Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas

Reduced HLA-I Transcript Levels and Increased Abundance of a CD56dim NK Cell Signature Are Associated with Improved Survival in Lower-Grade Gliomas

BACKGROUND

Human leukocyte antigen class I (HLA-I) plays a pivotal role in shaping anti-tumour immunity by influencing the functionality of T cells and natural killer (NK) cells within the tumour microenvironment.

METHODS

Here, we explored the transcriptional landscape of HLA-I molecules across various solid cancer transcriptomes from The Cancer Genome Atlas (TCGA) database and assessed the impact of HLA-I expression on the clinical significance of tumour-infiltrating CD56dim and CD56bright NK cells.

RESULTS

Our analysis revealed that high HLA-I expression correlated with reduced patient survival in the TCGA lower-grade glioma (LGG) cohort, with this association varying by histopathological subtype. We then estimated the relative abundance of 23 immune and stromal cell signatures in LGG transcriptomes using a cellular deconvolution approach, which revealed that LGG patients with low HLA-I expression and high CD56dim NK cell abundance had better survival outcomes compared to those with high HLA-I expression and low CD56dim NK cell abundance. Furthermore, HLA-I expression was positively correlated with various inhibitory NK cell receptors and negatively correlated with activating NK cell receptors, particularly those within the killer cell lectin-like receptor (KLR) gene family. High co-expression of HLA-E and NKG2A predicted poor survival outcomes in LGG patients, whereas low HLA-E and high NKG2C/E abundance predicted more favourable outcomes, suggesting a potential modulatory role of HLA-I on the tumour-infiltrating cytotoxic CD56dim NK cell subset.

CONCLUSIONS

Overall, our study unveils a potential role for deregulated HLA-I expression in modulating the clinical impact of glioma-infiltrating CD56dim NK cells. These findings lay the foundation for future in-depth experimental studies to investigate the underlying mechanisms.

Limitations of nomogram models in predicting survival outcomes for glioma patients

Purpose

Glioma represents a prevalent and malignant tumor of the central nervous system (CNS), and it is essential to accurately predict the survival of glioma patients to optimize their subsequent treatment plans. This review outlines the most recent advancements and viewpoints regarding the application of nomograms in glioma prognosis research.

Design

With an emphasis on the precision and external applicability of predictive models, we carried out a comprehensive review of the literature on the application of nomograms in glioma and provided a step-by-step guide for developing and evaluating nomograms.

Results

A summary of thirty-nine articles was produced. The majority of nomogram-building research has used limited patient samples, disregarded the proportional hazards (PH) assumption in Cox regression models, and some of them have failed to incorporate external validation. Furthermore, the predictive capability of nomograms is influenced by the selection of incorporated risk factors. Overall, the current predictive accuracy of nomograms is moderately credible.

Conclusion

The development and validation of nomogram models ought to adhere to a standardized set of criteria, thereby augmenting their worth in clinical decision-making and clinician-patient communication. Prior to the clinical application of a nomogram, it is imperative to thoroughly scrutinize its statistical foundation, rigorously evaluate its accuracy, and, whenever feasible, assess its external applicability utilizing multicenter databases.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BACKGROUND

Glioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1-4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.

RESULTS

Our analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.

CONCLUSION

Our findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.

Exploring the prognostic potential of m6A methylation regulators in low-grade glioma: implications for tumor microenvironment modulation

BACKGROUND

The biological behavior of low-grade glioma (LGG) is significantly affected by N6-methyladenosine (m6A) methylation, an essential epigenetic alteration. Therefore, it is crucial to create a prognostic model for LGG by utilizing genes that regulate m6A methylation.

METHODS

Using TCGA and GTEx databases. We examined m6A modulator levels in LGG and normal tissues, and investigated PD-L1 and PD-1 expression, immune scores, immune cell infiltration, tumor immune microenvironment (TIME) and potential underlying mechanisms in different LGG clusters. We also performed immunohistochemistry and RT-qPCR to identify essential m6A adjustment factor.

