate Cox regression analysis was used to determine the independent prognostic factors of GC, and a nomogram prediction model was established. The receiver operating characteristic (ROC) curve, the ROC area under the curve, the calibration curve, and C-index were used to evaluate the predictive effectiveness of the model. Kaplan Meier analysis was used to compare the risk stratification of subgroups according to the median score in the nomogram model of each patient.

RESULTS

Microarray IHC analysis showed that the positive rate of NPAS2 protein expression in GC tissues was 65.35%, which was significantly higher than 30.69% in adjacent tissues. The high expression of NPAS2 was correlated with tumor-node-metastasis (TNM) stage (P < 0.05), pN stage (P < 0.05), metastasis (P < 0.05), venous invasion (P < 0.05), lymphatic invasion (P < 0.05), and lymph node positive (P < 0.05) of GC. Kaplan Meier survival analysis showed that the 3-year overall survival (OS) of patients with high NPAS2 expression was significantly shortened (P < 0.0001). Univariate and multivariate COX regression analysis showed that TNM stage (P = 0.009), metastasis (P = 0.009), and NPAS2 expression (P = 0.020) were independent prognostic factors of OS in GC patients for 3 years. The nomogram prediction model based on independent prognostic factors has a C-Index of 0.740 (95%CI: 0.713-0.767). Furthermore, subgroup analysis showed that the 3-year OS time of the high-risk group was significantly lower than that of the low-risk group (P < 0.0001).

CONCLUSION

NPAS2 is highly expressed in GC tissues and is closely related to worse OS in patients. Therefore, the evaluation of NPAS2 expression may be a potential marker for GC prognosis evaluation. Notably, the nomogram model based on NPAS2 can improve the accuracy of GC prognosis prediction and assist clinicians in postoperative patient management and decision-making.

Identification of circadian clock genes as regulators of immune infiltration in Hepatocellular Carcinoma

Background: Multiple studies have reported that the immune system is under the control of a circadian clock, especially in cancers, but how circadian clock genes shape tumor immune cell infiltration in hepatocellular carcinoma (HCC) remains unclear. Methods: The rhythmicity of circadian clock genes was investigated using the GETx database. The expression and methylation level of circadian clock genes in HCC and paracancerous was evaluated using the GETx and TCGA databases. The differential expression of circadian clock genes in HCC was analyzed using the "limma" package of the R 4.0.4 software. The prognosis of each circadian clock gene was accessed by Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. Quantitative real-time PCR and immunohistochemistry (IHC) was carried out to confirm the results. The relationship between circadian rhythm and immune infiltration in HCC was evaluated using the TIMER database and the CIBERSORT algorithm. Results: In addition to RORA, RORB, and ARNTL2, there was a rhythmic expression of other circadian clock genes in liver tissue. The correlation between the expression of circadian clock genes differed when comparing HCC and liver tissue. HCC patients who express low levels of PER-1and CRY2 had a poor overall survival (OS). In contrast, patients with higher expression of NPAS2 had a poor prognosis. In HCC, the expression of the PER-1, CRY2, and NPAS2 genes was closely related to immune infiltration. Conclusion: Our study indicated the disruption of the expression of circadian clock-regulated genes in HCC and identified PER-1, CRY2, and NPAS2 as independent predictors of survival. These genes may be applied as candidate molecular targets for diagnosis and therapy of HCC.

Circadian Rhythm Dysregulation and Leukemia Development: The Role of Clock Genes as Promising Biomarkers

The circadian clock (CC) is a daily system that regulates the oscillations of physiological processes and can respond to the external environment in order to maintain internal homeostasis. For the functioning of the CC, the clock genes (CG) act in different metabolic pathways through the clock-controlled genes (CCG), providing cellular regulation. The CC’s interruption can result in the development of different diseases, such as neurodegenerative and metabolic disorders, as well as cancer. Leukemias correspond to a group of malignancies of the blood and bone marrow that occur when alterations in normal cellular regulatory processes cause the uncontrolled proliferation of hematopoietic stem cells. This review aimed to associate a deregulated CC with the manifestation of leukemia, looking for possible pathways involving CG and their possible role as leukemic biomarkers.

Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

Objective

Precision medicine with molecular profiles has revolutionized the management of lung cancer contributing to improved prognosis. Herein, we aimed to uncover the gene expression profiling of transcription factors (TFs) in lung cancer as well as to develop a TF-based genomic model.

Methods

We retrospectively curated lung cancer patients from public databases. Through comparing mRNA expression profiling between lung cancer and normal specimens, specific TFs were determined. Thereafter, a TF genomic model was developed with univariate Cox regression and stepwise multivariable Cox analyses, which was verified through the GSE72094 dataset. Gene set enrichment analyses (GSEA) were presented. Downstream targets of TFs were predicted with ChEA, JASPAR, and MotifMap projects, and their biological significance was investigated through the clusterProfiler algorithm.

Results

In the TCGA cohort, we proposed a TF-based genomic model, comprised of SATB2, HLF, and NPAS2. Lung cancer individuals were remarkably stratified into high- and low-risk groups. Survival analyses uncovered that high-risk populations presented unfavorable survival outcomes. ROCs confirmed the excellent predictive potency in patients' prognosis. Additionally, this model was an independent prognostic indicator in accordance with multivariate analyses. The clinical implication of the model was well verified in an independent dataset. High risk score was in relation to carcinogenic pathways. Downstream targets were characterized by immune and carcinogenic activation.

Conclusion

The proposed TF genomic model acts as a promising marker for estimation of lung cancer patients' outcomes. Prospective research is required for testing the clinical utility of the model in individualized management of lung cancer.