A more novel and robust gene signature predicts outcome in patients with esophageal squamous cell carcinoma.
Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. The latest research has displayed that tumor immune cell infiltration (ICI) is closely connected with the ESCC patients' clinical prognosis. This study was designed to construct a gene signature based on the ICI of ESCC to predict prognosis.
Based on the selection criteria we set, the eligible ESCC cases from the GSE53625 and TCGA-ESCA datasets were chosen for the training cohort and the validation cohort, respectively. Unsupervised clustering detailed grouped ESCC cases of the training cohort based on the ICI profile. We determined the differential expression genes (DEGs) between the ICI clusters, and, subsequently, we adopted the univariate Cox analysis to recognize DEGs with prognostic potential. These screened DEGs underwent a Lasso regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS. More importantly, we listed similar studies in the most recent year and compared theirs with ours. We performed the functional annotation, immune relevant signature correlation analysis, and immune infiltrating analysis to thoroughly understand the functional mechanism of the signature and the immune cells' roles in the gene signature's predicting capacity.
A sixteen-gene signature (ARSD, BCAT1, BIK, CLDN11, DLEU7-AS1, GGH, IGFBP2, LINC01037, LINC01446, LINC01497, M1AP, PCSK2, PCSK5, PPP2R2A, TIGD7, and TMSB4X) was generated from the Lasso model. We then confirmed the signature as having solid and stable prognostic capacity by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GSEA uncovered the specifically mechanism of action related to the gene signature. Two immune relevant signatures, including GZMA and LAG3 were identified associating with our signature. The immune-infiltrating analysis identified crucial roles of resting mast cells, which potentially support the sixteen-gene signature's prognosis ability.
We discovered a robust sixteen-gene signature that can accurately predict ESCC prognosis. The immune relevant signatures, GZMA and LAG3, and resting mast cells infiltrating were closely linked to the sixteen-gene signature's ability.
Ma C
，Luo H
《-》
Prognostic value of esophageal cancer immune prognostic index in advanced esophageal squamous cell carcinoma patients with anti-programmed cell death-1 therapy.
This study aimed to determine whether the immune prognostic index (ECIPI), based on hemoglobin (Hb) and neutrophil-to-lymphocyte ratio (NLR), could predict the prognosis in patients with advanced esophageal squamous cell carcinoma (ESCC) receiving programmed cell death-1 (PD-1) inhibitor treatment.
Advanced ESCC patients who had been treated with PD-1 inhibitors from Jan 2016 to Oct 2021 were included. Kaplan-Meier method and Cox proportional hazards regression were used to analyze progression-free survival (PFS) and overall survival (OS). The overall response rate (ORR) was the percentage of complete and partial responses. Univariate and multivariate analyses were used for estimating hazard ratio (HR) and 95% confidence interval (CI). Patients were grouped by ECIPI (good: Hb > 105 g/L and NLR ≤ 4.3; intermediate: Hb ≤ 105 g/L and NLR ≤ 4.3, or Hb > 105 g/L and NLR < 4.3; poor: Hb ≤ 105 g/L and NLR > 4.3). Variables for the multivariate model were selected if the p-value was below 0.05 in the univariate analysis. All statistical comparisons were two-way, and a p-value below 0.05 was set as statistical significance.
Totally, of 123 ESCC patients with stage III or IV were included in the study. Efficacy evaluation showed that patients with pretreatment ECIPI good had the best ORR compared with those with ECIPI intermediate and ECIPI poor (53% vs. 22% vs. 8%, p < 0.01). Multivariate analysis showed that ECIPI was an independent influential factor for PFS (p = 0.004) and OS (p < 0.001). Kaplan-Meier curves demonstrated that patients with ECIPI good had the longest PFS (median: 11.6 vs. 3.5 vs. 1.7 months, p < 0.0001) and OS (median: 23.6 vs. 16.7 vs. 4.0 months, p < 0.0001) compared with those with ECIPI intermediate and ECIPI poor. Subgroup analysis indicated that ECIPI good was associated with improved PFS and OS in patients with ECOG 0-1, PD-1 inhibitor plus chemotherapy, first-line treatment, and smoke (all p < 0.05).
Pretreatment ECIPI was associated with the prognosis in advanced ESCC patients with anti-PD-1 therapy, suggesting that ECIPI may be a useful tool to identify patients likely sensitive to PD-1 inhibitors.
Lu J
，Du L
，Lei X
，Zhang Z
... -
《Cancer Medicine》
Comprehensive Analysis of PD-L1 Expression, Immune Infiltrates, and m6A RNA Methylation Regulators in Esophageal Squamous Cell Carcinoma.
Esophageal squamous cell carcinoma (ESCC) is one of the most common cancer types and represents a threat to global public health. N6-Methyladenosine (m6A) methylation plays a key role in the occurrence and development of many tumors, but there are still few studies investigating ESCC. This study attempts to construct a prognostic signature of ESCC based on m6A RNA methylation regulators and to explore the potential association of these regulators with the tumor immune microenvironment (TIME).
The transcriptome sequencing data and clinical information of 20 m6A RNA methylation regulators in 453 patients with ESCC (The Cancer Genome Atlas [TCGA] cohort, n = 95; Gene Expression Omnibus [GEO] cohort, n = 358) were obtained. The differing expression levels of m6A regulators between ESCC and normal tissue were evaluated. Based on the expression of these regulators, consensus clustering was performed to investigate different ESCC clusters. PD-L1 expression, immune score, immune cell infiltration and potential mechanisms among different clusters were examined. LASSO Cox regression analysis was utilized to obtain a prognostic signature based on m6A RNA methylation modulators. The relationship between the risk score based on the prognostic signature and the TIME of ESCC patients was studied in detail.
Six m6A regulators (METTL3, WTAP, IGF2BP3, YTHDF1, HNRNPA2B1 and HNRNPC) were observed to be significantly highly expressed in ESCC tissues. Two molecular subtypes (clusters 1/2) were determined by consensus clustering of 20 m6A modulators. The expression level of PD-L1 in ESCC tissues increased significantly and was significantly negatively correlated with the expression levels of YTHDF2, METL14 and KIAA1429. The immune score, CD8 T cells, resting mast cells, and regulatory T cells (Tregs) in cluster 2 were significantly increased. Gene set enrichment analysis (GSEA) shows that this cluster involves multiple hallmark pathways. We constructed a five-gene prognostic signature based on m6A RNA methylation, and the risk score based on the prognostic signature was determined to be an independent prognostic indicator of ESCC. More importantly, the prognostic value of the prognostic signature was verified using another independent cohort. m6A regulators are related to TIME, and their copy-number alterations will dynamically affect the number of tumor-infiltrating immune cells.
Our study established a strong prognostic signature based on m6A RNA methylation regulators; this signature was able to accurately predict the prognosis of ESCC patients. The m6A methylation regulator may be a key mediator of PD-L1 expression and immune cell infiltration and may strongly affect the TIME of ESCC.
Guo W
，Tan F
，Huai Q
，Wang Z
，Shao F
，Zhang G
，Yang Z
，Li R
，Xue Q
，Gao S
，He J
... -
《Frontiers in Immunology》
ResearchGate
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钛学术
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