RESULTS

The results showed that m6A regulatory element expression was significantly increased in LGG tissues and was significantly associated with TMIE. A substantial increase in PD-L1 and PD-1 levels in LGG tissues and high-risk cohorts was observed. PD-L1 expression was positively correlated with FTO, ZCCHC4, and HNRNPD, whereas PD-1 expression was negatively correlated with FTO, ZC3H7B, and HNRNPD. The prognostic signature created using regulators of m6A RNA methylation was shown to be strongly associated with the overall survival of LGG patients, and FTO and ZCCHC4 were confirmed as independent prognostic markers by clinical samples. Furthermore, the results revealed different TIME characteristics between the two groups of patients, indicating disrupted signaling pathways associated with LGG.

CONCLUSION

Our results present that the m6A regulators play vital role in regulating PD-L1/PD-1 expression and the infiltration of immune cells, thereby exerting a sizable impact on the TIME of LGG. Therefore, m6A regulators have precise predictive value in the prognosis of LGG.

Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma

BACKGROUND

Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients.

METHODS

RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs.

RESULTS

A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined.

CONCLUSIONS

A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma

BACKGROUND

Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients.

METHODS

In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values.

RESULTS

We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values.

CONCLUSIONS

Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD.

Novel Cuproptosis-Related Gene Signature for Precise Identification of High-Risk Populations in Low-Grade Gliomas

Background

Patients with low-grade glioma (LGG) have wildly varying average lifespans. However, no effective way exists for identifying LGG patients at high risk. Cuproptosis is a recently described form of cell death associated with the abnormal aggregation of lipid acylated proteins. Few investigations have been conducted on cuproptosis-associated genes and LGG thus far. The purpose of this research is to establish a predictive model for cuproptosis-related genes in order to recognise LGG populations at high risk.

Methods

We analyzed 926 LGGs from 2 public datasets, all of which were RNA sequencing datasets. On the basis of immune scores, the LGG population was split into different risk categories with X-tile. LASSO and Cox regressions were employed to filter cuproptosis-associated genes and construct prediction models. The accuracy of the predictive models was measured by using TCGA internal validation set and the CGGA external validation set. In addition, LGG immune cell infiltration was viewed using CIBERSORT and ssGSEA algorithms and correlation analysis was done with cuproptosis-related genes. Finally, immune escape capacity in LGG low- and high-risk groups was evaluated using the TIDE method.

Results

The prediction model constructed by four cuproptosis-related genes was used to identify high-risk populations in LGG. It performed well in training and all validation sets (AUC values: 0.915, 0.894, and 0.774). Meanwhile, we found that FDX1 and ATP7A in the four cuproptosis-related genes were positively correlated with immune response, while GCSH and ATP7B were opposite. In addition, the high immune score group had a lower TIDE score, indicating that their immune escape capacity was weak.

Conclusion

High-risk individuals in LGG can be reliably identified by the model based on cuproptosis-related genes. Furthermore, cuproptosis is closely related to tumor immune microenvironment, which gives a novel approach to treating LGG.

Ferroptosis-related NFE2L2 and NOX4 Genes are Potential Risk Prognostic Biomarkers and Correlated with Immunogenic Features in Glioma

Ferroptosis is a newfound mode of regulated cell death that may have potential to associate with prognostic or diagnostic factors in glioma. In this research, 5 genes related to glioma were screened through the FerrDb database, and we analyzed the combination between genes and glioma of survival and prognosis via TCGA, GEPIA, TIMER, and other databases. Survival curve and prognostic analysis showed that the overexpression of NFE2L2 and NOX4, respectively, has a remarkable link with a worse prognosis in glioma. Then, the association between the expression of the two genes and tumor-infiltrating immune cells level was explored based on the GSCA, and the immunity of NFE2L2 and NOX4 based on the TISIDB database was also investigated. In glioma, especially GBM, there is a strong association between gene expression and immune infiltration, even in macrophages, nTreg, and Th2 cells, which play immunosuppressive functions in TME. In conclusion, these results indicate that NFE2L2 and NOX4 could be risk prognosis biomarkers in glioma, and they bound up with immune infiltration and tumor immunity in tumorigenesis.

