Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

A comprehensive survey on the use of deep learning techniques in glioblastoma

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Nobiletin Inhibits Breast Cancer Stem Cell by Regulating the Cell Cycle: A Comprehensive Bioinformatics Analysis and In Vitro Experiments

Inhibiting breast cancer stem cell (BCSC) signaling pathways is a strategic method for successfully treating breast cancer. Nobiletin (NOB) is a compound widely found in orange peel that exhibits a toxic effect on various types of cancer cells, and inhibits the signaling pathways that regulate the properties of BCSCs; however, the effects of NOB on BCSCs remain elusive. The purpose of this study was to determine the target genes of NOB for inhibiting BCSCs using in vitro three-dimensional breast cancer cell culture (mammospheres) and in silico approaches. We combined in vitro experiments to develop mammospheres and conducted cytotoxicity, next-generation sequencing, and bioinformatics analyses, such as gene ontology, the Reactome pathway enrichment, network topology, gene set enrichment analysis, hub genes selection, genetic alterations, prognostic value related to the mRNA expression, and mRNA and protein expression of potential NOB target genes that inhibit BCSCs. Here, we show that NOB inhibited BCSCs in mammospheres from MCF-7 cells. We also identified CDC6, CHEK1, BRCA1, UCHL5, TOP2A, MTMR4, and EXO1 as potential NOB targets inhibiting BCSCs. NOB decreased G0/G1, but increased the G2/M cell population. These findings showed that NOB is a potential therapeutic candidate for BCSCs treatment by regulating cell cycle.

Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

Cuprotosis clusters predict prognosis and immunotherapy response in low-grade glioma

Cuprotosis, an emerging mode of cell death, has recently caught the attention of researchers worldwide. However, its impact on low-grade glioma (LGG) patients has not been fully explored. To gain a deeper insight into the relationship between cuprotosis and LGG patients' prognosis, we conducted this study in which LGG patients were divided into two clusters based on the expression of 18 cuprotosis-related genes. We found that LGG patients in cluster A had better prognosis than those in cluster B. The two clusters also differed in terms of immune cell infiltration and biological functions. Moreover, we identified differentially expressed genes (DEGs) between the two clusters and developed a cuprotosis-related prognostic signature through the least absolute shrinkage and selection operator (LASSO) analysis in the TCGA training cohort. This signature divided LGG patients into high- and low-risk groups, with the high-risk group having significantly shorter overall survival (OS) time than the low-risk group. Its predictive reliability for prognosis in LGG patients was confirmed by the TCGA internal validation cohort, CGGA325 cohort and CGGA693 cohort. Additionally, a nomogram was used to predict the 1-, 3-, and 5-year OS rates of each patient. The analysis of immune checkpoints and tumor mutation burden (TMB) has revealed that individuals belonging to high-risk groups have a greater chance of benefiting from immunotherapy. Functional experiments confirmed that interfering with the signature gene TNFRSF11B inhibited LGG cell proliferation and migration. Overall, this study shed light on the importance of cuprotosis in LGG patient prognosis. The cuprotosis-related prognostic signature is a reliable predictor for patient outcomes and immunotherapeutic response and can help to develop new therapies for LGG.

LGR5, a prognostic stem cell target, promotes endometrial cancer proliferation through autophagy activation

Endometrial cancer (EC) is a common malignant tumor in women worldwide. Although early EC has a good prognosis, advanced endometrial cancer is still associated with the risk of drug resistance and recurrence. Cancer stem cells (CSCs), a category closely related to drug resistance and recurrence, are rarely studied at present. Here, we constructed a risk model containing ten stemness-related prognostic genes. Compared with patients in the low-risk group, patients in the high-risk group had a shorter overall survival time. The accuracy of this model was verified by ROC in the TCGA (AUC = 0.779) and Peking University People's Hospital (PKUPH, AUC = 0.864) cohorts. The risk score and stage were independent risk factors in the multivariate regression analysis, which was subsequently used to construct the nomogram and verified in the TCGA cohort. LGR5 was significantly correlated with overall survival and involvement in the Wnt signaling pathway. In addition, LGR5 was highly expressed in EC tissues and was related to age, stage, histological type, and menopause status in the TCGA database. Overexpression of LGR5 accelerated the proliferation rate of EC cells, which may be related to autophagy activation. Taken together, our study established a prognostic model based on transcription sequencing data from the TCGA database and verified it in the PKUPH cohort, which has prospective clinical implications for the prognostic evaluation of EC. We systematically studied the code gene LGR5 in EC, which may help clinicians make personalized prognostic assessments and effective clinical decisions for EC.

