CTLA4 has a profound impact on the landscape of tumor-infiltrating lymphocytes with a high prognosis value in clear cell renal cell carcinoma (ccRCC)

YAP represses the TEAD-NF-κB complex and inhibits the growth of clear cell renal cell carcinoma

The Hippo pathway is generally understood to inhibit tumor growth by phosphorylating the transcriptional cofactor YAP to sequester it to the cytoplasm and reduce the formation of YAP-TEAD transcriptional complexes. Aberrant activation of YAP occurs in various cancers. However, we found a tumor-suppressive function of YAP in clear cell renal cell carcinoma (ccRCC). Using cell cultures, xenografts, and patient-derived explant models, we found that the inhibition of upstream Hippo-pathway kinases MST1 and MST2 or expression of a constitutively active YAP mutant impeded ccRCC proliferation and decreased gene expression mediated by the transcription factor NF-κB. Mechanistically, the NF-κB subunit p65 bound to the transcriptional cofactor TEAD to facilitate NF-κB-target gene expression that promoted cell proliferation. However, by competing for TEAD, YAP disrupted its interaction with NF-κB and prompted the dissociation of p65 from target gene promoters, thereby inhibiting NF-κB transcriptional programs. This cross-talk between the Hippo and NF-κB pathways in ccRCC suggests that targeting the Hippo-YAP axis in an atypical manner-that is, by activating YAP-may be a strategy for slowing tumor growth in patients.

Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

In recent years, RNA methylation modification has been found to be related to a variety of tumor mechanisms, such as rectal cancer. Clear cell renal cell carcinoma (ccRCC) is most common in renal cell carcinoma. In this study, we get the RNA profiles of ccRCC patients from ArrayExpress and TCGA databases. The prognosis model of ccRCC was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis, and the samples were stratified into low-high risk groups. In addition, our prognostic model was validated through the receiver operating characteristic curve (ROC). "pRRophetic" package screened five potential small molecule drugs. Protein interaction networks explore tumor driving factors and drug targeting factors. Finally, polymerase chain reaction (PCR) was used to verify the expression of the model in the ccRCC cell line. The mRNA matrix in ArrayExpress and TCGA databases was used to establish a prognostic model for ccRCC through LASSO regression analysis. Kaplan Meier analysis showed that the overall survival rate (OS) of the high-risk group was poor. ROC verifies the reliability of our model. Functional enrichment analysis showed that there was a obviously difference in immune status between the high-low risk groups. "pRRophetic" package screened five potential small molecule drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706). Protein interaction network shows that epidermal growth factor receptor [EGRF] and estrogen receptor 1 [ESR1] are tumor drivers and drug targeting factors. To further analyze the differential expression and pathway correlation of the prognosis risk model species. Finally, polymerase chain reaction (PCR) showed the expression of YTHN6-Methyladenosine RNA Binding Protein 1[YTHDF1], TRNA Methyltransferase 61B [TRMT61B], TRNA Methyltransferase 10C [TRMT10C] and AlkB Homolog 1[ALKBH1] in ccRCC cell lines. To sum up, the prognosis risk model we created not only has good predictive value, but also can provide guidance for accurately predicting the prognosis of ccRCC.

The use of RNA-based treatments in the field of cancer immunotherapy

Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.

