ITGA5 Predicts Dual-Drug Resistance to Temozolomide and Bevacizumab in Glioma

A new perspective on antiangiogenic antibody drug resistance: Biomarkers, mechanisms, and strategies in malignancies

Drug resistance of malignant tumor leads to disease progression be the bottleneck in clinical treatment. Antiangiogenic therapy, which aims to "starve" the tumor by inhibiting angiogenesis, is one of the key strategies in clinical oncology treatments. Recently, dozens of investigational antibody drugs and biosimilars targeting angiogenesis have obtained regulatory approval for the treatment of various malignancies. Moreover, a new generation of bispecific antibodies based on the principle of antiangiogenesis are being advanced for clinical trial to overcome antiangiogenic resistance in tumor treatment or enhance the efficacy of monotherapy. Tumors often develop resistance to antiangiogenesis therapy, presenting as refractory and sometimes even resistant to new therapies, for which there are currently no effective management strategies. Thus, a detailed understanding of the mechanisms mediating resistance to antiangiogenesis antibodies is crucial for improving drug effectiveness and achieving a durable response to antiangiogenic therapy. In this review, we provide a novel perspective on the tumor microenvironment, including antibody structure, tumor stroma, and changes within tumor cells, to analyze the multifactorial reasons underlying resistance to antiangiogenesis antibodies. The review also enumerates biomarkers that indicate resistance and potential strategies for monitoring resistance. Furthermore, based on recent clinical and preclinical studies, we summarize potential strategies and translational clinical trials aimed at overcoming resistance to antiangiogenesis antibodies. This review provides a valuable reference for researchers and clinical practitioners involved in the development of new drugs or therapeutic strategies to overcome antiangiogenesis antibodies resistance.

Upregulation of HAS2 promotes glioma cell proliferation and chemoresistance via c-myc

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant human brain tumor. Although comprehensive therapies, including chemotherapy and radiotherapy following surgery, have shown promise in prolonging survival, the prognosis for GBM patients remains poor, with an overall survival rate of only 14.6 months. Chemoresistance is a major obstacle to successful treatment and contributes to relapse and poor survival rates in glioma patients. Therefore, there is an urgent need for novel strategies to overcome chemoresistance and improve treatment outcomes for human glioma patients. Recent studies have shown that the tumor microenvironment plays a key role in chemoresistance. Our study demonstrates that upregulation of HAS2 and subsequent hyaluronan secretion promotes glioma cell proliferation, invasion, and chemoresistance in vitro and in vivo through the c-myc pathway. Targeting HAS2 sensitizes glioma cells to chemotherapeutic agents. Additionally, we found that hypoxia-inducible factor HIF1α regulates HAS2 expression. Together, our findings provide insights into the dysregulation of HAS2 and its role in chemoresistance and suggest potential therapeutic strategies for GBM.

Identification and validation of COL6A1 as a novel target for tumor electric field therapy in glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive primary brain malignancy. Novel therapeutic modalities like tumor electric field therapy (TEFT) have shown promise, but underlying mechanisms remain unclear. The extracellular matrix (ECM) is implicated in GBM progression, warranting investigation into TEFT-ECM interplay.

METHODS

T98G cells were treated with TEFT (200 kHz, 2.2 V/m) for 72 h. Collagen type VI alpha 1 (COL6A1) was identified as hub gene via comprehensive bioinformatic analysis based on RNA sequencing (RNA-seq) and public glioma datasets. TEFT intervention models were established using T98G and Ln229 cell lines. Pre-TEFT and post-TEFT GBM tissues were collected for further validation. Focal adhesion pathway activity was assessed by western blot. Functional partners of COL6A1 were identified and validated by co-localization and survival analysis.

RESULTS

TEFT altered ECM-related gene expression in T98G cells, including the hub gene COL6A1. COL6A1 was upregulated in GBM and associated with poor prognosis. Muti-database GBM single-cell analysis revealed high-COL6A1 expression predominantly in malignant cell subpopulations. Differential expression and functional enrichment analyses suggested COL6A1 might be involved in ECM organization and focal adhesion. Western blot (WB), immunofluorescence (IF), and co-immunoprecipitation (Co-IP) experiments revealed that TEFT significantly inhibited expression of COL6A1, hindering its interaction with ITGA5, consequently suppressing the FAK/Paxillin/AKT pathway activity. These results suggested that TEFT might exert its antitumor effects by downregulating COL6A1 and thereby inhibiting the activity of the focal adhesion pathway.

