Identification and validation of a hypoxia-related prognostic and immune microenvironment signature in bladder cancer

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer

BACKGROUND

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported.

PURPOSE

Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients.

METHODS

First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments.

RESULTS

A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells.

CONCLUSION

A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke

Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

A Novel Risk Model for lncRNAs Associated with Oxidative Stress Predicts Prognosis of Bladder Cancer

Background

Oxidative stress (OS) reactions are closely related to the development and progression of bladder cancer (BCa). This project aimed to identify new potential biomarkers to predict the prognosis of BCa and improve immunotherapy.

Methods

We downloaded transcriptomic information and clinical data on BCa from The Cancer Genome Atlas (TCGA). Screening for OS genes was statistically different between tumor and adjacent normal tissue. A coexpression analysis between lncRNAs and differentially expressed OS genes was performed to identify OS-related lncRNAs. Then, differentially expressed oxidative stress lncRNAs (DEOSlncRNAs) between tumors and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select the lncRNAs for risk assessment. LASSO analysis was conducted to establish a prognostic model. The prognostic risk model could accurately predict BCa patient prognosis and reveal a close correlation with clinicopathological features. We analyzed the principal component analysis (PCA), immune microenvironment, and half-maximal inhibitory concentration (IC50) in the risk groups.

Results

We constructed a model containing eight DEOSlncRNAs (AC021321.1, AC068196.1, AC008750.1, SETBP1-DT, AL590617.2, THUMPD3-AS1, AC112721.1, and NR4A1AS). The prognostic risk model showed better results in predicting the prognosis of BCa patients and was strongly correlated with clinicopathological characteristics. We found great agreement between the calibration plots and prognostic predictions in this model. The areas under the receiver operating characteristic (ROC) curve (AUCs) at 1, 3, and 5 years were 0.792, 0.804, and 0.843, respectively. This model also showed good predictive ability regarding the tumor microenvironment and tumor mutation burden. In addition, the high-risk group was more sensitive to eight therapeutic agents, and the low-risk group was more responsive to five therapeutic agents. Sixteen immune checkpoints were significantly different between the two risk groups.

Conclusion

Our eight DEOSlncRNA risk models provide new insights into predicting prognosis and clinical progression in BCa patients.

Prognostic significance and identification of basement membrane-associated lncRNA in bladder cancer

Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.

Construction and verification of a novel hypoxia-related lncRNA signature related with survival outcomes and immune microenvironment of bladder urothelial carcinoma by weighted gene co-expression network analysis

Background: Bladder urothelial carcinoma (BLCA) is a common malignant tumor with the greatest recurrence rate of any solid tumor. Hypoxia is crucial in the growth and immune escape of malignant tumors. To predict clinical outcomes and immunological microenvironment of patients with BLCA, a hypoxia-related long non-coding RNA (HRlncRNA) signature was established.

Methods: The Cancer Genome Atlas (TCGA) provided us with the differentially expressed profile of HRlncRNAs as well as clinical data from patients with BLCA, and we used weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with malignancies.

Results: Finally, Cox analysis revealed that HRlncRNAs, which comprised 13 lncRNAs, were implicated in the predictive signature. The training, testing, and overall cohorts of BLCA patients were divided into the low-risk group and high-risk group based on the median of the risk score. The Kaplan–Meier curves revealed that BLCA patients with a high-risk score had a poor prognosis, and the difference between subgroups was statistically significant. The receiver operating characteristic curves revealed that this signature outperformed other strategies in terms of predicting ability. Multivariate analysis revealed that the risk score was an independent prognostic index for overall survival (HR = 1.411; 1.259–1.582; p < 0.001). Then, a nomogram with clinicopathological features and risk score was established. This signature could effectively enhance the capacity to predict survival, according to the calibration plots, stratification, and clinical analysis. The majority of Kyoto Encyclopedia of Genes and Genomes (KEGG) were WNT, MAPK, and ERBB signaling pathways. Two groups had different immune cell subtypes, immune checkpoints, immunotherapy response, and anti-tumor drug sensitivity, which might result in differing survival outcomes. We then validated the differential expression of signature-related genes between tumor and normal tissues using TCGA paired data.

Conclusion: This prognostic signature based on 13 HRlncRNAs may become a novel and potential prognostic biomarker, providing more accurate clinical decision-making and effective treatment for BLCA patients.

Nanodelivery Systems Delivering Hypoxia-Inducible Factor-1 Alpha Short Interfering RNA and Antisense Oligonucleotide for Cancer Treatment

Hypoxia-inducible factor (HIF), which plays a crucial role in oxygen homeostasis, contributes to immunosuppression, tumor angiogenesis, multidrug resistance, photodynamic therapy resistance, and metastasis. HIF as a therapeutic target has attracted scientists’ strong academic research interests. Short interfering RNA (siRNA) and antisense oligonucleotide (ASO) are the more promising and broadly utilized methods for oligonucleotide-based therapy. Their physicochemical characteristics such as hydrophilicity, negative charge, and high molecular weight make them impossible to cross the cell membrane. Moreover, siRNA and ASO are subjected to a rapid deterioration in circulation and cannot translocate into nuclear. Delivery of siRNA and ASO to specific gene targets should be realized without off-target gene silencing and affecting the healthy cells. Nanoparticles as vectors for delivery of siRNA and ASO possess great advantages and flourish in academic research. In this review, we summarized and analyzed regulation mechanisms of HIF under hypoxia, the significant role of HIF in promoting tumor progression, and recent academic research on nanoparticle-based delivery of HIF siRNA and ASO for cancer immunotherapy, antiangiogenesis, reversal of multidrug resistance and radioresistance, potentiating photodynamic therapy, inhibiting tumor metastasis and proliferation, and enhancing apoptosis are reviewed in this thesis. Furthermore, we hope to provide some rewarding suggestions and enlightenments for targeting HIF gene therapy.

