The WGCNA R package is capable of processing already normalized data for weighted correlation network analysis. The package can also be used to describe the correlation structure of high-level data structures such as gene expression profiles, proteomics data, etc. We used WCGNA to analyze normalized expression data to obtain a set of immune genes that are closely associated with LGG survival. And we obtained lncRNAs closely related to survival immune genes by correlation analysis between survival-related immune genes and lncRNAs. Finally, by WCGNA of lncRNAs associated with survival-related immune genes, we obtained lncRNA gene modules closely related to survival and immune.
The model made up by AC022034, CYTOR, HOTAIRM1, LINC01831, and AC104407 showed a good prediction of survival in LGG patients, especially at one year. It even has good results in forecasts of glioblastoma. Patients with differential LMPS had different prognoses in both the training and validation models, even in the externally validated dataset. The AUC of ROC is close to 0.6 or above in training and validation sets or TCGA and CGGA datasets. All above suggest a great predictive value of the present model for LGG (even glioma).
IDH1 mutation is a classical LGG molecular marker. IDH1 mutation patients have a better prognosis. LMPS is lower in both IDH1 mutation and TP53 mutation patients. It is consistent with the better survival of lower LMPS. It indicates the strong association of the model with common LGG molecular markers and the accuracy of predicting patient prognosis by our model.
There were significant differences in LPMS scores between the different clinical phenotypes. LMPS, AC022034, CYTOR, HOTAIRM1, LINC01831 were significantly more highly expressed in older patients, suggesting an effect of age grouping on the model. Histologically typed G3 patients have higher expression of LMPS and CYTOR, HOTAIRM1, LINC01831 compared to G2 patients. It suggests that histological type is a vital factor in the applicability of the model. At the same time, the model is a reference for histological typing. There were differential expressions of LPMS, CYTOR, and HOTAIRM1 in astrocytoma, oligodendroglioma, and mixed cell tumors. It suggests that this model may contribute to the diagnostic staging of gliomas. At the same time, It indicates that different tumor stratification combined with LMPS is further beneficial in determining the prognosis of patients.
TME is vital for revealing the immune status of patients and predicting their immune outcomes. The ESTIMATE algorithm for estimating the patient's immune microenvironment has been cited in various literature[26, 27]. We explored the correlation between LMPS and patients' TME. The Result suggested that LMPS scores were positive correlated with both patients' immune and stromal scores. It is consistent with previous literature that patients with high CD86 positively relating to stromal and immune scores had worse survival in LGG. It indicates that the model had a deep relationship with TME and could guide the immunotherapy.
Lei X et al. report the role of tumor immune infiltrating cells in tumors and suggest that CAR-T therapies may play a significant role in future treatments. By TIMER, our study showed that the expression of LMPS, AC0220354, AC104477, and CYTOR had significantly positive correlations with contents of CD8 T cells, neutrophils, dendritic cells, macrophages, and B cells. It suggests a close relationship between this model and immune-cell infiltration. We subsequently obtained the same results in CIBERSORT. The importance of cytotoxic CD8 T cells in mediating adaptive immune responses in various cancers, including gliomas, has been well documented. Liang J et al. reported that increased recruitment of neutrophils during anti-vascular endothelial growth factor treatment promotes glioma progression and may increase resistance to treatment. Dendritic cells induce an immune response through antigen presentation, and dendritic cell immunotherapy with inducing anti-tumor immunity has been in clinical trials since the 1990s. Tumor-related macrophages are recruited into the glioma environment and release a range of growth factors and cytokines in response to those recruitment factors produced by cancer cells. Through this feedback, tumor-associated macrophages promote tumor proliferation, survival, and migration. The specific role of B lymphocytes in the development of brain tumors remains unclear. In other types of cancer, tumor-infiltrating B cells are associate with the recognition of various tumor antigens. On the one side, the induction of CD4 + T cell-dependent CD8 + memory T cells contributes to the control of tumor invasion and metastasis. On the other side, this may activate the phenotype of M2 macrophages and promote tumorigenesis. The interaction between the role of anti-cancer immune cells, represented by CD8 T cells, and pro-cancer immune cells, expressed by neutrophils and M2 macrophages, may explain the poorer prognosis of patients with high LMPS. Or this may be related to the overproliferation of dysfunctional CD8 T cells. In any case, the present immune model, and the genes that make up the model, are indisputably linked to the infiltration of tumor immune cells. In other words, this model has the potential to serve as an indicator for LGG immunotherapy. Further, it has the potential to be used as a target for LGG immunotherapy.