CONCLUSION

TEFT could remodel the ECM of GBM cells by downregulating COL6A1 expression and inhibiting focal adhesion pathway. COL6A1 could interact with ITGA5 and activate the focal adhesion pathway, suggesting that it might be a potential therapeutic target mediating the antitumor effects of TEFT.

Identification of key pathways and mRNAs in interstitial cystitis/bladder pain syndrome treatment with quercetin through bioinformatics analysis of mRNA-sequence data

BACKGROUND

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic condition with painful bladder. At present, the pathogenesis of IC/BPS is still unknown. Quercetin (QCT) is a kind of natural flavonoid with wide sources and multiple biological activities. The purpose of this study was to explore the effects of QCT on mRNA expression and related regulatory signal pathways in IC model rats.

METHODS

LL-37 was used to induce the IC/BPS model rats. 20 mg/kg QCT was injected intraperitoneally into IC/BPS rats. ELISA, HE, Masson and TB staining were used to evaluate the level of inflammation and pathology. The concentration of QCT in rats was detected by HPLC. The mRNA sequencing was used to detect the differentially expressed (DE) mRNA in each group. The over-expression experiment of Lpl was carried out in IC/BPS model rats.

RESULTS

QCT treatment significantly decreased the level of MPO, IL-1β, IL-6 and TNF-α induced by LL-37 in rats, and alleviated bladder injury and mast cell degranulation. There were significant differences in mRNA sequencing data between groups, and the hub gene Lpl were screened by Cytohubba. The expression of Lpl was downregulated in IC/BPS rats. QCT intervention promoted Lpl expression. Overexpression of Lpl reduced the bladder injury induced by LL-37, increased GAG level and decreased the expression of MPO, IL-1β, IL-6 and TNF-α.

CONCLUSION

In this study, we provided the DE mRNA in IC/BPS rats treated with QCT, the signaling pathways for DE enrichment, screened out the hub genes, and revealed that Lpl overexpression alleviated IC/BPS model rats.

ICBP90, an epigenetic regulator, induces DKK3 promoter methylation, promotes glioma progression, and reduces sensitivity to cis-platinum

Glioma is the most common brain malignancy, characterized by high morbidity, high mortality, and treatment-resistance. Inverted CCAAT box Binding Protein of 90 kDa (ICBP90) has been reported to be involved in tumor progression and the maintenance of DNA methylation. Herein, we constructed ICBP90 over-expression and knockdown glioma cell lines, and found that ICBP90 knockdown inhibited glioma cell proliferation, migration, and invasion. ICBP90 silencing potentially enhanced cellular sensitivity to cis-platinum (DDP) and exacerbated DDP-induced pyroptosis, manifested by the elevated levels of gasdermin D-N-terminal and cleaved caspase 1; whereas, ICBP90 over-expression exhibited the opposite effects. Consistently, ICBP90 knockdown inhibited tumor growth in an in vivo mouse xenograft study using U251 cells stably expressing sh-ICBP90 and oe-ICBP90. Further experiments found that ICBP90 reduced the expression of Dickkopf 3 homolog (DKK3), a negative regulator of β-catenin, by binding its promoter and inducing DNA methylation. ICBP90 knockdown prevented the nuclear translocation of β-catenin and suppressed the expression of c-Myc and cyclin D1. Besides, DKK3 over-expression restored the effects of ICBP90 over-expression on cell proliferation, migration, invasion, and DDP sensitivity. Our findings suggest that ICBP90 inhibits the expression of DKK3 in glioma by maintaining DKK3 promoter methylation, thereby conducing to ICBP90-mediated carcinogenesis and drug insensitivity.

