Objective To evaluate the prognostic value of C-reactive protein (CRP), performance status (PS), lactate dehydrogenase (LDH), albumin (ALB) and derived neutrophil-to-lymphocyte ratio (dNLR) composite index (C-PLAN) as a prognostic indicator in advanced esophageal cancer patients treated with immune checkpoint inhibitor (ICI). Methods Hematologic indexes of 147 patients with advanced esophageal cancer treated by ICI in the Affiliated Hospital of Yangzhou University were collected before the first immunotherapy. CRP, PS, LDH, ALB and dNLR were scored and added to obtain C-PLAN index. Chi-square test was used to analyze the correlation between C-PLAN index and clinicopathological features; Kaplan-Meier survival curve was used to analyze the effects of C-PLAN index on overall survival (OS) and progression-free survival (PFS). Univariate and multifactor Cox proportional regression models were used to analyze whether C-PLAN index could be used as an independent factor affecting prognosis. Results A total of 147 patients with advanced esophageal cancer were divided into low-risk group (scored < 2, n=46) and high-risk group (scored ≥2, n=101) according to C-PLAN index. C-PLAN index was not correlated with age, sex, PS score, smoking, clinical stage, body mass index, pathological type, treatment strategy and operation (P>0.05). PFS and OS in the low-risk group were significantly better than those in the high-risk group (P < 0.001). In univariate Cox regression analysis, C-PLAN index was the influencing factor of PFS (P < 0.001) and OS (P=0.002). In multivariate Cox analysis, C-PLAN index was an independent prognostic factor of PFS (P=0.001) and OS (P=0.006). Conclusion C-PLAN index can be used as a reliable clinical index to predict the prognosis of patients with advanced esophageal cancer treated by ICI.

Objective To investigate the prognostic value of N6-methyladenosine (m⁶A) modification related genes and immune infiltration in colorectal cancer (CRC) and construct a risk model for predicting outcome of patients. Methods The transcriptome data and matched clinical information of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The prognostic value of m⁶A modification related genes and immune infiltration were investigated using the consensus clustering method and single sample gene set enrichment analysis. The weighted gene co-expression network analysis (WGCNA) was used to identify prognostic genes related with m⁶A modification and immune infiltration. Lasso regression analysis was used to construct a multi-gene risk model. The expression differences of prognostic genes identified were further validated through expression differential analysis in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, the Kaplan-Meier was used to evaluate the predicting performance of the model in different subgroups and external validation cohorts. Results Both the m⁶A modification and immune infiltration phenotype could effectively stratify the prognosis of CRC patients from the TCGA cohort. Most m⁶A modification related genes were significantly correlated with immune infiltration in CRC tissues. Four following prognostic genes were selected using the WGCNA method combined with Lasso regression analysis: intelectin-1, lymphocyte antigen 6 complex locus G6D, atonal homolog 1 and matrix metalloproteinase 28. In colorectal cancer tissues, the expression levels of lymphocyte antigen 6 complex locus G6D and matrix metalloproteinase 28 exhibited significant differences compared to adjacent non-cancerous tissues (P < 0.05). The risk model constructed based on the above prognostic genes can effectively identify the potential risk population with poor prognosis in different clinical subgroups of the TCGA cohort and the GEO validated cohort. Conclusion A risk model based on m⁶A modification and immune infiltration could effectively predict the clinical outcome of CRC patients, and related prognostic genes have potential to be developed as molecular targets for CRC therapy.