Comprehensive Analysis of the Prognostic Value and Molecular Function of CRNDE in Glioma at Bulk and Single-Cell Levels

Colorectal neoplasia differentially expressed (CRNDE) is an oncogenic long noncoding RNA (lncRNA) overexpressed in diverse malignancies. Here, we comprehensively analyze the prognostic value and molecular function of CRNDE in glioma. Bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), and single-cell RNA-sequencing data from the Tumor Immune Single-Cell Hub (TISCH) were analyzed. Kaplan-Meier survival analysis was applied to verify the prognostic value of CRNDE. Then, a nomogram based on multivariate Cox regression was established for individualized survival prediction. Subsequently, the expression characteristic and biological function of CRNDE were analyzed at the single-cell level. Lastly, the effects of CRNDE on the proliferation and invasion of glioma cell were explored in vitro. We discovered that CRNDE was a powerful marker for risk stratification of glioma patients. Regardless of the status of IDH and 1p/19q, CRNDE could effectively stratify patients' prognosis. The nomogram that incorporated the CRNDE expression was proved to be a reliable tool for survival prediction. In addition, epithelial-mesenchymal transition may be the most important biological process regulated by CRNDE, which was identified at both the bulk and single-cell levels. Moreover, CRNDE knockdown significantly inhibited the proliferation and invasion of glioma cell. Overall, CRNDE is a vital oncogene and may be a valuable supplement to improve the clinical stratification of glioma.

Methylation-based reclassification and risk stratification of skull-base chordomas

BACKGROUND

Skull-base chordomas are rare malignant bone cancers originating from the remnant of the notochord. Survival is variable, and clinical or molecular factors cannot reliably predict their outcomes. This study therefore identified epigenetic subtypes that defined new chordoma epigenetic profiles and their corresponding characteristics.

METHODS

Methylation profiles of 46 chordoma-resected neoplasms between 2008 and 2014, along with clinical information, were collected. K-means consensus clustering and principal component analysis were used to identify and validate the clusters. Single-sample gene set enrichment analysis, methylCIBERSORT algorithm, and copy number analysis were used to identify the characteristics of the clusters.

RESULTS

Unsupervised clustering analysis confirmed two clusters with a progression-free survival difference. Gene set enrichment analysis indicated that the early and late estrogen response pathways and the hypoxia pathway were activated whereas the inflammatory and interferon gamma responses were suppressed. Forty-six potential therapeutic targets corresponding to differentially methylated sites were identified from chordoma patients. Subgroups with a worse outcome were characterized by low immune cell infiltration, higher tumor purity, and higher stemness indices. Moreover, copy number amplifications mostly occurred in cluster 1 tumors and the high-risk group. Additionally, the presence of a CCNE1 deletion was exclusively found in the group of chordoma patients with better outcome, whereas RB1 and CDKN2A/2B deletions were mainly found in the group of chordoma patients with worse outcome.

CONCLUSIONS

Chordoma prognostic epigenetic subtypes were identified, and their corresponding characteristics were found to be variable.

Identification of Gender- and Subtype-Specific Gene Expression Associated with Patient Survival in Low-Grade and Anaplastic Glioma in Connection with Steroid Signaling

Simple Summary

Gliomas are primary brain tumors that are initially slow growing but progress to be more aggressive and, ultimately, fatal within a few years. They are more common in men than in women, suggesting a protective role for female hormones. By analyzing patient data collected in the public TGCA-LGG database, we have demonstrated a link between the expression level of key steroid biosynthesis enzymes or hormone receptors with patient survival, in ways that are dependent on gender and molecular subtype. We also determined the genes which expression associated with these actors of steroid signaling and the functions they perform, to decipher the mechanisms underlying gender-dependent differences. Together, these results establish, for the first time, the involvement of hormones in low-grade and anaplastic gliomas and provide clues for refining their classification and, thus, facilitating more personalized management of patients.