Integrative analysis developing and validating potential candidate biomarkers for cancer stemness features of pan-renal cell carcinoma

In our study, 49 key genes significantly associated with renal cell carcinoma (RCC) stemness were obtained. Next, we developed a molecular prognostic signature associated with stemness features of pan-RCC. The difference in OS between high-risk and low-risk group was statistically significant (P < 0.05). The area under ROC curve for 1-years OS, 5-years OS and 10-years OS was 0.759, 0.712 and 0.918, respectively. The results of validation in TCGA cohort and ICGC cohort revealed the predictive capability of this signature. Further, we selected three genes and further validation showed that these three hub genes were potential hub biomarkers for pan-RCC stemness features.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining

BACKGROUND

Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications.

METHODS

We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes.

RESULTS

We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC_1-year = 0.714, ROC_3-year = 0.750, ROC_5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response.

CONCLUSION

The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

Roles of follistatin-like protein 3 in human non-tumor pathophysiologies and cancers

Follistatin-like protein 3 (FSTL3) is a type of FSTLs. By interacting with a disintegrin and metalloproteinase 12 (ADAM12), transforming growth factor-β ligands (activin, myostatin and growth differentiation factor (GDF) 11), FSTL3 can either activate or inhibit these molecules in human non-tumor pathophysiologies and cancers. The FSTL3 gene was initially discovered in patients with in B-cell chronic lymphocytic leukemia, and subsequent studies have shown that the FSTL3 protein is associated with reproductive development, insulin resistance, and hematopoiesis. FSTL3 reportedly contributes to the development and progression of many cancers by promoting tumor metastasis, facilitating angiogenesis, and inducing stem cell differentiation. This review summarizes the current pathophysiological roles of FSTL3, which may be a putative prognostic biomarker for various diseases and serve as a potential therapeutic target.

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Background

Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient's prognostic survival.

Methods

From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn.

Results

Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups.

Conclusion

Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients' prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.

Prognostic Modeling of Lung Adenocarcinoma Based on Hypoxia and Ferroptosis-Related Genes

Background. It is well known that hypoxia and ferroptosis are intimately connected with tumor development. The purpose of this investigation was to identify whether they have a prognostic signature. To this end, genes related to hypoxia and ferroptosis scores were investigated using bioinformatics analysis to stratify the risk of lung adenocarcinoma. Methods. Hypoxia and ferroptosis scores were estimated using The Cancer Genome Atlas (TCGA) database-derived cohort transcriptome profiles via the single sample gene set enrichment analysis (ssGSEA) algorithm. The candidate genes associated with hypoxia and ferroptosis scores were identified using weighted correlation network analysis (WGCNA) and differential expression analysis. The prognostic genes in this study were discovered using the Cox regression (CR) model in conjunction with the LASSO method, which was then utilized to create a prognostic signature. The efficacy, accuracy, and clinical value of the prognostic model were evaluated using an independent validation cohort, Receiver Operator Characteristic (ROC) curve, and nomogram. The analysis of function and immune cell infiltration was also carried out. Results. Here, we appraised 152 candidate genes expressed not the same, which were related to hypoxia and ferroptosis for prognostic modeling in The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort, and these genes were further validated in the GSE31210 cohort. We found that the 14-gene-based prognostic model, utilizing MAPK4, TNS4, WFDC2, FSTL3, ITGA2, KLK11, PHLDB2, VGLL3, SNX30, KCNQ3, SMAD9, ANGPTL4, LAMA3, and STK32A, performed well in predicting the prognosis in lung adenocarcinoma. ROC and nomogram analyses showed that risk scores based on prognostic signatures provided desirable predictive accuracy and clinical utility. Moreover, gene set variance analysis showed differential enrichment of 33 hallmark gene sets between different risk groups. Additionally, our results indicated that a higher risk score will lead to more fibroblasts and activated CD4 T  cells but fewer myeloid dendritic cells, endothelial cells, eosinophils, immature dendritic cells, and neutrophils. Conclusion. Our research found a 14-gene signature and established a nomogram that accurately predicted the prognosis in patients with lung adenocarcinoma. Clinical decision-making and therapeutic customization may benefit from these results, which may serve as a valuable reference in the future.

