Identification of an Immune-Related LncRNA Signature in Gastric Cancer to Predict Survival and Response to Immune Checkpoint Inhibitors

Long Non-Coding RNA LOC339059 Attenuates IL-6/STAT3-Signaling-Mediated PDL1 Expression and Macrophage M2 Polarization by Interacting with c-Myc in Gastric Cancer

Background: Long non-coding RNA (lncRNA) was identified as a novel diagnostic biomarker in gastric cancer (GC). However, the functions of lncRNAs in immuno-microenvironments have not been comprehensively explored. In this study, we explored a critical lncRNA, LOC339059, that can predict the clinical prognosis in GC related to the modulation of PD-L1 and determined its influence upon macrophage polarization via the IL-6/STAT3 pathway. Methods: To date, accumulating evidence has demonstrated that the dysregulation of LOC339059 plays an important role in the pathological processes of GC. It acts as a tumor suppressor, regulating GC cell proliferation, migration, invasion, tumorigenesis, and metastasis. A flow cytometry assay showed that the loss of LOC339059 enhanced PDL1 expression and M2 macrophage polarization. RNA sequencing, RNA pull-down, RNA immunoprecipitation, Chip-PCR, and a luciferase reporter assay revealed the pivotal role of signaling alternation between LOC339059 and c-Myc. Results: A lower level of LOC339059 RNA was found in primary GC tissues compared to adjacent tissues, and such a lower level is associated with a poorer survival period (2.5 years) after surgery in patient cohorts. Moreover, we determined important immunological molecular biomarkers. We found that LOC339059 expression was correlated with PD-L1, CTLA4, CD206, and CD204, but not with TIM3, FOXP3, CD3, C33, CD64, or CD80, in a total of 146 GC RNA samples. The gain of LOC339059 in SGC7901 and AGS inhibited biological characteristics of malignancy, such as proliferation, migration, invasion, tumorigenesis, and metastasis. Furthermore, our data gathered following the co-culture of THP-1 and U937 with genomic GC cells indicate that LOC339059 led to a reduction in the macrophage cell ratio, in terms of CD68+/CD206+, to 1/6, whereas the selective knockdown of LOC339059 promoted the abovementioned malignant cell phenotypes, suggesting that it has a tumor-suppressing role in GC. RNA-Seq analyses showed that the gain of LOC339059 repressed the expression of the interleukin family, especially IL-6/STAT3 signaling. The rescue of IL-6 in LOC339059-overexpressing cells reverted the inhibitory effects of the gain of LOC339059 on malignant cell phenotypes. Our experiments verified that the interaction between LOC339059 and c-Myc resulted in less c-Myc binding to the IL-6 promoter, leading to the inactivation of IL-6 transcription. Conclusions: Our results establish that LOC339059 acts as a tumor suppressor in GC by competitively inhibiting c-Myc, resulting in diminished IL-6/STAT3-signaling-mediated PDL1 expression and macrophage M2 polarization.

The landscape of lncRNAs in gastric cancer: from molecular mechanisms to potential clinical applications

Gastric cancer (GC) is a highly prevalent and deadly malignant neoplasm worldwide. Currently, long non-coding RNAs (lncRNAs) have recently been identified as crucial regulators implicated in GC development and progression. Dysregulated expression of lncRNAs is commonly associated with enhanced tumor migration, invasiveness, and therapy resistance, highlighting their potential as promising targets for clinical applications. This review offers a comprehensive historical overview of lncRNAs in GC, describes the molecular mechanisms, and discusses the prospects and challenges of establishing lncRNAs as precision biomarkers.

Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Background

Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding.

Methods

We developed a multi-step process to identify immune-related long non-coding RNA signatures with prognostic connotation in multiple TCGA solid cancer datasets.

Results

Using the breast cancer dataset as a discovery cohort we found 2988 differentially expressed lncRNAs between immune favorable and unfavorable tumors, as defined by the immunologic constant of rejection (ICR) gene signature. Mapping of the lncRNAs to a coding-non-coding network identified 127 proxy protein-coding genes that are enriched in immune-related diseases and functions. Next, we defined two distinct 20-lncRNA prognostic signatures that show a stronger effect on overall survival than the ICR signature in multiple solid cancers. Furthermore, we found a 3 lncRNA signature that demonstrated prognostic significance across 5 solid cancer types with a stronger association with clinical outcome than ICR. Moreover, this 3 lncRNA signature showed additional prognostic significance in uterine corpus endometrial carcinoma and cervical squamous cell carcinoma and endocervical adenocarcinoma as compared to ICR.

Conclusion

We identified an immune-related 3-lncRNA signature with prognostic connotation in multiple solid cancer types which performed equally well and in some cases better than the 20-gene ICR signature, indicating that it could be used as a minimal informative signature for clinical implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03654-7.