Abstract

Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test. Finally, a functional analysis of gender-specific correlated genes was performed. HOX-related genes appeared to be differentially expressed between males and females in both grades, suggesting that a glioma could originate in perturbation of developmental signals. Moreover, aromatase, androgen, and estrogen receptor expressions were associated with patient survival and were mainly related to angiogenesis or immune response. Therefore, consideration of the tight control of steroid hormone production and signaling seems crucial for the understanding of glioma pathogenesis and emergence of future targeted therapies.

Construction and validation of a glioblastoma prognostic model based on immune-related genes

Background

Glioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.

Purpose

Here, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.

Methods

Glioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).

Results

Six IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.

Conclusion

Herein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research.

FCGR3A Is a Prognostic Biomarker and Correlated with Immune Infiltrates in Lower-Grade Glioma

Low-grade gliomas (LGGs) are primary invasive brain tumors that grow slowly but are incurable and eventually develop into high malignant glioma. Fc fragment of IgG receptor IIIa (FCGR3A) gene polymorphism may correlate with some cancers' treatment responses. However, the expression and prognosis value of FCGR3A and correlation with tumor-immune infiltrate in LGG remain unclear. FCGR3A mRNA expression in gastric cancer (GC) was examined using TIMER and GEPIA databases. Correlations between FCGR3A expression and clinicopathological parameters were analyzed using ULACAN and CGGA databases. GEPIA, OncoLnc, and ULACAN databases were used to examine the clinical prognostic significance of FCGR3A in LGG. TIMER was used to analyze the correlations among FCGR3A and tumor-infiltrating immune cells. Signaling pathways related to FCGR3A expression were identified by LinkedOmics. We found that FCGR3A expression was higher in LGG than in normal tissue and was correlated with various clinical parameters. In addition, high FCGR3A expression predicted poor overall survival in LGG. More importantly, FCGR3A expression positively correlated with immune checkpoint molecules, including PD1, PD-L1, PD-L2, CTLA4, LAG-3 and TIM-3, and tumor-associated macrophage (TAM) gene markers in LGG. GO and KEGG pathway analyses indicated that TUBA1C may potentially regulate the pathogenesis of LGG through immune-related pathways. These findings indicated that FCGR3A plays a vital role in the infiltration of immune cells and could constitute a promising prognostic biomarker in LGG patients.

A Novel Classification Model for Lower-Grade Glioma Patients Based on Pyroptosis-Related Genes

Recent studies demonstrated that pyroptosis plays a crucial role in shaping the tumor-immune microenvironment. However, the influence of pyroptosis on lower-grade glioma regarding immunotherapy and targeted therapy is still unknown. This study analyzed the variations of 33 pyroptosis-related genes in lower-grade glioma and normal tissues. Our study found considerable genetic and expression alterations in heterogeneity among lower-grade gliomas and normal brain tissues. There are two pyroptosis phenotypes in lower-grade glioma, and they exhibited differences in cell infiltration characteristics and clinical characters. Then, a PyroScore model using the lasso-cox method was constructed to measure the level of pyroptosis in each patient. PyroScore can refine the lower-grade glioma patients with a stratified prognosis and a distinct tumor immune microenvironment. Pyscore may also be an effective factor in predicting potential therapeutic benefits. In silico analysis showed that patients with a lower PyroScore are expected to be more sensitive to targeted therapy and immunotherapy. These findings may enhance our understanding of pyroptosis in lower-grade glioma and might help optimize risk stratification for the survival and personalized management of lower-grade glioma patients.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

BACKGROUND

Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.

METHODS

Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.

FINDINGS

APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.

INTERPRETATION

APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).

FUNDING

National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

Identification of a Nomogram with an Autophagy-Related Risk Signature for Survival Prediction in Patients with Glioma

Background

Glioma is a common type of tumor in the central nervous system characterized by high morbidity and mortality. Autophagy plays vital roles in the development and progression of glioma, and is involved in both normal physiological and various pathophysiological progresses.