ARHGAP11A Is a Novel Prognostic and Predictive Biomarker Correlated with Immunosuppressive Microenvironment in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor and immune dysfunction is associated with ccRCC poor prognosis. The RhoGTPase-activating proteins (RhoGAPs) family was reported to affect ccRCC development, but its role in immunity and prognosis prediction for ccRCC remain unknown. In the current study, we found ARHGAP11A was the only independent risk factor among 33 RhoGAPs (hazard ratio [HR] 1.949, 95% confidence interval [CI] 1.364-2.785). High ARHGAP11A level was associated with shorter overall survival (OS, HR 2.040, 95% CI 1.646-3.417) and ARHGAP11A is a prognostic biomarker for ccRCC. ARHGAP11A knockdown suppressed renal cell carcinoma (RCC) cell proliferation, colony formation, and migration, suggesting the promoting role of ARHGAP11A on RCC development. Mechanistically, ARHGAP11A might contribute to the suppressive tumor immune microenvironment (TIME). High ARHGAP11A level was correlated with infiltration of immunosuppressive cells (including T helper 2 (Th2) cells, regulatory T (Treg) cells, myeloid derived suppressor cells (MDSC), and M2 macrophage cells), activation of immunosuppressive pathways (IL6-JAK-STAT3 signaling and IFNγ response), and expression of inhibitory immune checkpoints (ICs). ARHGAP11A could promote T cell exhaustion and induce immune escape. ccRCC patients with low ARHGAP11A level were more suitable for immune checkpoint inhibitors (ICIs) therapy, while those with high ARHGAP11A level might benefit from a combination of ARHGAP11A blockade and ICIs. In all, ARHGAP11A might serve as a novel prognostic marker, therapeutic target, and predictor in the clinical response to ICIs therapy for ccRCC.

Computed tomography‐based radiomics prediction of CTLA4 expression and prognosis in clear cell renal cell carcinoma

OBJECTIVES

To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors.

METHODS

The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis.

RESULTS

CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months.

CONCLUSIONS

CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma

Aims: This research aimed to construct a signature based on chromatin regulation in localized clear cell renal cell carcinoma (ccRCC). Materials & methods: Non-negative matrix factorization clustering was performed on 438 localized ccRCC cases. The immune infiltration was generated by the single-sample gene set enrichment analysis algorithm. Survival analyses were performed using the Kaplan-Meier method, and the significance of the differences was determined using the log-rank test. The risk score was constructed based on the expression of chromatin regulators to quantify chromatin modification. Results: A score system based on chromatin modification was established. The high-risk subtype was characterized by increased tumor mutation burden, whereas a low-risk score was characterized by an increase in chromatin regulator expression and better overall survival. Conclusion: This research has constructed a signature based on chromatin regulation in localized ccRCC.

Identification and Verification of an Alternative Polyadenylation-Related lncRNA Prognostic Signature for Glioma

Due to the high mortality and modality of glioma, it was urgently needed to develop a glioma prognostic assessment system. Previous studies demonstrated that alternative polyadenylation- (APA-) related genes are important in immune response and oncogenesis. mRNA and lncRNA expression information of glioma samples were acquired from CGGA and TCGA databases, and lncRNAs associated with APA were selected through correlation analysis. The prognosis model of APA-related lncRNAs was built by the univariate Cox, random forest, and univariate Cox regression analyses. Glioma samples were assigned into high- and low-risk groups. Independence and effectiveness of the prognostic model were evaluated by Kaplan-Meier analysis, ROC curve, and Cox regression analyses. GO, KEGG enrichment, and GSEA analyses showed that the mainly involved signaling pathways were enriched in cellular immunity and immune signal transduction. We further analyzed expression differences of negative immune regulatory genes and immune cell infiltration degree between two groups. Immune checkpoints CTLA4 and LAG3 and immune suppressors TGFB, IL10, NOS3, and IDO1 and immune cell infiltration were notably upregulated in the high-risk group. The PD1/PDL1 expression was significantly correlated with risk score, showing that the prognostic model of APA-related lncRNA could effectively assess the tumor immune suppression. In conclusion, we established a risk assessment model of APA-related lncRNA in glioma, which could effectively evaluate prognosis of patients with glioma and tumor immune suppression and could provide guidance for clinical treatment.

