A novel 10-gene immune-related lncRNA signature model for the prognosis of colorectal cancer

Identification of a PANoptosis-related long noncoding rna risk signature for prognosis and immunology in colon adenocarcinoma

BACKGROUND

PANoptosis, a complex programmed cell death (PCD) pathway that includes apoptosis, pyroptosis and necroptosis, is significantly involved in the progression of cancers. Long noncoding RNAs (lncRNAs) play crucial roles in PCD. However, the predictive value of PANoptosis-related lncRNAs (PRlncRNAs) for colon adenocarcinoma (COAD) has not been established.

METHODS

Gene expression data and clinical characteristics of patients with COAD were obtained from The Cancer Genome Atlas database. Differential expression analysis and Pearson correlation analysis were used to identify PRlncRNAs. In addition to least absolute shrinkage and selection operator, univariate and multivariate Cox regression analyses were employed to obtain PRlncRNAs for constructing a risk signature. Patients with COAD in the training set, testing set and entire set were stratified into high- and low-risk groups for further comparison of survival prognosis, using the median risk score as the cut-off point. Time-dependent receiver operating characteristic curves, a nomogram and multivariate Cox regression analysis were conducted to validate the risk signature in the testing set and the entire set. In addition, critical pathways, immune infiltration cells, immune checkpoint-related genes, Tumor Immune Dysfunction and Exclusion (TIDE) scores and antitumour drugs were compared between the two risk groups in the entire set. Correlations between ferroptosis, cuproptosis, disulfidptosis and the PRlncRNA risk score were evaluated. Finally, a competitive endogenous RNA (ceRNA) network was established, and enrichment analysis of the predicted mRNAs was performed using Gene Ontology (GO) analysis. The Kaplan-Meier plotter database was used as an external database to confirm the accuracy of the risk signature in predicting patient prognosis. Additionally, small interfering RNA (siRNA), a cell counting kit- 8 assay, a cell colony formation assay, quantitative polymerase chain reaction (qPCR) and an apoptosis assay were further employed to investigate the roles of AP003555.1 in colon cancer.

RESULTS

A risk signature comprising four PRlncRNAs (LINC01133, FOXD3-AS1, AP001066.1, and AP003555.1) was developed to predict the prognosis of patients with COAD. Kaplan‒Meier curves demonstrated significant differences in prognosis between the high- and low-risk groups across the three sets. Multivariate Cox regression analysis confirmed that the risk signature was an independent prognostic factor across the three sets. A nomogram, receiver operating characteristic curves and calibration curves indicated strong confidence in the risk signature. Using the CIBERSORT algorithm and gene set enrichment analysis, variations in infiltrating immune cells and immune processes were observed between the two risk groups. Furthermore, TIDE algorithm suggested that the high-risk group exhibited a lower risk of immunotherapy escape and better immunotherapy outcomes than the low-risk group. Distinct responses to various antitumour drugs were observed between the two risk groups. Additionally, we constructed a ceRNA network based on PRlncRNAs, and GO enrichment analysis of the predicted mRNAs revealed different functions. In addition, the results of the Kaplan‒Meier plotter database revealed that patients who exhibited high levels of LINC01133 and FOXD3-AS1 experienced significantly shorter overall survival than those with low levels of these lncRNAs. Specifically, in terms of functionality, AP003555.1 was found to be highly expressed in colon cancer tissue and promoted viability and proliferation while suppressing the apoptosis of colon cancer cells.

CONCLUSION

We identified a novel risk signature consisting of four PRlncRNAs, which is an independent prognostic indicator for patients with COAD. This PRlncRNA risk signature is potentially relevant for immunotherapy and could serve as a therapeutic target for COAD.