Identification and validation of an anoikis-associated gene signature to predict clinical character, stemness, IDH mutation, and immune filtration in glioblastoma

Background

Glioblastoma (GBM) is the most prominent and aggressive primary brain tumor in adults. Anoikis is a specific form of programmed cell death that plays a key role in tumor invasion and metastasis. The presence of anti-anoikis factors is associated with tumor aggressiveness and drug resistance.

Methods

The non-negative matrix factorization algorithm was used for effective dimension reduction for integrated datasets. Differences in the tumor microenvironment (TME), stemness indices, and clinical characteristics between the two clusters were analyzed. Difference analysis, weighted gene coexpression network analysis (WGCNA), univariate Cox regression, and least absolute shrinkage and selection operator regression were leveraged to screen prognosis-related genes and construct a risk score model. Immunohistochemistry was performed to evaluate the expression of representative genes in clinical specimens. The relationship between the risk score and the TME, stemness, clinical traits, and immunotherapy response was assessed in GBM and pancancer.

Results

Two definite clusters were identified on the basis of anoikis-related gene expression. Patients with GBM assigned to C1 were characterized by shortened overall survival, higher suppressive immune infiltration levels, and lower stemness indices. We further constructed a risk scoring model to quantify the regulatory patterns of anoikis-related genes. The higher risk score group was characterized by a poor prognosis, the infiltration of suppressive immune cells and a differentiated phenotype, whereas the lower risk score group exhibited the opposite effects. In addition, patients in the lower risk score group exhibited a higher frequency of isocitrate dehydrogenase (IDH) mutations and a more sensitive response to immunotherapy. Drug sensitivity analysis was performed, revealing that the higher risk group may benefit more from drugs targeting the PI3K/mTOR signaling pathway.

Conclusion

We revealed potential relationships between anoikis-related genes and clinical features, TME, stemness, IDH mutation, and immunotherapy and elucidated their therapeutic value.

Chitinase 3-Like 2

OBJECTIVES

We aimed to evaluate the expression pattern of chitinase 3-like 2 (CHI3L2) in the tumor core and peritumoral brain zone (PBZ) of newly diagnosed glioblastoma (GBM) in recurrent tumors and its association with patient prognosis.

METHODS

The study was conducted on three sample sets derived from different patient cohorts. Messenger RNA (mRNA) expression of CHI3L2 in the tumor core and PBZ (n = 34) compared with control (n = 20) tissues was studied by quantitative polymerase chain reaction in sample set 1. Sample set 2 included 19 paired, primary-recurrent GBM tissues. Sample set 3 comprised 82 GBM tissues of patients with treatment and follow-up information. Immunohistochemistry (IHC) was performed on all three sample sets.

RESULTS

mRNA expression of CHI3L2 was significantly higher in the tumor core and PBZ compared with control (P < .0001). By IHC, CHI3L2 showed strong cytoplasmic staining in tumor cells. Recurrent tumors had a higher expression of CHI3L2 compared with primary tumors (P = .007). Survival analysis showed CHI3L2 expression was associated with shorter overall survival (P = .034) and progression-free survival (P = .010), which was in line with The Cancer Genome Atlas cohort (P = .043).

CONCLUSIONS

High expression of CHI3L2 in the tumor core and PBZ, as well as its association with tumor recurrence and poor patient prognosis, suggests it might be contributing to tumor spread and recurrence.