Decoding the Immune Microenvironment of Clear Cell Renal Cell Carcinoma by Single-Cell Profiling to Aid Immunotherapy

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, and it is the major cause of kidney cancer death. Understanding tumor immune microenvironments (TMEs) is critical in cancer immunotherapies. Here, we studied the immune characterization at single-cell resolution by integrating public data of ccRCC across different tissue types, and comparing the transcriptome features and tumor TME differences in tumors, normal adjacent tissue, and peripheral blood. A total of 16 different types of cell components of ccRCC were identified. We revealed that there is an overall increase in T-cell and myeloid populations in tumor-infiltrated immune cells compared to normal renal tissue, and the B-cell population in the tumor showed a sharp decrease, which indicates that the cells in tumor tissue undergo strong immune stress. In addition, the cell-cell communication analysis revealed specific or conserved signals in different tissue types, which may aid to uncover the distinct immune response. By combining and analyzing publicly available ccRCC bulk RNA-seq datasets, 10 genes were identified as marker genes in specific cell types, which were significantly associated with poor prognosis. Of note, UBE2C, which may be a good indicator of tumor proliferation, is positively associated with reductions in overall survival and highly associated with tumor grade. Our integrated analysis provides single-cell transcriptomic profiling of ccRCC and their TME, and it unmasked new correlations between gene expression, survival outcomes, and immune cell-type components, enabling us to dissect the dynamic variables in the tumor development process. This resource provides deeper insight into the transcriptome features and immune response of ccRCC and will be helpful in kidney cancer immunotherapy.

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Background

Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis.

Methods

The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package.

Results

In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration.

Conclusions

Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma

In recent years, methylation modification regulators have been found to have essential roles in various tumor mechanisms. However, the relationships between N⁶-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators and clear cell renal cell carcinoma (ccRCC) remain unknown. This study investigated these relationships using the data from The Cancer Genome Atlas database. We calculated risk scores using a Lasso regression analysis and divided the patient samples into two risk groups (tumor vs. normal tissues). Furthermore, we used univariate and multivariate Cox analyses to determine independent prognostic indicators and explore correlations between the regulatory factors and immune infiltrating cell characteristics. Finally, quantitative reverse transcriptase–polymerase chain reaction (PCR) and The Human Protein Atlas were used to verify signature-related gene expression in clinical samples. We identified expression differences in 35 regulatory factors between the tumor and normal tissue groups. Next, we constructed a five-gene risk score signature (NOP2 nucleolar protein [NOP2], methyltransferase 14, N6-adenosine-methyltransferase subunit [METTL14], NOP2/Sun RNA methyltransferase 5 [NSUN5], heterogeneous nuclear ribonucleoprotein A2/B1 [HNRNPA2B1], and zinc finger CCCH-type containing 13 [ZC3H13]) using the screening criteria (p < 0.01), and then divided the cases into high- and low-risk groups based on their median risk score. We also screened for independent prognostic factors related to age, tumor grade, and risk score. Furthermore, we constructed a Norman diagram prognostic model by combining two clinicopathological characteristics, which demonstrated good prediction efficiency with prognostic markers. Then, we used a single-sample gene set enrichment analysis and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method to evaluate the tumor microenvironment of the regulatory factor prognostic characteristics. Moreover, we evaluated five risk subgroups with different genetic signatures for personalized prognoses. Finally, we analyzed the immunotherapy and immune infiltration response and demonstrated that the high-risk group was more sensitive to immunotherapy than the low-risk group. The PCR results showed that NSUN5 and HNRNPA2B1 expression was higher in tumor tissues than in normal tissues. In conclusion, we identified five m6A and m5C regulatory factors that might be promising biomarkers for future research.

Elevated SNRPA1, as a Promising Predictor Reflecting Severe Clinical Outcome via Effecting Tumor Immunity for ccRCC, Is Related to Cell Invasion, Metastasis, and Sunitinib Sensitivity