To further explore the relationship between this model and immunity, we performed an enrichment analysis of LGG expression data according to LMPS. KEGG analysis revealed significant positive enrichment in calcium signaling pathways, cytokine-cytokine receptor interactions, JAK_STAT signaling pathway, and T cell receptor signaling pathways in the high LMPS group. All are closely related to the immune response and the progression of cancer[39–43]. GO analysis yielded positive enrichment of humoral immune responses, immune response regulating cell surface receptor signaling, positive regulation of cytokine production, regulation of immune effector processes, and activation of T cells in the high LMPS group. All five of these pathways are closely associated with cancer immunity and significantly inhibit cancer growth[44, 45]. We found that T cell-related pathways were significantly up in KEGG as well as GO analysis, so we performed co-expression analysis of related pathway genes. We caught that LMPS showed an overall positive correlation with the core genes of both activation of T cells and T cell receptor signaling pathways. It suggests that the model is closely associated with T-cell function.
To further explore the potential of this model, through Cytoscape and a pre-correlation analysis, we were able to obtain lncRNA action networks that were highly associated with this immune model. Patients with high expression of MIR4477B, MIR4666A, and MIR6071 had better survival outcomes. In contrast, patients with higher expression of MIR4648, AC104407, C8G, SEMA6D, AC093726, LGR4, and MIR635 had worse survival outcomes. The same with it. Expression of AC093726, LGR4, MIR635, MIR4648, and SEMA6D were significantly positively associated with B cells, CD8T cells, neutrophils, macrophages, and dendritic cells, while the expression of MIR6071, MIR4666A, and MIR4477B were significantly negatively associated with B cells, CD8T cells, neutrophils, macrophages, and dendritic cells. We also analyzed the relationships between hub genes and TME, immune-infiltrating cells. The AC104407 is common in genes that composing model and hub genes. This gene performed well in terms of survival prognosis, immune score, and TME. This suggests that the AC104407 may have a potential role to be explored. It has been reported that MIR6071 inhibits the development of glioblastoma by suppressing the PI3K/AKT/mTOR pathway. It is another validation of the effectiveness of our lncRNA network. MIR4666A has been reported to have a possible role in femoral necrosis , atrial fibrillation, and congestive heart failure, but the present study suggests a possible role in glioma immunity and prognosis. AC093726 also performed well in prognostic and immunological analyses, suggesting a possible association with tumor development. We have also performed a drug sensitivity analysis on the immunoprotein contained in hub genes. SEMA6D was positively correlated with the expression of multiple anti-cancer drugs, suggesting that SEMA6D is associated with the anti-cancer effects of multiple cancer drugs, on the contrary, LGR4 was negatively correlated with the expression of multiple anti-cancer drugs, suggesting that LGR4 is associated with cancer drug resistance.
Finally, we further explored possible immunotherapeutic compounds. In total, seven compounds have been the subject of cMap identification. Among these, loratadine has been used for combination chemotherapy for cancers such as breast cancer, melanoma, and gastrointestinal tumor. However, it has not been used in glioma yet. The results of this study suggest that loratadine may also be an effective agent for adjuvant chemotherapy in LGG. Fidaxomicin acts as an RNA polymerase inhibitor, binding to the RNAP-DNA complex and thereby inhibiting transcription initiation. The drug is currently in clinical use for refractory clostridial infections. Combined with the mechanism of action of fidaxomicin and the results of this study, the therapeutic effect of this drug on LGG is expected in the future. The efficacy of BRD-K86712468, BRD-K89010697, BRD-M52804417, BRD-K52369815, BRD-K49675259 in the treatment of LGG is still waiting for more studies.
Overall, an immune-related lncRNA prognostic model was obtained in the LGG training set based on the available immune genes, and its utility was validated in the LGG test set and two glioblastoma datasets. In all datasets, the high LMPS group had worse overall survival. Its prognostic value was further validated by analysis with IDH1 and TP53. The analysis of different clinical phenotypes then illustrates that the model may be able to be used for diagnosis and guiding immunotherapy. We could find a significant positive correlation between LMPS and tumor immune score, stromal score, and the abundance of CD8T cells, neutrophils, macrophages, dendritic cells, and B cells. GSEA got pathways that are closely related to tumors as well as immunity. Despite the complexity of the immune context, the model is indisputable in predicting the immune status of patients. Moreover, in combination with the prognosis of the model, it can help predict the outcome of immunotherapy in LGG patients. Our study also established a core network of interacting lncRNAs and validated the prognostic impact of the hub genes in this network and the correlation with TME. AC093726, LGR4, SEMA6D, AC104477, and MIR6071 are new factors that we found to be significantly associated with LGG prognosis and immunity. Even the therapeutic value of LGR4, SEMA6D was explored. In the end, we also looked for drugs related to possible immune-related treatments by cMap. Although more research is still needed, the study indicates that loratadine and fidaxomicin may both be promising in the adjuvant chemotherapy of LGG.