Ascendancy of unfolded protein response over glioblastoma: estimating progression, prognosis and survival

Glioblastoma (GBM) is presented with a poor prognosis. The endoplasmic reticulum stress (ERS) has been implicated as a major contributor to disease progression and chemoresistance in GBM. Triggering ERS by chemical agents or genetic modulations is identified as some of the reasons for regulating gene expression and the pathogenesis of GBM. ERS initiates unfolded protein response (UPR), an integrated system useful in restoring homeostasis or inducing apoptosis. Modulation of UPR might have positive outcomes in GBM treatment as UPR inducers have been shown to alter cell survival and migration. In the current review, we have utilized GSE7806, a publicly available dataset from Gene Expression Omnibus (GEO), to evaluate the genes expressed during 6.5 hr and 18 hr, which can be comparable to the early and late-onset of the disease. Subsequently, we have elucidated the prognosis and survival information whilst the expression of these genes in the GBM was noted in previous studies. This is the first of its kind review summarizing the most recent gene information correlating UPR and GBM.

Identification of EMT-Related Genes and Prognostic Signature With Significant Implications on Biological Properties and Oncology Treatment of Lower Grade Gliomas

The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.

Multiomics Data Analysis and Identification of Immune-Related Prognostic Signatures With Potential Implications in Prognosis and Immune Checkpoint Blockade Therapy of Glioblastoma

Background

In recent years, glioblastoma multiforme (GBM) has been a concern of many researchers, as it is one of the main drivers of cancer-related deaths worldwide. GBM in general usually does not responding well to immunotherapy due to its unique microenvironment.

Methods

To uncover any further informative immune-related prognostic signatures, we explored the immune-related distinction in the genetic or epigenetic features of the three types (expression profile, somatic mutation, and DNA methylation). Twenty eight immune-related hub genes were identified by Weighted Gene Co-Expression Network Analysis (WGCNA). The findings showed that three genes (IL1R1, TNFSF12, and VDR) were identified to construct an immune-related prognostic model (IRPM) by lasso regression. Then, we used three hub genes to construct an IRPM for GBM and clarify the immunity, mutation, and methylation characteristics.

Results

Survival analysis of patients undergoing anti-program cell death protein 1 (anti-PD-1) therapy showed that overall survival was superior in the low-risk group than in the high-risk group. The high-risk group had an association with epithelial-mesenchymal transition (EMT), high immune cell infiltration, immune activation, a low mutation number, and high methylation, while the low-risk group was adverse status.

Conclusions

In conclusion, IRPM is a promising tool to distinguish the prognosis of patients and molecular and immune characteristics in GBM, and the IRPM risk score can be used to predict patient sensitivity to checkpoint inhibitor blockade therapy. Thus, three immune-related signatures will guide us in improving treatment strategies and developing objective diagnostic tools.

Identification of Implications of Angiogenesis and m6A Modification on Immunosuppression and Therapeutic Sensitivity in Low-Grade Glioma by Network Computational Analysis of Subtypes and Signatures

Angiogenesis is a complex process in the immunosuppressed low-grade gliomas (LGG) microenvironment and is regulated by multiple factors. N⁶-methyladenosine (m6A), modified by the m6A modification regulators (“writers” “readers” and “erasers”), can drive LGG formation. In the hypoxic environment of intracranial tumor immune microenvironment (TIME), m6A modifications in glioma stem cells are predominantly distributed around neovascularization and synergize with complex perivascular pathological ecology to mediate the immunosuppressive phenotype of TIME. The exact mechanism of this phenomenon remains unknown. Herein, we elucidated the relevance of the angiogenesis-related genes (ARGs) and m6A regulators (MAGs) and their influencing mechanism from a macro perspective. Based on the expression pattern of MAGs, we divided patients with LGG into two robust categories via consensus clustering, and further annotated the malignant related mechanisms and corresponding targeted agents. The two subgroups (CL1, CL2) demonstrated a significant correlation with prognosis and clinical-pathology features. Moreover, WGCNA has also uncovered the hub genes and related mechanisms of MAGs affecting clinical characters. Clustering analysis revealed a synergistic promoting effect of M6A and angiogenesis on immunosuppression. Based on the expression patterns of MAGs, we established a high-performance gene-signature (MASig). MASig revealed somatic mutational mechanisms by which MAGs affect the sensitivity to treatment in LGG patients. In conclusion, the MAGs were critical participants in the malignant process of LGG, with a vital potential in the prognosis stratification, prediction of outcome, and therapeutic sensitivity of LGG. Findings based on these strategies may facilitate the development of objective diagnosis and treatment systems to quantify patient survival and other outcomes, and in some cases, to identify potential unexplored targeted therapies.