The Importance of HHLA2 in Solid Tumors-A Review of the Literature

Cancer immunotherapy is a rapidly developing field of medicine that aims to use the host's immune mechanisms to inhibit and eliminate cancer cells. Antibodies targeting CTLA-4, PD-1, and its ligand PD-L1 are used in various cancer therapies. However, the most thoroughly researched pathway targeting PD-1/PD-L1 has many limitations, and multiple malignancies resist its effects. Human endogenous retrovirus-H Long repeat-associating 2 (HHLA2, known as B7H5/B7H7/B7y) is the youngest known molecule from the B7 family. HHLA2/TMIGD2/KIRD3DL3 is one of the critical pathways in modulating the immune response. Recent studies have demonstrated that HHLA2 has a double effect in modulating the immune system. The connection of HHLA2 with TMIGD2 induces T cell growth and cytokine production via an AKT-dependent signaling cascade. On the other hand, the binding of HHLA2 and KIR3DL3 leads to the inhibition of T cells and mediates tumor resistance against NK cells. This review aimed to summarize novel information about HHLA2, focusing on immunological mechanisms and clinical features of the HHLA2/KIR3DL3/TMIGD2 pathway in the context of potential strategies for malignancy treatment.

Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients

Older patients with cancer, particularly those over 75 years of age, often experience poorer clinical outcomes compared to younger patients. This can be attributed to age-related comorbidities, weakened immune function, and reduced tolerance to treatment-related adverse effects. In the immune checkpoint inhibitors (ICI) era, age has emerged as an influential factor impacting the discovery of predictive biomarkers for ICI treatment. These age-linked changes in the immune system can influence the composition and functionality of tumor-infiltrating immune cells (TIICs) that play a crucial role in the cancer response. Older patients may have lower levels of TIICs infiltration due to age-related immune senescence particularly T cell function, which can limit the effectivity of cancer immunotherapies. Furthermore, age-related immune dysregulation increases the exhaustion of immune cells, characterized by the dysregulation of ICI-related biomarkers and a dampened response to ICI. Our review aims to provide a comprehensive understanding of the mechanisms that contribute to the impact of age on ICI-related biomarkers and ICI response. Understanding these mechanisms will facilitate the development of treatment approaches tailored to elderly individuals with cancer.

A glimpse of research cores and frontiers on the relationship between long noncoding RNAs (lncRNAs) and colorectal cancer (CRC) using the VOSviewer tool

As lncRNAs are essential participants in colorectal carcinogenesis. This study aimed to use the VOSviewer tool to access the research cores and frontiers on the relationship between lncRNAs and CRC. Our findings showed that the mechanism of lncRNA in the occurrence and development of CRC was the core theme of the field. (1) Immunotherapy and immune microenvironment of CRC and lncRNAs, (2) CRC and lncRNAs in exosomes and (3) CRC and lncRNA-targeted therapy might represent three research frontiers. A comprehensive understanding of their existing mechanisms and the search for new regulatory paradigms are the core topics of future research. This knowledge will also help us select appropriate targeting methods and select appropriate preclinical models to promote clinical translation and ultimately achieve precise treatment of CRC.

Prognosis analysis of necroptosis-related genes in colorectal cancer based on bioinformatic analysis

Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis.

Methods: CRC’s data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan–Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes’ expression was examined in the GSE41258 dataset.

Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan–Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset.

Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.

Special issue: informatics & data-driven medicine-2021

Modern medical diagnosis, treatment, or rehabilitation problems of the patient reach completely different levels due to the rapid development of artificial intelligence tools. Methods of machine learning and optimization based on the intersection of historical data of various volumes provide significant support to physicians in the form of accurate and fast solutions of automated diagnostic systems. It significantly improves the quality of medical services. This special issue deals with the problems of medical diagnosis and prognosis in the case of short datasets. The problem is not new, but existing machine learning methods do not always demonstrate the adequacy of prediction or classification models, especially in the case of limited data to implement the training procedures. That is why the improvement of existing and development of new artificial intelligence tools that will be able to solve it effectively is an urgent task. The special issue contains the latest achievements in medical diagnostics based on the processing of small numerical and image-based datasets. Described methods have a strong theoretical basis, and numerous experimental studies confirm the high efficiency of their application in various applied fields of Medicine.