Background: Upregulation of the six-transmembrane epithelial antigen of prostate-1 (STEAP1) is closely associated with prognosis of numerous malignant cancers. However, its role in lung adenocarcinoma (LUAD), the most common type of lung cancer, remains unknown. This study aimed to investigate the role of STEAP1 in the occurrence and progression of LUAD and the potential mechanisms underlying its regulatory effects.Methods: STEAP1 mRNA and protein expression were analyzed in 40 LUAD patients via real-time PCR and western blotting, respectively. We accessed the clinical data of 522 LUAD patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to investigate the expression and prognostic role of STEAP1 in LUAD. Further, we performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) to elucidate the potential mechanism underlying the role of STEAP1 in LUAD. The protein-protein interaction (PPI) network of STEAP1 was analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes with significant positive and negative associations with STEAP1 were identified and their role in LUAD prognosis was predicted.Results: STEAP1 was significantly upregulated in LUAD patients and associated with LUAD prognosis. Further, TCGA data indicated that STEAP1 upregulation is correlated with the clinical prognosis of LUAD. GO and KEGG analysis revealed that the genes co-expressed with STEAP1 were primarily involved in cell division, DNA replication, cell cycle, apoptosis, cytokine signaling, NF-kB signaling, and TNF signaling. GSEA revealed that homologous recombination, p53 signaling pathway, cell cycle, DNA replication, apoptosis, and toll-like receptor signaling were highly enriched upon STEAP1 upregulation. Gene Expression Profiling Interactive Analysis (GEPIA) analysis revealed that the top 10 hub genes associated with STEAP1 expression were also associated with the LUAD prognosis.

Background The expression of progestin and adipoQ receptor 3 (PAQR3) is generally downregulated in multiple tumors, which is associated with tumor progression and poor prognosis. Methods The clinical value of PAQR3 was analyzed using various databases and in 60 patients with non-small cell lung cancer (NSCLC). In addition, cell counting kit-8 (CCK-8), colony formation, and flow cytometry assays were used to evaluate the effect of PAQR3 on the growth of NSCLC cells in vitro . Gene set enrichment analysis (GSEA) was performed to investigate the possible mechanism through which PAQR3 is involved in the progression of lung cancer. Furthermore, western blotting was employed to verify the relevant mechanism. Results The expression of PAQR3 was decreased in 60 NSCLC patients and was related to the histological subtype, lymph node metastasis, tumor size, and diagnosis of NSCLC. Patients with lung adenocarcinoma with increased PAQR3 expression tended to have a better prognosis. Besides, PAQR3 inhibited proliferation, clone formation, and cycle transition in NSCLC cells, but induced apoptosis. The results of GSEA showed that PAQR3 regulated the progression of lung cancer by affecting cell cycle, DNA replication, and the p53 signaling pathway. We confirmed that PAQR3 overexpression inhibited the expression of NF-κB, while it increased the expression of p53, phospho-p53, and Bax. On the contrary, PAQR3 inhibition played an opposite role in these proteins. Conclusion PAQR3 inhibited the growth of NSCLC cells through the NF-κB/P53/Bax signaling pathway and might be a new target for diagnosis and treatment.

Background BRAF-activated noncoding RNA (BANCR) is aberrantly expressed in various tumor tissues and has been confirmed to function as a tumor suppressor or oncogene in many types of cancers. Considering the conflicting results and insufficient sampling, a meta-analysis was performed to explore the prognostic value of BANCR in various carcinomas. Methods A comprehensive literature search of PubMed, Web of Science, EMBASE, Cochrane Library and the China National Knowledge Infrastructure (CNKI) was conducted to collect relevant articles. Results The pooled results showed a strong relationship between high BANCR expression and poor overall survival (OS) (HR (hazard ratio) =1.60, 95% confidence interval (CI): 1.19–2.15, P = 0.002) and recurrence-free survival (RFS) (HR = 1.53, 95% CI: 1.27–1.85, P < 0.00001). In addition, high BANCR expression predicted advanced tumor stage (OR (odds ratio) =2.39, 95% CI: 1.26–4.53, P = 0.008), presence of lymph node metastasis (OR = 2.03, 95% CI: 1.08–3.83, P = 0.03), positive distant metastasis (OR = 3.08, 95% CI: 1.92–4.96, P < 0.00001) and larger tumor sizes (OR = 1.63, 95% CI: 1.09–2.46, P = 0.02). However, no associations were found for smoking status (OR = 1.01, 95% CI: 0.65–1.56, P = 0.98), age (OR = 0.88, 95% CI: 0.71–1.09, P = 0.236) and sex (OR = 0.91, 95% CI: 0.72–1.16, P = 0.469). The sensitivity analysis of OS showed that the results of each publication were almost consistent with the combined results, and the merged results have high robustness and reliability. Conclusions The results showed that elevated BANCR expression was associated with unfavorable prognosis for most cancer patients, and BANCR could serve as a promising therapeutic target and independent prognostic predictor in most of cancer types.

Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be reflected through a simple graph or Euclidean distance, is worth paying attention to. For this purpose, we propose a novel similarity distance-based hypergraph embedding method (SDHE) for hyperspectral images DR. Unlike conventional graph embedding-based methods that only consider the affinity between two samples, SDHE takes advantage of hypergraph embedding to describe the complex sample relationships in high order. Besides, we propose a novel similarity distance instead of Euclidean distance to measure the affinity between samples for the reason that the similarity distance not only discovers the complicated geometrical structure information but also makes use of the local distribution information. Finally, based on the similarity distance, SDHE aims to find the optimal projection that can preserve the local distribution information of sample sets in a low-dimensional subspace. The experimental results in three hyperspectral image data sets demonstrate that our SDHE acquires more efficient performance than other state-of-the-art DR methods, which improve by at least 2% on average.

Background: This study aims to explore the dynamic survival probability of lung cancers after resection based on those had survived several years, provide more precise monitoring and treatment information for non-metastatic non-small cell lung cancer (NSCLC) patients.Materials and Methods: In the Surveillance, Epidemiology, and End Results (SEER) database (2000–2016), 95531 eligible non-metastatic NSCLC patients after surgery were enrolled, TNM stage were reclassified, the methods of condition survival probability (CS) and actuarial overall survival (OS) were used to explore the relationship between clinicopathological characteristics and cancer prognosis.Results: The 1-, 3-, 5- and 10-year OS of included patients were 83.6% (95%CI: 83%-84%), 62.9% (95%CI: 62.6%-63.1%), 50.8% (95%CI: 50.6%-51.0%) and 33.1% (95%CI: 32.7%-33.6%) respectively. For those already survived 1, 2, 3, 4 and 5 years after diagnosis, the probability for surviving an additional 3 years were 67%, 71%, 73%, 75% and 77% respectively. Enrolled population were reclassified into 9 cohorts including T1aN0, T1bN0, T1cN0, T2aN0, T2bN0, T3N0, T4N0, T1-4N1, T1-4N2 according to 8th TNM staging. According to the conditional survival probability, patients with unfavorable tumor stage diagnosed initially at surgery had the significant improvement in CS over time. Analysis based on other clinical features demonstrated similar conclusion that the poorer the initial diagnosis, the more significant the benefit of conditional survival over time.Conclusion: The worse the patient's prognosis, the more significant the benefit of time-dependent conditional survival probability, long-lived cancer patients may have a better cancer prognosis.