A Human Adenovirus C Infection-Related Gene Panel for Predicting Survival and Treatment Responsiveness in Glioma Patients

BACKGROUND

Viruses are critical for the regulation of cancer development and for therapy. Human adenovirus C (HadVC) has been detected in central nervous system and glioma tissue. The objective of the present study was the development of a robust prognostic model based on HadVC infection (HadVCi)-relevant genes.

METHODS

The genome, transcriptome, and virome were systemically analyzed using The Cancer Genome Atlas dataset for training and 2 cohorts from the Chinese Glioma Genome Atlas and an immunotherapy trial cohort with 17 patients receiving anti-PD-1 treatment for validation. HadVCi-relevant gene selection from differentially expressed genes between HadVC-infected and non-HadVC-infected glioma patients using least absolute shrinkage and selection operator regression was followed by Cox regression modeling to establish a prognostic HadVCi score. Kaplan-Meier and receiver operating characteristic curve analyses were performed to estimate the predictive capacity of the HadVCi score. The χ2, Spearman, and Mann-Whitney U tests were used to identify the correlation with the clinicopathological parameters, treatment responsiveness, and immune landscape. Temozolomide-resistant glioma cells were established and analyzed at the transcriptional level using RNA sequencing data.

RESULTS

The HadVCi score was (-0.2526673∗TRPC6) + (-0.2244276∗RNF207) + (-0.0894468∗SEC31B) + (-0.0190214∗ZCRB1) + (-0.017122∗DNPH1) + (0.0495818∗CCDC34) + (0.1196349∗PURG) + (0.1778997∗LILRA5). The score possesses a strong ability to predict overall survival. Further analysis revealed a higher HadVCi score correlated with a malignant phenotype and poorer treatment responsiveness to temozolomide-based chemotherapy and combined therapies. Additionally, transcriptomic analysis showed malignancy-, stemness-, and radioresistant-related gene activation in the HadVCi group, which characterized the poor outcomes and limited sensitivity to standard therapy.

CONCLUSIONS

The HadVCi score could be an effective tool for survival prediction and treatment guidance for patients with glioma.

Mitochondrial-related genes markers that predict survival in patients with head and neck squamous cell carcinoma affect immunomodulation through hypoxia, glycolysis, and angiogenesis pathways

Mitochondria play a crucial role in the occurrence and development of tumors. We used mitochondria-related genes for consistent clustering to identify three stable molecular subtypes of head and neck squamous cell carcinoma (HNSCC) with different prognoses, mutations, and immune characteristics. Significant differences were observed in clinical characteristics, immune microenvironment, immune cell infiltration, and immune cell scores. TP53 was the most significantly mutated; cell cycle-related pathways and tumorigenesis-related pathways were activated in different subtypes. Risk modeling was conducted using a multifactor stepwise regression method, and nine genes were identified as mitochondria-related genes affecting prognosis (DKK1, EFNB2, ITGA5, AREG, EPHX3, CHGB, P4HA1, CCND1, and JCHAIN). Risk score calculations revealed significant differences in prognosis, immune cell scores, immune cell infiltration, and responses to conventional chemotherapy drugs. Glycolysis, angiogenesis, hypoxia, and tumor-related pathways were positively correlated with the RiskScore. Clinical samples were subjected to qPCR to validate the results. In this work, we constructed a prognostic model based on the mitochondrial correlation score, which well reflects the risk and positive factors for the prognosis of patients with HNSCC. This model can be used to guide individualized adjuvant and immunotherapy in patients with HNSCC.

Establishment and validation of a novel integrin-based prognostic gene signature that sub-classifies gliomas and effectively predicts immunosuppressive microenvironment

The integrin family members play a key role in cancer immunomodulation and prognosis. We comprehensively analyzed the expression patterns and clinical significance of integrin family-related genes in gliomas. A total of 2293 gliomas from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gliovis platform were enrolled for analyses. Twenty-six integrin coding genes showed different expression patterns between glioma and normal brain tissues. We screened an integrin family-related gene signature (ITGA5, ITGA9, ITGAE, ITGB7 and ITGB8) that showed independent prognostic value and sub-classified gliomas into different prognostic and molecular clusters, further composed an integrin-based risk score model associated with glioma malignant clinical features, overall survival (OS), and immune microenvironment alterations. Besides, glioma patients with high-risk scores showed chemotherapeutic resistance and more immune cells infiltration as well as high immune checkpoints expression. Concurrently, we also revealed that high-risk score group presented resistance to T cell-mediated cancer killing process and lower rates of response to immune checkpoint blockade (ICB) treatment. In conclusion, our study identified a valuable integrin gene signature that predicted gliomas OS effectively, and sub-classified them into different phenotypes and accompanied with immunological changes, possibly acted as a biomarker for ICB treatment.