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and is associated with poor prognosis and notorious for its immune dysfunction characteristic. SNRPA1 is a spliceosome component responsible for processing pre-mRNA into mRNA, while the biological effect of SNRPA1 in ccRCC remains elusive. The aim of this study was to decipher the effect of SNRPA1 on clinical effect and tumor immunity for ccRCC patients. Multi-databases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor immune microenvironment, and drug sensitivity of SNRPA1 on ccRCC. IHC was utilized to validate the expression and prognostic value of SNRPA1 in ccRCC patients from the SMMU cohort. The knockout expression of SNRPA by sgRNA plasmid inhibited the cell proliferation, migration, and metastasis ability and significantly increased the sensitivity of sunitinib treatment. In addition, we explored the role of SNRPA1 in pan-cancer level. The results indicated that SNRPA1 was differentially expressed in most cancer types. SNRPA1 may significantly influence the prognosis of multiple cancer types, especially in ccRCC patients. Notably, SNRPA1 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, the aggressive and immune inhibitory effects shown in SNRPA1 overexpression and the effect of SNRPA1 on ccRCC cell line invasion, metastasis, and drug sensitivity in vitro were observed. Moreover, SNRPA1 was related to Myc, MTORC, G2M, E2F, and DNA repair pathways in various cancer types. In all, SNRPA1 may prove to be a new biomarker for prognostic prediction, effect tumor immunity, and drug susceptibility in ccRCC.

ISPRF: a machine learning model to predict the immune subtype of kidney cancer samples by four genes

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the proper patients for immunotherapy.

Methods

A total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by a random forest (RF) algorithm and used on an open and free online website to predict the immune subtype.

Results

In the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified four hub genes related to immune subtypes, CTLA4, FOXP3, IFNG, and CD19. The RF model was constructed by mRNA expression of these four hub genes, and the value of area under the receiver operating characteristic curve (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the RF model (https://immunotype.shinyapps.io/ISPRF/).

Conclusions

The current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.

From Bench to Bedside: How the Tumor Microenvironment Is Impacting the Future of Immunotherapy for Renal Cell Carcinoma

Immunotherapy has revolutionized the treatment landscape for many cancer types. The treatment for renal cell carcinoma (RCC) has especially evolved in recent years, from cytokine-based immunotherapies to immune checkpoint inhibitors. Although clinical benefit from immunotherapy is limited to a subset of patients, many combination-based approaches have led to improved outcomes. The success of such approaches is a direct result of the tumor immunology knowledge accrued regarding the RCC microenvironment, which, while highly immunogenic, demonstrates many unique characteristics. Ongoing translational work has elucidated some of the mechanisms of response, as well as primary and secondary resistance, to immunotherapy. Here, we provide a comprehensive review of the RCC immunophenotype with a specific focus on how preclinical and clinical data are shaping the future of immunotherapy.

Aiduqing formula inhibits breast cancer metastasis by suppressing TAM/CXCL1-induced Treg differentiation and infiltration

Background

Metastasis represents the leading cause of death in patients with breast cancer. Traditional Chinese medicine is particularly appreciated for metastatic diseases in Asian countries due to its benefits for survival period prolongation and immune balance modulation. However, the underlying molecular mechanisms remain largely unknown. This study aimed to explore the antimetastatic effect and immunomodulatory function of a clinical formula Aiduqing (ADQ).

Methods

Naive CD4⁺ T cells, regulatory T cells (Tregs), and CD8⁺ T cells were sorted by flow cytometry. Then, breast cancer cells and these immune cells were co-cultured in vitro or co-injected into mice in vivo to simulate their coexistence. Flow cytometry, ELISA, qPCR, double luciferase reporter gene assay, and chromatin immunoprecipitation assay were conducted to investigate the immunomodulatory and antimetastatic mechanisms of ADQ.

Results

ADQ treatment by oral gavage significantly suppressed 4T1-Luc xenograft growth and lung metastasis in the orthotopic breast cancer mouse model, without noticeable hepatotoxicity, nephrotoxicity, or hematotoxicity. Meanwhile, ADQ remodeled the immunosuppressive tumor microenvironment (TME) by increasing the infiltration of tumor-infiltrating lymphocytes (TILs) and cytotoxic CD8⁺ T cells, and decreasing the infiltration of Tregs, naive CD4⁺ T cells, and tumor-associated macrophages (TAMs). Molecular mechanism studies revealed that ADQ remarkably inhibited CXCL1 expression and secretion from TAMs and thus suppressed the chemotaxis and differentiation of naive CD4⁺ T cells into Tregs, leading to the enhanced cytotoxic effects of CD8⁺ T cells. Mechanistically, TAM-derived CXCL1 promoted the differentiation of naive CD4⁺ T cells into Tregs by transcriptionally activating the NF-κB/FOXP3 signaling. Lastly, mouse 4T1-Luc xenograft experiments validated that ADQ formula inhibited breast cancer immune escape and lung metastasis by suppressing the TAM/CXCL1/Treg pathway.

