Presence Of Clinical Parameters Research Articles

Relationship of Tumor-Associated Macrophage Population Detected by CD68 PG-M1, CD68 KP1, and CD163 with Latent EBV Infection and Prognosis in Classical Hodgkin Lymphoma.

To evaluate the quantity of tumor-associated macrophages (TAMs) in cases of Hodgkin Lymphoma of classical type (cHL), and to reveal possible associations between TAM intensity and latent Epstein-Barr virus (EBV) infection, overall survival, progression-free survival, prognostic indices, and clinicopathological parameters. A total 46 cases of cHL with complete clinical records were selected and re-evaluated histopathologically. Staining for CD68 (PG-M1; KP1 clones) and CD163 was evaluated and the cut-off values were defined. Also, all cases were evaluated using the chromogen in situ hybridization (CISH) method with EBER (Epstein-Barr virus-encoded RNA) probes for the presence of possible EBV infection. It was found that high expression levels of PG-M1 and high International Prognostic Scores (IPS) were associated with shortened overall survival (p=0.047, p=0.013). Cases with 2 or less areas of nodal region involvement were observed to have longer progression-free survival period (p=0.043). Higher expression levels of CD68 PG-M1, CD68 KP1, and CD163 were found to show significant associations with the presence of some clinical parameters such as the presence of B symptoms, spleen involvement, and the presence of EBV infection. Our findings suggest that increase of PG-M1+ TAM is associated with shortened overall survival, while higher expressions of all immunohistochemical markers are statistically significantly associated with the presence of EBV infection and clinical parameters mentioned above. These findings indicate that highlighting the TAM rate via macrophage markers in cases of cHL could be helpful in determining the prognostic risk groups and the relevant results should be mentioned in pathology reports.

A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer.

Purpose Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P < 0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P < 0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P < 0.001, and P < 0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P < 0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P < 0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer.

Identification of a potential gene signature for improved diagnosis in non-small cell lung cancer (NSCLC) patient is necessary. Here, we aim to establish and validate the prognostic efficacy of a gene set that can predict prognosis and benefits of adjuvant chemotherapy (ACT) in NSCLC patients from various ethnicities. An 8-gene signature was calculated from the gene expression of 181 patients using univariate Cox proportional hazard regression analysis. The prognostic value of the signature was robustly validated in 1,477 patients from five microarray independent data sets and one RNA-seq data set. The 8-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses [hazard ratio (HR): 2.84, 95% confidence interval CI (1.74-4.65), p=3.06e-05, [HR] 2.62, 95% CI (1.51-4.53), p=0.001], respectively. Subset analysis demonstrated that the 8-gene signature could identify high-risk patients in stage II-III with improved survival from ACT [(HR) 1.47, 95% CI (1.01-2.14), p=0.044]. The 8-gene signature also stratified risk groups in EGFR-mutated and wild-type patients. In conclusion, the 8-gene signature is a strong and independent predictor that can significantly stratify patients into low- and high-risk groups. Our gene signature also has the potential to predict patients in stage II-III that are likely to benefit from ACT.