A transcriptomic pan-cancer signature for survival prognostication and prediction of immunotherapy response based on endothelial senescence

BACKGROUND

The microvascular endothelium inherently controls nutrient delivery, oxygen supply, and immune surveillance of malignant tumors, thus representing both biological prerequisite and therapeutic vulnerability in cancer. Recently, cellular senescence emerged as a fundamental characteristic of solid malignancies. In particular, tumor endothelial cells have been reported to acquire a senescence-associated secretory phenotype, which is characterized by a pro-inflammatory transcriptional program, eventually promoting tumor growth and formation of distant metastases. We therefore hypothesize that senescence of tumor endothelial cells (TEC) represents a promising target for survival prognostication and prediction of immunotherapy efficacy in precision oncology.

METHODS

Published single-cell RNA sequencing datasets of different cancer entities were analyzed for cell-specific senescence, before generating a pan-cancer endothelial senescence-related transcriptomic signature termed EC.SENESCENCE.SIG. Utilizing this signature, machine learning algorithms were employed to construct survival prognostication and immunotherapy response prediction models. Machine learning-based feature selection algorithms were applied to select key genes as prognostic biomarkers.

RESULTS

Our analyses in published transcriptomic datasets indicate that in a variety of cancers, endothelial cells exhibit the highest cellular senescence as compared to tumor cells or other cells in the vascular compartment of malignant tumors. Based on these findings, we developed a TEC-associated, senescence-related transcriptomic signature (EC.SENESCENCE.SIG) that positively correlates with pro-tumorigenic signaling, tumor-promoting dysbalance of immune cell responses, and impaired patient survival across multiple cancer entities. Combining clinical patient data with a risk score computed from EC.SENESCENCE.SIG, a nomogram model was constructed that enhanced the accuracy of clinical survival prognostication. Towards clinical application, we identified three genes as pan-cancer biomarkers for survival probability estimation. As therapeutic perspective, a machine learning model constructed on EC.SENESCENCE.SIG provided superior pan-cancer prediction for immunotherapy response than previously published transcriptomic models.

CONCLUSIONS

We here established a pan-cancer transcriptomic signature for survival prognostication and prediction of immunotherapy response based on endothelial senescence.

Multiomics integration reveals the effect of Orexin A on glioblastoma

Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment. Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A. Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo. Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.

Comprehensive analysis of HHV-6 and HHV-7-related gene signature in prognosis and response to temozolomide of glioma

Human herpesvirus (HHV)-6 and HHV-7 have been detected in central nervous system and glioma tissue, while their exact role in glioma remains uncertain. Omics profiles and clinical information were downloaded from public databases, including The Cancer Genome Atlas cohort for training set and the Chinese Glioma Genome Atlas cohorts for validation sets. Differentially expressed genes between HHV-6 and HHV-7 infected or noninfected glioma patients were screened for establishing the HHV-6 and HHV-7 infection (HI) model through Lasso regression analysis. Bioinformatics methods were used to analyze the correlation between HI scores and prognosis, metastasis in glioma patients. Predictable efficacy of HI in temozolomide-resistance and HI-related genetic signatures were also explored. The HI model was constructed as: Risk score = (0.014709*DIRAS3) + (0.029787*TEX26) + (0.223492*FBXO39) + (0.074951*MYBL1) + (0.060202*HILS1). The five gene signature showed good performance in predicting survival time for glioma patients, while higher HI score is correlated with malignant features. Moreover, DNA mismatch repair genes were augmented in glioma patients with higher HI score as well as nonresponse to temozolomide treatment, which was in parallel with the transcriptomic result of temozolomide-resistant glioma cell. Targeting the five gene signature is beneficial for prognosis of glioma patients, especially in glioma patients underwent temozolomide treatment.

Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness

With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.

The impact of temozolomide and lonafarnib on the stemness marker expression of glioblastoma cells in multicellular spheroids

Glioblastoma multiforme (GBM) is a highly malignant brain tumor with poor prognosis. The GBM microenvironment is highly heterogeneous and is composed of many cell types including astrocytes and endothelial cells (ECs) along with tumor cells, which are responsible for heightened resistance to standard chemotherapeutic drugs such as Temozolomide (TMZ). Here, we investigated how drug treatments impact stemness marker expression of GBM cells in multicellular tumor spheroid (MCTS) models. Co- and tri-culture MCTS constructed using U87-MG GBM cells, astrocytes and/or ECs were cultured for 7 days. At day 7, 5 μM lonafarnib (LNF), 100 μM TMZ, or combination of 5 μM LNF + 100 μM TMZ was added and the MCTS were cultured for an additional 48 h. We assessed the spheroid sizes and expression of stemness markers- NESTIN, SOX2, CD133, NANOG, and OCT4- through qRT-PCR and immunostaining. Following 48 h treatment with LNF, TMZ or their combination (LNF+TMZ), the spheroid sizes decreased compared to the untreated control. We also observed that the expression of most of the stemness markers significantly increased in the LNF+TMZ treated condition as compared to the untreated condition. These results indicate that drug treatment can influence the stemness marker expression of GBM cells in MCTS models and these aspects must be considered while evaluating therapies. In future, by incorporating other relevant cell types, we can further our understanding of their crosstalk, eventually leading to the development of new therapeutic strategies.

Construction of a prognostic classifier and prediction of the immune landscape and immunosuppressive molecules in gliomas based on combination of inflammatory response-related genes and angiogenesis-associated genes

Objective:We aimed to combine inflammatory response-related genes (IRRGs) and angiogenesis-associated genes (AAGs) to build a prognostic classifier and to predict immune landscapes and immunosuppressive molecules in gliomas.

Introduction: Gliomas, the commonest primary brain tumors, account for about 80% of cancerous tumors in the central nervous system (CNS), featuring rapid progression, high malignancy, and extremely poor prognosis. The induction of inflammatory responses and angiogenesis have been considered to be closely related to tumors. However, there are little publications systematically elaborating on their impacts on gliomas.

Methods: We downloaded the data of IRRGs and AAGs from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and retrieved 68 differentially expressed genes (DEGs), of which 13 DEGs pertained to the prognosis of glioma cases. Next, 9 DEGs were screened from the 13 major DEGs with predictive significance and utilized to build a 9-gene signature as a prognostic risk score model (PRSM) with the aid of univariate Cox regression analyses (CRA) and least absolute shrinkage and selection operator (LASSO)-CRA. On this basis, glioma patients fell into high-risk (HR) group and low-risk (LR) group. Later, we implemented Gene Set Enrichment Analysis (GSEA, Gene Set: WP_ANGIOGENESIS) and calculate the scores of cell infiltration and immune-associated function by harnessing single-sample GSEA (ssGSEA).

Results: The prognosis was compared between the two groups by introducing Kaplan-Meier (KM) analysis, which yielded that HR group exhibited poorer prognosis. Additionally, the predictive capacity and independent characteristics were proven by the receiver operating characteristic curve (ROC) and multivariate CRA. Further, We took an evaluation of immune profiles, which unraveled that immunosuppressive cell count was distinctively larger in HS group. Finally, a protein-protein interaction (PPI) network of DEGs was built, and 10 hub genes were obtained, of which epidermal growth factor receptor (EGFR) was closely related to poor prognosis.

Conclusion: A 9-gene signature was established on the strength of IRRGs and AAGs for predicting glioma prognosis, tumor microenvironment (TME), immune landscapes and immunosuppressive molecules. However, the molecular mechanism developed by this signature to function in tumor immunity needs further experimental research in the future and is expected to be a research target for glioma immunotherapy strategies.