Patients and Methods

A total of 531 autophagy-related genes (ARGs) were obtained and 1738 glioma patients were collected from three public databases. We performed least absolute shrinkage and selection operator regression to identify the optimal prognosis-related genes and constructed an autophagy-related risk signature. The performance of the signature was validated by receiver operating characteristic analysis, survival analysis, clinic correlation analysis, and Cox regression. A nomogram model was established by using multivariate Cox regression analysis. Schoenfeld’s global and individual test were used to estimate time-varying covariance for the assumption of the Cox proportional hazard regression analysis. The R programming language was used as the main data analysis and visualizing tool.

Results

An overall survival-related risk signature consisting of 15 ARGs was constructed and significantly stratified glioma patients into high- and low-risk groups (P < 0.0001). The area under the ROC curve of 1-, 3-, 5-year survival was 0.890, 0.923, and 0.889, respectively. Univariate and multivariate Cox analyses indicated that the risk signature was a satisfactory independent prognostic factor. Moreover, a nomogram model integrating risk signature with clinical information for predicting survival rates of patients with glioma was constructed (C-index=0.861±0.024).

Conclusion

This study constructed a novel and reliable ARG-related risk signature, which was verified as a satisfactory prognostic marker. The nomogram model could provide a reference for individually predicting the prognosis for each patient with glioma and promoting the selection of optimal treatment.

The immunology of low-grade gliomas

Low-grade gliomas (LGGs), which harbor an isocitrate dehydrogenase (IDH) mutation, have a better prognosis than their high-grade counterparts; nonetheless, they remain incurable and impart significant negative impacts on patients' quality of life. Although immunotherapies represent a novel avenue of treatment for patients with LGGs, they have not yet been successful. Accurately selecting and evaluating immunotherapies requires a detailed understanding of LGG tumor immunology and the underlying tumor immune phenotype. A growing body of literature suggests that LGGs significantly differ in their immunology from high-grade gliomas, highlighting the importance of investigation into LGG immunology specifically. In this review, the authors aimed to discuss relevant research surrounding the LGG tumor immune microenvironment, including immune cell infiltration, tumor immunogenicity, checkpoint molecule expression, the impact of an IDH mutation, and implications for immunotherapies, while also briefly touching on current immunotherapy trials and future directions for LGG immunology research.

SCAMP4 is a novel prognostic marker and correlated with the tumor progression and immune infiltration in glioma

BACKGROUND

Glioma is the most prevalent brain tumor with high mortality and morbidity and the prognosis of patients remains very poor. Glioma therapy is largely limited by the extraordinary invasive capability in glioma and the lack of valuable biomarkers of LGG and GBM. So it is urgent and important for us to identify valuable biomarkers to treat glioma patients. SCAMP4 (Secretory Carrier-Associated Membrane Protein 4) has not been reported to be linked to cancer prognostic or any treatments.

METHODS

We analyzed the role of SCAMP4 in LGG and GBM via the publicly available CGGA (The Chinese Glioma Atlas) and TCGA (The Cancer Genome Atlas) databases. The correlations between SCAMP4 and the immune cells were analyzed by applying CIBERSORT and TIMER, while R was utilized in the analysis of the statistical data.

RESULTS

Our results indicated that SCAMP4 which is correlated to age, stage, grade and tumor status and may be a promising independent prognostic factor in LGG and GBM. Meanwhile, the expression of SCAMP4 is closely associated with some tumor-infiltrating immune cells such as Monocytes, NK cells activated, Macrophages, Mast cells resting and so on. Furthermore, during the in-depth analysis of the integrated correlations, we also find that isocitrate dehydrogenase 1 (IDH1) and SCAMP4 shared similar prognostic values.

CONCLUSIONS

Together with all these findings, the identification of SCAMP4 as a new biomarker could elucidate how the immune microenvironment influence the glioma development. With further analysis, SCAMP4 may be a predictor for glioma prognosis.