Computational identification of immune-related lncRNA signature for predicting the prognosis and immune landscape of human glioblastoma multiforme

Emerging evidence shows immune-related long noncoding RNAs (ir-lncRNAs) perform critical roles in tumor progression and prognosis assessment. However, the identification of ir-lncRNAs and their clinical significance in human glioblastoma multiforme (GBM) remain largely unexplored. Here, a designed computational frame based on immune score was used to identify differentially expressed ir-lncRNAs (DEir-lncRNAs) from The Cancer Genome Atlas (TCGA) GBM program. The immune-related lncRNA signature (IRLncSig) composed of prognosis-related DEir-lncRNAs selected by Cox regression analysis and its clinical predictive values were verified, which was further validated by another dataset from the Gene Expression Omnibus database (GEO). Subsequently, the association between IRLncSig and immune cell infiltration, immune checkpoint inhibitor (ICI) biomarkers, O6-methylguanine-DNA methyltransferase (MGMT) gene expression, and biological function were also analyzed. After calculation, five prognosis-related ir-lncRNAs were included in the establishment of IRLncSig. The risk assessment based on IRLncSig indicated that the high-IRLncSig-score group was significantly associated with poor prognosis (p < 0.001), significant aggregation of macrophages (p < 0.05), higher ICI biomarker expression, and MGMT gene expression (p < 0.05). Signature-related lncRNAs may be involved in immune activities in the tumorigenesis and progression of GBM. In summary, the novel IRLncSig shows a promising clinical value in predicting the prognosis and immune landscape of GBM.

N6-methyladenosine-related lncRNAs identified as potential biomarkers for predicting the overall survival of Asian gastric cancer patients

Objective

Gastric cancer (GC) is one of the most prevalent malignant tumors in Asian countries. Studies have proposed that lncRNAs can be used as diagnostic and prognostic indicators of GC due to the high specificity of lncRNAs expression involvement in GC. Recently, N6-methyladenosine (m6A) has also emerged as an important modulator of the expression of lncRNAs in GC. This study aimed at establishing a novel m6A-related lncRNAs prognostic signature that can be used to construct accurate models for predicting the prognosis of GC in the Asian population.

Methods

First, the levels of m6A modification and m6A methyltransferases expression in GC samples were determined using dot blot and western blot analyses. Next, we evaluated the lncRNAs expression profiles and the corresponding clinical data of 88 Asian GC patients retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of m6A-related lncRNAs between GC and normal tissues was investigated. The relationship between these target lncRNAs and potential immunotherapeutic signatures was also analyzed. Gene set enrichment analysis (GSEA) was performed to identify the malignancy-associated pathways. Univariate Cox regression, LASSO regression, and multivariate Cox regression analyses were performed to establish a novel prognostic m6A-related lncRNAs prognostic signature. Moreover, we constructed a predictive nomogram and determined the expression levels of nine m6A-related lncRNAs in 12 pairs of clinical samples.

Results

We found that m6A methylation levels were significantly increased in GC tumor samples compared to adjacent normal tissues, and the increase was positively correlated with tumor stage. Patients were then divided into two clusters (cluster 1 and cluster 2) based on the differential expression of the m6A-related lncRNAs. Results showed that there was a significant difference in survival probability between the two clusters (p = 0.018). Notably, the low survival rate in cluster 2 may be associated with high expression of immune cells (resting memory CD4⁺ T cells, p = 0.027; regulatory T cells, p = 0.0018; monocytes, p = 0.00095; and resting dendritic cells, p = 0.015), and low expression of immune cells (resting NK cells, p = 0.033; and macrophages M1, p = 0.045). Enrichment analysis indicated that malignancy-associated biological processes were more common in the cluster 2 subgroup. Finally, the risk model comprising of six m6A-related lncRNAs was identified as an independent predictor of prognoses, which could divide patients into high- or low-risk groups. Time-dependent ROC analysis suggested that the risk score could accurately predict the prognosis of GC patients. Patients in the high-risk group had worse outcomes compared to patients in the low-risk group, and the risk score showed a positive correlation with immune cells (resting memory CD4⁺ T cells, R = 0.31, P = 0.038; regulatory T cells, R = 0.42, P = 0.0042; monocytes, R = 0.42, P = 0.0043). However, M1 macrophages (R = -0.37, P = 0.012) and resting NK cells (R = -0.31, P = 0.043) had a negative correlation with risk scores. Furthermore, analysis of clinical samples validated the weak positive correlation between the risk score and tumor stage.

Conclusions

The risk model described here, based on the six m6A-related lncRNAs signature, and may predict the clinical prognoses and immunotherapeutic response in Asian GC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09801-z.