Characterization of the Ferroptosis-Related Genes for Prognosis and Immune Infiltration in Low-Grade Glioma

Background: Although ferroptosis has been validated to play a crucial role in some types of tumors, the influence of ferroptosis-related genes (FRGs) on the immune microenvironment in low-grade glioma (LGG) remains unclear. In this research, we screen the FRGs to assess the prognosis value and immune microenvironment in LGG, to provide reliable diagnosis and treatment evidence for the clinic.

Methods: A total of 1,239 patients of LGG samples were selected for subsequent analyses from The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and the Repository of Molecular Brain Neoplasia Data datasets. Univariate Cox regression analysis was used to screen for prognostic FRGs. Consensus clustering was utilized to determine ferroptosis subtypes of LGG patients. Next, the prognostic model was constructed based on differentially expressed FRGs and validation in the validating datasets. The immune microenvironment, biological pathway, and hypoxia score were explored by single-sample gene set enrichment analysis. The potential response of chemotherapy and immune checkpoint blockade therapy was also estimated. In addition, the correlation between the risk score and autophagy-related genes was examined by the Pearson correlation coefficient.

Results: A total of three ferroptosis subtypes were identified by consensus clustering for prognostic FRGs which exhibited different outcomes, clinicopathological characteristics, and immune microenvironment. Afterward, a prognostic model that performed great predictive ability based on nine prognostic FRGs has been constructed and validated. Moreover, the prognostic model had the potential to screen the sensitivity to chemotherapy and immunotherapy in LGG patients. Finally, we also found that the prognostic model has a great connection to autophagy and hypoxia.

Conclusion: We developed a ferroptosis-related prognostic model which strongly linked to diagnosis, treatment, prognosis, and recurrence of LGG. This study also reveals the connection between ferroptosis and tumor immune microenvironment.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

Identifying a hypoxia related score to predict the prognosis of bladder cancer: a study with The Cancer Genome Atlas (TCGA) database

Background

Recurrence is common in bladder cancer, with a hypoxic tumor microenvironment (TME) playing a role in genetic instability and prognosis of bladder cancer. However, we still lack practical hypoxia related model for predicting the prognosis of bladder cancer. In this study, we identified new prognosis-related hypoxia genes and established a new hypoxia score related signature.

Methods

The Gene Set Variation Analysis (GSVA) algorithm was utilized to calculate the hypoxia score of bladder cancer cases found on the The Cancer Genome Atlas (TCGA) database on the gene expression profiles. The cases were first divided into low- and high-hypoxia score groups and then differentially expressed genes (DEGs) expression analysis was conducted. Hypoxia-related genes were identified using weighted gene co-expression network analysis (WGCNA). We then conducted a protein-protein interaction (PPI) network and carried out functional enrichment analysis of the genes that overlapped between DEGs and hypoxia-related genes. LASSO Cox regression analysis was used to establish a hypoxia-related prognostic signature, which was validated using the GSE69795 dataset downloaded from GEO database.

Results

Results from Kaplan-Meier analysis showed that patients with a high hypoxia score had significantly poor overall survival compared to patients with low hypoxia score. We selected 270 DEGs between low- and high-hypoxia score groups, while WGCNA analysis identified 1,313 genes as hypoxia-related genes. A total of 170 genes overlapped between DEGs and hypoxia-related genes. LASSO algorithms identified 29 genes associated with bladder cancer prognosis, which were used to construct a novel 29-gene signature model. The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759–0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585–2.019) (P<0.001). The low-risk score patients had remarkably longer overall survival than patients with a higher score (survival rate 71.06% vs. 23.66%) in the The Cancer Genome Atlas (TCGA) cohort (P<0.0001) and in the dataset GSE69795 (P=0.0079).

Conclusions

We established a novel 29-gene hypoxia-related signature model to predict the prognosis of bladder cancer cases. This model and identified hypoxia-related genes may further been used as biomarkers, assisting the evaluation of prognosis of bladder cancer cases and decision making in clinical practice.

A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients' prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

A circadian rhythm-related gene signature associated with tumor immunity, cisplatin efficacy, and prognosis in bladder cancer

Circadian dysregulation involves malignant tumor initiation and progression, but the understanding of circadian rhythm’s roles in bladder cancer (BCa) remains insufficient. The circadian rhythm-related genes were collected and clustered based on the Cancer Genome Atlas (TCGA), and the clustering was significantly associated with the prognosis and risk clinicopathological features. Through genomic difference analysis and gene pairing, a circadian rhythm-related signature was successfully established. Kaplan-Meier survival analysis and time-dependent receiver operating curves displayed that the prognosis model was a reliable prognosis biomarker both in the training cohort (n = 396, P = 2.687e-10) and external validation cohort (n = 224, P = 1.45e-02). The patients with high risk have high immune infiltration and high expression of immune checkpoint genes, which partly account for the poor prognosis. TIDE algorithm and the validation in IMvigor210 cohort indicated that the risk signature was a promising marker for the immunotherapeutic response. The risk model could also predict the therapeutic response of cisplatin, which was validated in the Genomics of Drug Sensitivity in Cancer database (P = 0.0049), TCGA (P = 0.038), and T24 BCa cells treated with cisplatin. The functional enrichment showed the risk model was significantly correlated with some malignant phenotypes, such as angiogenesis, epithelial-mesenchymal transition, and KRAS signaling pathway. Totally, we proposed a novel circadian rhythm-related signature for prognosis evaluation, which also helped to predict the immune infiltration and cisplatin sensitivity in BCa.