Extended Application of Genomic Selection to Screen Multi-Omics Data for the Development of Novel Pyroptosis-Immune Signatures and Predicting Immunotherapy of Glioma

Glioma is one of the most human malignant diseases and the leading cause of cancer-related deaths worldwide. Nevertheless, the present stratification systems do not accurately predict the prognosis and treatment benefit of glioma patients. Currently, no comprehensive analyses of multi-omics data have been performed to better understand the complex link between pyroptosis and immune. In this study, we constructed four pyroptosis immune subgroups by pyroptosis regulators and obtained nine pyroptosis immune signatures by analyzing the differentially expressed genes between the four pyroptosis immune subgroups. Nine novel pyroptosis immune signatures were provided for assessing the complex heterogeneity of glioma by the analyses of multi-omics data. The pyroptosis immune prognostic model (PIPM) was constructed by pyroptosis immune signatures, and the PIPM risk score was established for glioma cohorts with a total of 1716 samples. Then, analyses of the tumor microenvironment revealed an unanticipated correlation of the PIPM risk score with stemness, immune checkpoint expression, infiltrating the immune system, and therapy response in glioma. The low PIPM risk score patients had a better response to immunotherapy and showed sensitivity to radio-chemotherapy. The results of the pan-cancer analyses revealed the significant correlation between the PIPM risk score and clinical outcome, immune infiltration, and stemness. Taken together, we conclude that pyroptosis immune signatures may be a helpful tool for overall survival prediction and treatment guidance for glioma and other tumors patients.

Transcriptomic Profiling of DNA Damage Response in Patient-Derived Glioblastoma Cells before and after Radiation and Temozolomide Treatment

Glioblastoma is a highly aggressive, invasive and treatment-resistant tumour. The DNA damage response (DDR) provides tumour cells with enhanced ability to activate cell cycle arrest and repair treatment-induced DNA damage. We studied the expression of DDR, its relationship with standard treatment response and patient survival, and its activation after treatment. The transcriptomic profile of DDR pathways was characterised within a cohort of isocitrate dehydrogenase (IDH) wild-type glioblastoma from The Cancer Genome Atlas (TCGA) and 12 patient-derived glioblastoma cell lines. The relationship between DDR expression and patient survival and cell line response to temozolomide (TMZ) or radiation therapy (RT) was assessed. Finally, the expression of 84 DDR genes was examined in glioblastoma cells treated with TMZ and/or RT. Although distinct DDR cluster groups were apparent in the TCGA cohort and cell lines, no significant differences in OS and treatment response were observed. At the gene level, the high expression of ATP23, RAD51C and RPA3 independently associated with poor prognosis in glioblastoma patients. Finally, we observed a substantial upregulation of DDR genes after treatment with TMZ and/or RT, particularly in RT-treated glioblastoma cells, peaking within 24 h after treatment. Our results confirm the potential influence of DDR genes in patient outcome. The observation of DDR genes in response to TMZ and RT gives insight into the global response of DDR pathways after adjuvant treatment in glioblastoma, which may have utility in determining DDR targets for inhibition.

Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme

Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.

