Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

Localization and density of tertiary lymphoid structures associate with molecular subtype and clinical outcome in colorectal cancer liver metastases

BACKGROUND

Tertiary lymphoid structures (TLSs) have been proposed to assess the prognosis of patients with cancer. Here, we investigated the prognostic value and relevant mechanisms of TLSs in colorectal cancer liver metastases (CRCLM).

METHODS

603 patients with CRCLM treated by surgical resection from three cancer centers were included. The TLSs were categorized according to their anatomic subregions and quantified, and a TLS scoring system was established for intratumor region (T score) and peritumor region (P score). Differences in relapse-free survival (RFS) and overall survival (OS) between groups were determined. Multiplex immunohistochemical staining (mIHC) was used to determine the cellular composition of TLSs in 40 CRCLM patients.

RESULTS

T score positively correlated with superior prognosis, while P score negatively associated with poor survival (all p<0.05). Meanwhile, T score was positively associated with specific mutation subtype of KRAS. Furthermore, TLSs enrichment gene expression was significantly associated with survival and transcriptomic subtypes of CRCLM. Subsequently, mIHC showed that the densities of Treg cells, M2 macrophages and Tfh cells were significantly higher in intratumor TLSs than in peritumor TLSs (p=0.029, p=0.047 and p=0.041, respectively), and the frequencies of Treg cells and M2 macrophages were positively correlated with P score, while the frequencies of Tfh cells were positively associated with T scores in intratumor TLSs (all p<0.05). Next, based on the distribution and abundance of TLSs, an Immune Score combining T score and P score was established which categorized CRCLM patients into four immune classes with different prognosis (all p<0.05). Among them, patients with higher immune class have more favorable prognoses. The C-index of Immune Class for RFS and OS was higher than Clinical Risk Score statistically. These results were also confirmed by the other two validation cohorts.

CONCLUSIONS

The distribution and abundance of TLSs is significantly associated with RFS and OS of CRCLM patients, and a novel immune class was proposed for predicting the prognosis of CRCLM patients.

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Aim

The aim of this study is to identify cuproptosis-related lncRNAs and construct a prognostic model for pancreatic cancer patients for clinical use.

Methods

The expression profile of lncRNAs was downloaded from The Cancer Genome Atlas database, and cuproptosis-related lncRNAs were identified. The prognostic cuproptosis-related lncRNAs were obtained and used to establish and validate a prognostic risk score model in pancreatic cancer.

Results

In total, 181 cuproptosis-related lncRNAs were obtained. The prognostic risk score model was constructed based on five lncRNAs (AC025257.1, TRAM2-AS1, AC091057.1, LINC01963, and MALAT1). Patients were assigned to two groups according to the median risk score. Kaplan-Meier survival curves showed that the difference in the prognosis between the high- and low-risk groups was statistically significant. Multivariate Cox analysis showed that our risk score was an independent risk factor for pancreatic cancer patients. Receiver operator characteristic curves revealed that the cuproptosis-related lncRNA model can effectively predict the prognosis of pancreatic cancer. The principal component analysis showed a difference between the high- and low-risk groups intuitively. Functional enrichment analysis showed that different genes were involved in cancer-related pathways in patients in the high- and low-risk groups.

Conclusion

The risk model based on five prognostic cuproptosis-related lncRNAs can well predict the prognosis of pancreatic cancer patients. Cuproptosis-related lncRNAs could be potential biomarkers for pancreatic cancer diagnosis and treatment.

Pan-Cancer Analysis Reveals the Relation between TRMT112 and Tumor Microenvironment

Dysregulated epigenetic modifications play a critical role in cancer development where TRMT112 is a member of the transfer RNA (tRNA) methyltransferase family. Till now, no studies have revealed the linkage between TRMT112 expression and diverse types of tumors. Based on TCGA data, we first probed into the relation between TRMT112 and prognosis and the potential role of TRMT112 in tumor microenvironment across 33 types of tumor. TRMT112 presented with increased expression in most cancers, which was significantly prognostic. Furthermore, TRMT112 was associated with tumor-associated fibroblasts in a variety of cancers. Additionally, a positive relationship was identified between TRMT112 expression and multiple tumor-related immune infiltrations, such as dendritic cells, CD8+ T cells, macrophages, CD4+ T cells, neutrophils, and B cells in lung adenocarcinoma and breast invasive carcinoma. In summary, our results suggest that TRMT112 might be a potential prognostic predictor of cancers and involved in regulating multiple cancer-related immune responses to some extent.

Prognostic implications of ferroptosis-associated gene signature in colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is one of the most common and fatal malignant tumors, which increases the difficulty of prognostic predictions. Thus, new biomarkers for the diagnosis and prognosis of COAD should be explored. Ferroptosis is a recently identified programmed cell death process that has the characteristics of iron-dependent lipid peroxide accumulation. However, the predictive value of ferroptosis-related genes (FRGs) for COAD still needs to be further clarified.

AIM

To identify some critical FRGs and construct a COAD patient prognostic signature for clinical utilization.

METHODS

The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus databases were the data sources for mRNA expression and corresponding COAD patient clinical information. Differentially expressed FRGs were recognized using R and Perl software. We constructed a multi-FRG signature of the TCGA-COAD cohort by performing a univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis. COAD patients from the Gene Expression Omnibus cohort were utilized for verification.

RESULTS

Our research showed that most of the FRGs (85%) were differentially expressed between the corresponding adjacent normal tissues and cancer tissues in the TCGA-COAD cohort. Seven FRGs were related to overall survival (OS) in the univariate Cox analysis (all P < 0.05). A model with five FRGs (AKR1C1, AKR1C3, ALOX12, CRYAB, and FDFT1) was constructed to divide patients into high- and low-risk groups. The OS of patients in the high-risk group was significantly lower than that of the low-risk group (all P < 0.01 in the TCGA and Gene Expression Omnibus cohorts). The risk score was an independent prognosticator of OS in the multivariate Cox analysis (hazard ratio > 1, P < 0.01). The predictive capacity of the model was verified by a receiver operating characteristic curve analysis. In addition, a nomogram based on the expression of five hub FRGs and risk score can precisely predict the OS of individual COAD cancer patients. Immune correlation analysis and functional enrichment analysis results revealed that immunology-related pathways were abundant, and the immune states of the high-risk group and the low-risk group were different.

CONCLUSION

In conclusion, a novel five FRG model can be utilized for predicting prognosis in COAD. Targeting ferroptosis may be a treatment option for COAD.

Prognostic Immune-Related Analysis Based on Differentially Expressed Genes in Left- and Right-Sided Colon Adenocarcinoma

Background

Colon 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.

Methods

The 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).

Results

A 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.

Conclusion

Through 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.