Proteomic biomarkers for lung cancer progression

Silencing of circ_0088036 inhibits growth and invasion of lung adenocarcinoma through miR-203/SP1 axis

Lung cancer is the leading cause of cancer-related deaths worldwide. Circular RNA (circRNA) circ_0088036 is a recently discovered circRNA known for its roles in rheumatoid arthritis. The study aimed to study the function of circ_0088036 in lung adenocarcinoma (LUAD). Circ_0088036 expressions were analyzed in the Gene Expression Omnibus (GEO) database. The relationship between circ_0088036 expressions and clinicopathological data of LUAD was assessed. The messenger RNA and protein levels were analyzed by quantitative real-time polymerase chain reaction and Western blot. Cell viability, apoptosis, and invasion were tested by Cell Counting Kit-8, flow cytometry, and transwell assay. The direct interaction between microRNA-203 (miR-203) and circ_0088036 or specificity protein 1 (SP1) was confirmed by dual-luciferase reporter assay, RNA pull-down, and RNA immunoprecipitation assays. Circ_0088036 was overexpressed in LUAD from the analysis of the GEO database. The poor prognosis was found in the patients with high expressions of circ_0088036. The level of Circ_0088036 was increased in LUAD tissues and cells. In terms of function, the deletion of circ_0088036 inhibited LUAD tumorigenesis in vitro by repressing cell growth, invasion, and epithelial-mesenchymal transition (EMT). In mechanism, circ_0088036 could competitively sponge miR-203, thereby affecting the expressions of the target gene SP1. In addition, lessening of miR-203 and enlarging of SP1 could eliminate the anticancer effect of short hairpin RNA-circ_0088036 on LUAD cells. Besides, the knockout of circ_0088036 hindered the growth of xenografted tumors in vivo. Circ_0088036 promoted the LUAD cell growth, invasion, and EMT via modulating the miR-203/SP1 axis in LUAD.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

Objective

Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need.

Methods

The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape.

Results

Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions.

Conclusion

This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.

Spread through air spaces predicts a worse survival in patients with stage I adenocarcinomas >2 cm after radical lobectomy

Background

The aim of this study was to evaluate the significance of spread through air spaces (STAS) in early lung adenocarcinomas after radical lobectomy and lymphadenectomy.

Methods

A total of 242 patients with lung adenocarcinomas less than 4 cm (8th pStage I) were selected from the lung cancer patients surgically treated from January, 2009 to September, 2011. Pathological review focused on STAS as well as histological subtypes, blood vessel & neural invasion, pathological tumor size etc. Recurrence or disease-free survival (DFS) and overall survival (OS) were compared between patients as stratified by STAS and tumor size.

Results

STAS was observed in 33.47% (81/242) patients, which was significantly correlated with histological predominant subtype (χ²=25.903, P=0.093×10^-3) and differentiation grade (χ²=23.986, P=0.025×10^-3). Patients with STAS (+) showed a comparable PFS (P=0.268) and OS rates (P=0.100) in all stage I cases, but a significant lower PFS (P=0.029) and OS (P=0.013) in tumors within 2< tumors ≤4 cm. Multivariate analysis revealed STAS to be an independent worse prognostic factor in lung adenocarcinomas within 2< tumors ≤4 cm, both for PFS (P=0.004) and OS (P=0.002), while no significant difference was found in patients with tumors ≤2 cm (PFS, P=0.537; OS, P=0.448), after adjusting by other clinicopathological parameters as age, gender, smoking etc.

Conclusions

Presence of STAS was a significant worse predictor for pStage I patients with lung adenocarcinoma >2 cm who underwent radical lobectomy, while it is not significant in patients with tumor ≤2 cm. These findings may be helpful in assessing postoperative therapy stratified by tumor size and STAS status.

Gender specificity improves the early-stage detection of clear cell renal cell carcinoma based on methylomic biomarkers

AIM

The two genders are different ranging from the molecular to the phenotypic levels. But most studies did not use this important information. We hypothesize that the integration of gender information may improve the overall prediction accuracy.

MATERIALS & METHODS

A comprehensive comparative study was carried out to test the hypothesis. The classification of the stages I + II versus III + IV of the clear cell renal cell carcinoma samples was formulated as an example.

RESULTS & CONCLUSION

In most cases, female-specific model significantly outperformed both-gender model, as similarly for the male-specific model. Our data suggested that gender information is essential for building biomedical classification models and even a simple strategy of building two gender-specific models may outperform the gender-mixed model.