Background The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. Methods The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by “ESTIMATE” and “CIBERSORT.” We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort. Results The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4+ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1. Conclusion The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

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BackgroundThis study aimed to establish a novel quantification system of ferroptosis patterns and comprehensively analyze the relationship between ferroptosis score (FS) and the immune cell infiltration (ICI) characterization, tumor mutation burden (TMB), prognosis, and therapeutic sensitivity in left-sided and right-sided colon cancers (LCCs and RCCs, respectively).MethodsWe comprehensively evaluated the ferroptosis patterns in 444 LCCs and RCCs based on 59 ferroptosis-related genes (FRGs). The FS was constructed to quantify ferroptosis patterns by using principal component analysis algorithms. Next, the prognostic value and therapeutic sensitivities were evaluated using multiple methods. Finally, we performed weighted gene co-expression network analysis (WGCNA) to identify the key FRGs. The IMvigor210 cohort, TCGA-COAD proteomics cohort, and Immunophenoscores were used to verify the predictive abilities of FS and the key FRGs.ResultsTwo ferroptosis clusters were determined. Ferroptosis cluster B demonstrated a high degree of congenital ICI and stromal-related signal enrichment with a poor prognosis. The prognosis, response of targeted inhibitors, and immunotherapy were significantly different between high and low FS groups (HSG and LSG, respectively). HSG was characterized by high TMB and microsatellite instability-high subtype with poor prognosis. Meanwhile, LSG was more likely to benefit from immunotherapy. ALOX5 was identified as a key FRG based on FS. Patients with high protein levels of ALOX5 had poorer prognoses.ConclusionThis work revealed that the evaluation of ferroptosis subtypes will contribute to gaining insight into the heterogeneity in LCCs and RCCs. The quantification for ferroptosis patterns played a non-negligible role in predicting ICI characterization, prognosis, and individualized immunotherapy strategies.

BackgroundColon adenocarcinoma (COAD) can be divided into left-sided and right-sided COAD (LCCs and RCCs, respectively). They have unique characteristics in various biological aspects, particularly immune invasion and prognosis. The purpose of our study was to develop a prognostic risk scoring model (PRSM) based on differentially expressed immune-related genes (IRGs) between LCCs and RCCs, therefore the prognostic key IRGs could be identified.MethodsThe gene sets and clinical information of COAD patients were derived from TCGA and GEO databases. The comparison of differentially expressed genes (DEGs) of LCCs and RCCs were conducted with appliance of “Limma” analysis. The establishment about co-expression modules of DEGs related with immune score was conducted by weighted gene co-expression network analysis (WGCNA). Furthermore, we screened the module genes and completed construction of gene pairs. The analysis of the prognosis and the establishment of PRSM were performed with univariate- and lasso-Cox regression. We employed the PRSM in the model group and verification group for the purpose of risk group assignment and PRSM accuracy verification. Finally, the identification of the prognostic key IRGs was guaranteed by the adoption of functional enrichment, “DisNor” and protein-protein interaction (PPI).ResultsA total of 215 genes were screened out by differential expression analysis and WGCNA. A PRSM with 16 immune-related gene pairs (IRGPs) was established upon the genes pairing. Furthermore, we confirmed that the risk score was an independent factor for survival by univariate- and multivariate-Cox regression. The prognosis of high-risk group in model group (P < 0.001) and validation group (P = 0.014) was significantly worse than that in low-risk group. Treg cells (P < 0.001) and macrophage M0 (P = 0.015) were highly expressed in the high-risk group. The functional analysis indicated that there was significant up-regulation with regard of lymphocyte and cytokine related terms in low-risk group. Finally, we identified five prognostic key IRGs associated with better prognosis through PPI and prognostic analysis, including IL2RB, TRIM22, CIITA, CXCL13, and CXCR6.ConclusionThrough the analysis and screening of the DEGs between LCCs and RCCs, we constructed a PRSM which could predicate prognosis of LCCs and RCCs, and five prognostic key IRGs were identified as well. Therefore, the basis for identifying the benefits of immunotherapy and immunomodulatory was built.

Background: The purpose of our study was to develop a prognostic risk model based on differential genomic instability-associated (DGIA) long non-coding RNAs (lncRNAs) of left-sided and right-sided colon cancers (LCCs and RCCs); therefore, the prognostic key lncRNAs could be identified.Methods: We adopted two independent gene datasets, corresponding somatic mutation and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Identification of differential DGIA lncRNAs from LCCs and RCCs was conducted with the appliance of “Limma” analysis. Then, we screened out key lncRNAs based on univariate and multivariate Cox proportional hazard regression analysis. Meanwhile, DGIA lncRNAs related prognostic model (DRPM) was established. We employed the DRPM in the model group and internal verification group from TCGA for the purpose of risk grouping and accuracy verification of DRPM. We also verified the accuracy of key lncRNAs with GEO data. Finally, the differences of immune infiltration, functional pathways, and therapeutic sensitivities were analyzed within different risk groups.Results: A total of 123 DGIA lncRNAs were screened out by differential expression analysis. We obtained six DGIA lncRNAs by the construction of DRPM, including AC004009.1, AP003555.2, BOLA3-AS1, NKILA, LINC00543, and UCA1. After the risk grouping by these DGIA lncRNAs, we found the prognosis of the high-risk group (HRG) was significantly worse than that in the low-risk group (LRG) (all p < 0.05). In all TCGA samples and model group, the expression of CD8+ T cells in HRG was lower than that in LRG (all p < 0.05). The functional analysis indicated that there was significant upregulation with regard to pathways related to both genetic instability and immunity in LRG, including cytosolic DNA sensing pathway, response to double-strand RNA, RIG-Ⅰ like receptor signaling pathway, and Toll-like receptor signaling pathway. Finally, we analyzed the difference and significance of key DGIA lncRNAs and risk groups in multiple therapeutic sensitivities.Conclusion: Through the analysis of the DGIA lncRNAs between LCCs and RCCs, we identified six key DGIA lncRNAs. They can not only predict the prognostic risk of patients but also serve as biomarkers for evaluating the differences of genetic instability, immune infiltration, and therapeutic sensitivity.