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

Background

Glioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.

Methods

We integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.

Results

Our study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.

Conclusion

Our work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

Prognostic value of the ratio of carcinoembryonic antigen concentration to maximum tumor diameter in patients with stage II colorectal cancer

Background

Recently, a study from our center indicated that the ratio of preoperative carcinoembryonic antigen (CEA) concentration to maximum tumor diameter (DMAX) may be a prognostic marker for patients with rectal cancer. Therefore, the study aimed to evaluate whether this ratio (CEA/DMAX) has prognostic value for patients with stage II colorectal cancer (CRC).

Methods

A prospectively maintained database was searched for patients with pathologically confirmed stage II CRC who underwent surgery between January 2010 and March 2019. Patients were stratified according to the mean CEA/DMAX value into low and high CEA/DMAX groups. Kaplan-Meier, univariable, and multivariable Cox regression analyses were used to evaluate whether the CEA/DMAX could predict overall survival (OS) and disease-free survival (DFS). Nomograms were constructed in terms of the results of multivariable Cox regression analyses.

Results

The study included 2,499 patients with stage II CRC. The mean CEA/DMAX value was 2.33 (ng/mL per cm). Kaplan-Meier analyses revealed that, relative to the low CEA/DMAX group, the high CEA/DMAX group had significantly poorer OS (67.31% vs. 85.02%, P<0.001) and DFS (61.41% vs. 77.10%, P<0.001). The multivariable Cox regression analysis revealed that CEA/DMAX independently predicted OS (hazard ratio: 2.58, 95% confidence interval: 1.51-4.38, P<0.001) and DFS (hazard ratio: 1.97, 95% confidence interval: 1.38-2.83, P<0.001). Two simple-to-use nomograms comprising CEA/DMAX, age, T stage, and lymphovascular invasion were developed to predict 1-, 3-, and 5-year rates of OS and DFS among patients with stage II CRC. The nomograms had good performance based on the concordance index, receiver operating characteristic (ROC) curve analysis, and calibration curves. Subgroup analyses further confirmed that a high CEA/DMAX was associated with poor OS and DFS among patients with stage II colon cancer and among patients with stage II rectal cancer (both P<0.05).

Conclusions

Among patients with stage II CRC, a high CEA/DMAX independently predicted poor OS and DFS, and the predictive abilities were also observed in subgroup analyses of patients with stage II colon cancer or rectal cancer. Furthermore, we developed two nomograms that had good accuracy for predicting the prognosis of stage II CRC.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Prognostic and Predictive Value of Immune-Related Gene Pair Signature in Primary Lower-Grade Glioma Patients

Immune-related gene pairs (IRGPs) have been associated with prognosis in various cancer types, but few studies have examined their prognostic capabilities in glioma patients. Here, we gathered the gene expression and clinical profile data of primary lower-grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA, containing CGGAseq1 and CGGAseq2), the Gene Expression Omnibus (GEO: GSE16011), and Rembrandt datasets. In the TCGA dataset, univariate Cox regression was performed to detect overall survival (OS)-related IRGs, Lasso regression, and multivariate Cox regression were used to screen robust prognosis-related IRGs, and 19 IRGs were selected for the construction of an IRGP prognostic signature. All patients were allotted to high- and low-risk subgroups based on the TCGA dataset median value risk score. Validation analysis indicated that the IRGP signature returned a stable prognostic value among all datasets. Univariate and multivariate Cox regression analyses indicated that the IRG -signature could efficiently predict the prognosis of primary LGG patients. The IRGP-signature-based nomogram model was built, revealing the reliable ability of the IRGP signature to predict clinical prognosis. The single-sample gene set enrichment analysis (ssGSEA) suggested that high-risk samples contained higher numbers of immune cells but featured lower tumor purity than low-risk samples. Finally, we verified the prognostic ability of the IRGP signature using experiments performed in LGG cells. These results indicated that the IRGP signature could be regarded as a stable prognostic assessment predictor for identifying high-risk primary LGG patients.