Transitioning pre-clinical glioblastoma models to clinical settings with biomarkers identified in 3D cell-based models: A systematic scoping review

Glioblastoma (GBM) remains incurable despite the overwhelming discovery of 2-dimensional (2D) cell-based potential therapeutics since the majority of them have met unsatisfactory results in animal and clinical settings. Incremental empirical evidence has laid the widespread need of transitioning 2D to 3-dimensional (3D) cultures that better mimic GBM's complex and heterogenic nature to allow better translation of pre-clinical results. This systematic scoping review analyses the transcriptomic data involving 3D models of GBM against 2D models from 22 studies identified from four databases (PubMed, ScienceDirect, Medline, and Embase). From a total of 499 genes reported in these studies, 313 (63%) genes were upregulated across 3D models cultured using different scaffolds. Our analysis showed that 4 of the replicable upregulated genes are associated with GBM stemness, epithelial to mesenchymal transition (EMT), hypoxia, and migration-related genes regardless of the type of scaffolds, displaying close resemblances to primitive undifferentiated tumour phenotypes that are associated with decreased overall survival and increased hazard ratio in GBM patients. The upregulation of drug response and drug efflux genes (e.g. cytochrome P450s and ABC transporters) mirrors the GBM genetic landscape that contributes to in vivo and clinical treatment resistance. These upregulated genes displayed strong protein-protein interactions when analysed using an online bioinformatics software (STRING). These findings reinforce the need for widespread transition to 3D GBM models as a relatively inexpensive humanised pre-clinical tool with suitable genetic biomarkers to bridge clinical gaps in potential therapeutic evaluations.

Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes

Cancer stem cells (CSCs) actively reprogram their tumor microenvironment (TME) to sustain a supportive niche, which may have a dramatic impact on prognosis and immunotherapy. However, our knowledge of the landscape of the gastric cancer stem-like cell (GCSC) microenvironment needs to be further improved. A multi-step process of machine learning approaches was performed to develop and validate the prognostic and predictive potential of the GCSC-related score (GCScore). The high GCScore subgroup was not only associated with stem cell characteristics, but also with a potential immune escape mechanism. Furthermore, we experimentally demonstrated the upregulated infiltration of CD206+ tumor-associated macrophages (TAMs) in the invasive margin region, which in turn maintained the stem cell properties of tumor cells. Finally, we proposed that the GCScore showed a robust capacity for prediction for immunotherapy, and investigated potential therapeutic targets and compounds for patients with a high GCScore. The results indicate that the proposed GCScore can be a promising predictor of prognosis and responses to immunotherapy, which provides new strategies for the precision treatment of GCSCs.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.

Follistatin-Like 3 Correlates With Lymph Node Metastasis and Serves as a Biomarker of Extracellular Matrix Remodeling in Colorectal Cancer

Background

As a heterogeneous disease, colorectal cancer (CRC) presents a great challenge to individualized treatment due to its lymph node metastasis (LNM). Existing studies have shown that immune and stromal components in extracellular matrix (ECM) act as important part in tumorigenicity and progression, while their roles in LNM have not been fully elucidated. Here, crucial ECM-related genes responsible for LNM in CRC were selected by multi-omics analysis.

Methods

Firstly, we characterized the immune infiltration landscape of CRC samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases by using ssGSEA algorithm. The CRC patients were divided into several immune subgroups by hierarchical clustering analyses. Then, differential genes were identified among immune subgroups and CRC vs. normal tissues in TCGA and GEO GSE39582 cohorts, respectively. Next, weighted correlation network analysis (WGCNA) was employed to construct a co-expression network to find LNM-related modules and hub genes. Subsequently, we evaluated the clinical value of hub gene in prognostic prediction and chemotherapy/immunotherapy. Besides, the protein level of key gene was verified in an external cohort from our center. Finally, we explored the underlying mechanism of FSTL3-mediated LNM by Gene function annotation and correlation analysis.

Results

Two immune subgroups, namely Immunity_High and Immunity_Low, were defined among the two CRC cohorts using ssGSEA algorithm, respectively. Based on the two immune subgroups, 2,635 overlapping differentially expressed genes were obtained from two cohorts, which were sequentially subjected to WGCNA and univariate Cox regression analysis. Ultimately, FSTL3 was selected as the key gene. Here, we first confirmed that overexpression of FSTL3 correlated with LNM and worse prognosis in CRC and was verified at the protein level in the external validation cohort. Moreover, FSTL3 expression showed strongly positive correlation with immune and stromal components in ECM. We furthermore found that FSTL3 may accelerate LNM through the formation of inhibitory immune microenvironment via promoting macrophage and fibroblast polarization and T cell exhaustion. Interestingly, high FSTL3 expression is linked to chemoresistance, but immunotherapy-sensitive.

