[New TNM classification of malignant lung tumours]

PRMT6 serves an oncogenic role in lung adenocarcinoma via regulating p18

Lung adenocarcinoma (LUAD), a major subtype of lung cancer, is the leading cause of cancer‑related mortality worldwide. Previous studies have determined the role of the protein arginine methyltransferases (PRMTs) in the physiology and pathology of LUAD. However, to the best of our knowledge, no empirical studies have been performed determining the association between protein arginine methyltransferase 6 (PRMT6) and LUAD. The present study aimed to determine the expression levels of PRMT6 in LUAD and its association with the clinicopathological characteristics. The effect of PRMT6 knockdown on cell growth was analyzed and chromatin immunoprecipitation (ChIP) assay was used to investigate the regulatory mechanisms of PRMT6 on downstream gene expression. In addition, a xenograft model was used to determine whether the PRMT6‑regulated expression levels of p18 in vitro could be validated in vivo. PRMT6 overexpression in LUAD is associated with high clinical stage, lymph node metastasis and poor clinical outcomes. Furthermore, the silencing of PRMT6 significantly reduced the enrichment of Histone H3 asymmetric demethylation at arginine 2 in the promoter region of the p18 gene, thereby activating the expression of the gene. This, in turn, induced G1/S phase cell cycle arrest, resulting in the inhibition of cell proliferation. The xenograft model also suggested that PRMT6 suppressed LUAD development by activating p18 expression in vivo. In conclusion, the findings of the present study suggested that PRMT6 may serve as an oncogene in the progression of LUAD through epigenetically suppressing p18 expression. Thus, PRMT6 may represent a novel potential therapeutic target for LUAD.

Recommended changes for the 8th edition of the TNM classification for lung cancer-the findings of a single-institution evaluation

BACKGROUND

To evaluate the efficacy of the nodal descriptors and subgroups proposed by International Association for the Study of Lung Cancer (IASLC) in the 8th edition of the TNM classification system and to provide references for future editions.

METHODS

A total of 3,177 patients with non-small cell lung cancer at the Beijing Cancer Hospital were classified based on the following three methods: (I) the N descriptors in the 8th edition of the TNM classification system: N0, N1, N2, and N3; (II) the IASLC-proposed N subgroups: N1a, N1b, N2a1, N2a2, and N2b; (III) our more extensive division method: N1a, N1b, N1c, N2a1, N2a2, N2b1, N2b2, N2c, N3a, and N3b. Five-year survival analysis was performed using the Kaplan-Meier method, and differences between subgroups were evaluated using the log-rank test.

RESULTS

(I) A significant survival difference was found between each adjacent N descriptor; (II) the difference between each adjacent subgroup N descriptor was significant, but the difference between N1b and N2a1 was not; (III) in our proposed method, a significant difference was found between all the subgroups apart from N2a2 and N2b1, N2b1 and N2b2, N2c and N3a, and N3a and N3b.

CONCLUSIONS

The N descriptors in the 8th edition of the tumor, node, and metastasis (TNM) classification system are consistent with our data. Although our more extensive division method could distinguish between patients at different stages, its implementation is complicated; thus, we recommend the implementation of the IASLC-proposed subgroups with the addition of the N1b and N2a1 groups.

Growth patterns of small peripheral squamous cell carcinoma of the lung and their impacts on pathological and biological characteristics of tumor cells

PURPOSE

The growth pattern of peripheral squamous cell carcinoma (SCC) of the lung is divided into two types: alveolar space-filling (ASF) growth and alveolar space-destructive (ASD) growth. The aim of this study was to investigate the clinicopathological differences between cancer cells displaying ASF and ASD growth.

METHODS

We analyzed 155 patients with peripheral SCC measuring 30 mm or less in diameter. The proportion of ASF in the total tumor area (%ASF) was determined using digital image analysis. We examined the clinicopathological characteristics of the cancer cells and compared the immunophenotypes of high %ASF tumors (> 30%) and low %ASF tumors (0%). Finally, we analyzed the prognostic impact of ASD area with small SCC cases (≤ 2.0 cm, n = 72).

RESULTS

Cases of high %ASF tumors showed significantly lower frequencies of lymphovascular invasion (p = 0.008). Immunohistochemical staining revealed that the expression score of laminin-5, invasive-related molecule, in cancer cells was significantly lower in high %ASF cases than in low %ASF cases (p = 0.001). Within the same tumor, laminin-5 expression in the ASF area was significantly lower than that in the ASD area (p = 0.001). The overall 5-year survival rate of patients with a larger ASD area (> 1.0 cm2) was significantly lower than that of patients with a smaller ASD area (≤ 1.0 cm2) (p = 0.017).

CONCLUSIONS

In this study, we clearly showed that cancer cells presenting with ASF represents a "less invasive phenotype" in peripheral SCC.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC.

METHODS

Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset.

RESULTS

Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients.

CONCLUSIONS

The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

A novel staging model to classify oesophageal squamous cell carcinoma patients in China

BACKGROUND

Oesophageal squamous cell carcinoma (ESCC) is the predominant subtype of oesophageal carcinoma in China, with the overall 5-year survival rate of <10%. The current tumour-node-metastasis (TNM) staging system has become so complex that it is not easy to use in the life expectancy assessment. We aim to combine clinical variables and biomarkers to develop and validate a relative simple and reliable model, named the FENSAM, for ESCC prognosis.

METHODS

To build the FENSAM, we analysed 22 potential prognostic factors from 461 patients, including 9 biomarkers (Ezrin, Fascin, desmocollin 2 (DSC2), pFascin, activating transcription factor 3 (ATF3), connective-tissue growth factor (CTGF), neutrophil gelatinase-associated lipocalin (NGAL), NGAL receptor (NGALR), and cysteine-rich angiogenic protein 61 (CYR61)) and other 13 clinical variables. We selected significant factors associated with survival of ESCC patients, and used them to build our FENSAM model. We then obtained the hazard risk score of the model to classify ESCC patients. In addition, we validated the model in an independent cohort of 290 patients from the same hospital. The predictive performance of the model was assessed by the Area under the Receiver Operating Characteristic Curve (AUC) and Kaplan-Meier survival analysis.

RESULTS

We found six markers significantly associated with survival of ESCC patients (Ezrin, Fascin, ATF3, surgery extent, N-stage, and M-stage). They were combined to create a novel four-stage FENSAM model for patients' classification. FENSAM possessed a high classification precision similar to the TNM staging system, but with a much simpler model. The efficiency of FENSAM was evaluated by different quantiles of AUC and the results of survival analysis. The validation result demonstrated the potential of the FENSAM model to improve classification accuracy for ESCC patients.

CONCLUSIONS

FENSAM provides an alternative classifier for ESCC patients with a high classification precision using a simple model.