Conclusions

This study not only provides preclinical evidence supporting the application of ADQ in inhibiting breast cancer metastasis but also sheds novel insights into TAM/CXCL1/NF-κB/FOXP3 signaling as a promising therapeutic target for Treg modulation and breast cancer immunotherapy.

Multidiscipline Immunotherapy-Based Rational Combinations for Robust and Durable Efficacy in Brain Metastases from Renal Cell Carcinoma

Advanced imaging techniques for diagnosis have increased awareness on the benefits of brain screening, facilitated effective control of extracranial disease, and prolonged life expectancy of metastatic renal cell carcinoma (mRCC) patients. Brain metastasis (BM) in patients with mRCC (RCC-BM) is associated with grave prognoses, a high degree of morbidity, dedicated assessment, and unresponsiveness to conventional systemic therapeutics. The therapeutic landscape of RCC-BM is rapidly changing; however, survival outcomes remain poor despite standard surgery and radiation, highlighting the unmet medical needs and the requisite for advancement in systemic therapies. Immune checkpoint inhibitors (ICIs) are one of the most promising strategies to treat RCC-BM. Understanding the role of brain-specific tumor immune microenvironment (TIME) is important for developing rationale-driven ICI-based combination strategies that circumvent tumor intrinsic and extrinsic factors and complex positive feedback loops associated with resistance to ICIs in RCC-BM via combination with ICIs involving other immunological pathways, anti-antiangiogenic multiple tyrosine kinase inhibitors, and radiotherapy; therefore, novel combination approaches are being developed for synergistic potential against RCC-BM; however, further prospective investigations with longer follow-up periods are required to improve the efficacy and safety of combination treatments and to elucidate dynamic predictive biomarkers depending on the interactions in the brain TIME.

The regulation of immune checkpoints by the hypoxic tumor microenvironment

The tumor microenvironment (TME) influences the occurrence and progression of tumors, and hypoxia is an important characteristic of the TME. The expression of programmed death 1 (PD1)/programmed death-ligand 1 (PDL1), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and other immune checkpoints in hypoxic malignant tumors is often significantly increased, and is associated with poor prognosis. The application of immune checkpoint inhibitors (ICIs) for treating lung cancer, urothelial carcinoma, and gynecological tumors has achieved encouraging efficacy; however, the rate of efficacy of ICI single-drug treatment is only about 20%. In the present review, we discuss the possible mechanisms by which the hypoxic TME regulates immune checkpoints. By activating hypoxia-inducible factor-1α (HIF-1α), regulating the adenosine (Ado)-A2aR pathway, regulating the glycolytic pathway, and driving epithelial-mesenchymal transition (EMT) and other biological pathways, hypoxia regulates the expression levels of CTLA4, PD1, PDL1, CD47, lymphocyte activation gene 3 (LAG3), T-cell immunoglobulin and mucin domain 3 (TIM3), and other immune checkpoints, which interfere with the immune effector cell anti-tumor response and provide convenient conditions for tumors to escape immune surveillance. The combination of HIF-1α inhibitors, Ado-inhibiting tumor immune microenvironment regulatory drugs, and other drugs with ICIs has good efficacy in both preclinical studies and phase I-II clinical studies. Exploring the effects of TME hypoxia on the expression of immune checkpoints and the function of infiltrating immune cells has greatly clarified the relationship between the hypoxic TME and immune escape, which is of great significance for the development of new drugs and the search for predictive markers of the efficacy of immunotherapy for treating malignant tumors. In the future, combination therapy with hypoxia pathway inhibitors and ICIs may be an effective anti-tumor treatment strategy.