Conclusion

FSTL3 is identified as a biomarker for ECM remodeling and worse clinical outcomes for the first time in CRC and is also a potential immunotherapeutic target to block LNM for CRC.

The Importance of Tumor Stem Cells in Glioblastoma Resistance to Therapy

Glioblastoma (GBM) is known to be the most common and lethal primary malignant brain tumor. Therapies against this neoplasia have a high percentage of failure, associated with the survival of self-renewing glioblastoma stem cells (GSCs), which repopulate treated tumors. In addition, despite new radical surgery protocols and the introduction of new anticancer drugs, protocols for treatment, and technical advances in radiotherapy, no significant improvement in the survival rate for GBMs has been realized. Thus, novel antitarget therapies could be used in conjunction with standard radiochemotherapy approaches. Targeted therapy, indeed, may address specific targets that play an essential role in the proliferation, survival, and invasiveness of GBM cells, including numerous molecules involved in signal transduction pathways. Significant cellular heterogeneity and the hierarchy with GSCs showing a therapy-resistant phenotype could explain tumor recurrence and local invasiveness and, therefore, may be a target for new therapies. Therefore, the forced differentiation of GSCs may be a promising new approach in GBM treatment. This article provides an updated review of the current standard and experimental therapies for GBM, as well as an overview of the molecular characteristics of GSCs, the mechanisms that activate resistance to current treatments, and a new antitumor strategy for treating GSCs for use as therapy.

m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma

m6A RNA methylation is an emerging epigenetic modification, and its potential role in immunity and stemness remains unknown. Based on 17 widely recognized m6A regulators, the m6A modification patterns and corresponding characteristics of immune infiltration and stemness of 1152 low-grade glioma samples were comprehensively analyzed. Machine-learning strategies for constructing m6AScores were trained to quantify the m6A modification patterns of individual samples. Here, we reveal a significant correlation between the multi-omics data of regulators and clinicopathological parameters. We identified two distinct m6A modification patterns (an immune-activated differentiation pattern and an immune-desert dedifferentiation pattern) and four regulatory patterns of m6A methylation on immunity and stemness. We show that the m6AScores can predict the molecular subtype of low-grade glioma, the abundance of immune infiltration, the enrichment of signaling pathways, gene variation and prognosis. The concentration of high immunogenicity and clinical benefits in the low-m6AScore group confirmed the sensitive response to radio-chemotherapy and immunotherapy in patients with high-m6AScore. The results of the pan-cancer analyses illustrate the significant correlation between m6AScore and clinical outcome, the burden of neoepitope, immune infiltration and stemness. The assessment of individual tumor m6A modification patterns will guide us in improving treatment strategies and developing objective diagnostic tools.

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma

Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA-sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the least absolute shrinkage and selection operator Cox regression analysis. The correlation network between survival-associated AS events and splicing factors was plotted. Prognostic models were built for every AS event type and performed well for risk stratification in patients with GBM. The final prognostic signature served as an independent prognostic factor [hazard ratio (HR), 4.61; 95% confidence interval (CI), 2.97-7.16; P=9.66×10-12] for several clinical parameters, including age, sex, isocitrate dehydrogenase mutation, O6-methylguanine-DNA methyltransferase promoter methylation and risk score. The HR for risk score with GBM was 1.0063 (95% CI, 1.0024-1.0103). The splicing regulatory network indicated that heat shock protein b-1, protein arginine N-methyltransferase 5, protein FAM50B and endoplasmic reticulum chaperone BiP genes were independent prognostic factors for GBM. The results of the present study support the ongoing effort in developing novel genomic models and providing potentially more effective treatment options for patients with GBM.