Tumor-Experienced Human NK Cells Express High Levels of PD-L1 and Inhibit CD8+ T Cell Proliferation

PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives

The advent of PD1/PD-L1 inhibitors has significantly transformed the therapeutic landscape for clear cell renal cell carcinoma (ccRCC). This review provides an in-depth analysis of the biological functions and regulatory mechanisms of PD1 and PD-L1 in ccRCC, emphasizing their role in tumor immune evasion. We comprehensively evaluate the clinical efficacy and safety profiles of PD1/PD-L1 inhibitors, such as Nivolumab and Pembrolizumab, through a critical examination of recent clinical trial data. Furthermore, we discuss the challenges posed by resistance mechanisms to these therapies and potential strategies to overcome them. We also explores the synergistic potential of combination therapies, integrating PD1/PD-L1 inhibitors with other immunotherapies, targeted therapies, and conventional modalities such as chemotherapy and radiotherapy. In addition, we examine emerging predictive biomarkers for response to PD1/PD-L1 blockade and biomarkers indicative of resistance, providing a foundation for personalized therapeutic approaches. Finally, we outline future research directions, highlighting the need for novel therapeutic strategies, deeper mechanistic insights, and the development of individualized treatment regimens. Our work summarizes the latest knowledge and progress in this field, aiming to provide a valuable reference for improving clinical efficacy and guiding future research on the application of PD1/PD-L1 inhibitors in ccRCC.

synNotch-programmed iPSC-derived NK cells usurp TIGIT and CD73 activities for glioblastoma therapy

Severe heterogeneity within glioblastoma has spurred the notion that disrupting the interplay between multiple elements on immunosuppression is at the core of meaningful anti-tumor responses. T cell immunoreceptor with Ig and ITIM domains (TIGIT) and its glioblastoma-associated antigen, CD155, form a highly immunosuppressive axis in glioblastoma and other solid tumors, yet targeting of TIGIT, a functionally heterogeneous receptor on tumor-infiltrating immune cells, has largely been ineffective as monotherapy, suggesting that disruption of its inhibitory network might be necessary for measurable responses. It is within this context that we show that the usurpation of the TIGIT - CD155 axis via engineered synNotch-mediated activation of induced pluripotent stem cell-derived natural killer (NK) cells promotes transcription factor-mediated activation of a downstream signaling cascade that results in the controlled, localized blockade of CD73 to disrupt purinergic activity otherwise resulting in the production and accumulation of immunosuppressive extracellular adenosine. Such "decoy" receptor engages CD155 binding to TIGIT, but tilts inhibitory TIGIT/CD155 interactions toward activation via downstream synNotch signaling. Usurping activities of TIGIT and CD73 promotes the function of adoptively transferred NK cells into intracranial patient-derived models of glioblastoma and enhances their natural cytolytic functions against this tumor to result in complete tumor eradication. In addition, targeting both receptors, in turn, reprograms the glioblastoma microenvironment via the recruitment of T cells and the downregulation of M2 macrophages. This study demonstrates that TIGIT/CD155 and CD73 are targetable receptor partners in glioblastoma. Our data show that synNotch-engineered pluripotent stem cell-derived NK cells are not only effective mediators of anti-glioblastoma responses within the setting of CD73 and TIGIT/CD155 co-targeting, but represent a powerful allogeneic treatment option for this tumor.

Tumour response to hypoxia: understanding the hypoxic tumour microenvironment to improve treatment outcome in solid tumours

Hypoxia is a common feature of solid tumours affecting their biology and response to therapy. One of the main transcription factors activated by hypoxia is hypoxia-inducible factor (HIF), which regulates the expression of genes involved in various aspects of tumourigenesis including proliferative capacity, angiogenesis, immune evasion, metabolic reprogramming, extracellular matrix (ECM) remodelling, and cell migration. This can negatively impact patient outcomes by inducing therapeutic resistance. The importance of hypoxia is clearly demonstrated by continued research into finding clinically relevant hypoxia biomarkers, and hypoxia-targeting therapies. One of the problems is the lack of clinically applicable methods of hypoxia detection, and lack of standardisation. Additionally, a lot of the methods of detecting hypoxia do not take into consideration the complexity of the hypoxic tumour microenvironment (TME). Therefore, this needs further elucidation as approximately 50% of solid tumours are hypoxic. The ECM is important component of the hypoxic TME, and is developed by both cancer associated fibroblasts (CAFs) and tumour cells. However, it is important to distinguish the different roles to develop both biomarkers and novel compounds. Fibronectin (FN), collagen (COL) and hyaluronic acid (HA) are important components of the ECM that create ECM fibres. These fibres are crosslinked by specific enzymes including lysyl oxidase (LOX) which regulates the stiffness of tumours and induces fibrosis. This is partially regulated by HIFs. The review highlights the importance of understanding the role of matrix stiffness in different solid tumours as current data shows contradictory results on the impact on therapeutic resistance. The review also indicates that further research is needed into identifying different CAF subtypes and their exact roles; with some showing pro-tumorigenic capacity and others having anti-tumorigenic roles. This has made it difficult to fully elucidate the role of CAFs within the TME. However, it is clear that this is an important area of research that requires unravelling as current strategies to target CAFs have resulted in worsened prognosis. The role of immune cells within the tumour microenvironment is also discussed as hypoxia has been associated with modulating immune cells to create an anti-tumorigenic environment. Which has led to the development of immunotherapies including PD-L1. These hypoxia-induced changes can confer resistance to conventional therapies, such as chemotherapy, radiotherapy, and immunotherapy. This review summarizes the current knowledge on the impact of hypoxia on the TME and its implications for therapy resistance. It also discusses the potential of hypoxia biomarkers as prognostic and predictive indictors of treatment response, as well as the challenges and opportunities of targeting hypoxia in clinical trials.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Interplay between inflammasomes and PD-1/PD-L1 and their implications in cancer immunotherapy

The inflammasomes play crucial roles in inflammation and cancer development, while the PD-1/PD-L1 pathway is critical for immune suppression in the tumor microenvironment (TME). Recent research indicates a reciprocal regulatory relationship between inflammasomes and PD-1/PD-L1 signaling in cancer development and PD-1 blockade treatment. By activating in diverse cells in tumor tissues, inflammasome upregulates PD-L1 level in the TME. Moreover, the regulation of PD-1/PD-L1 activity by inflammasome activation involves natural killer cells, tumor-associated macrophages and myeloid-derived suppressor cells. Conversely, PD-1 blockade can activate the inflammasome, potentially influencing treatment outcomes. The interplay between inflammasomes and PD-1/PD-L1 has profound and intricate effects on cancer development and treatment. In this review, we discuss the crosstalk between inflammasomes and PD-1/PD-L1 in cancers, exploring their implications for tumorigenesis, metastasis and immune checkpoint inhibitor (ICI) resistance. The combined therapeutic strategies targeting both inflammasomes and checkpoint molecules hold promising potential as treatments for cancer.

Harnessing Natural Killer Cells for Lung Cancer Therapy

Lung cancer is the leading cause of cancer-related death worldwide. Although natural killer (NK) cells are garnering interest as a potential anticancer therapy because they selectively recognize and eliminate cancer cells, their use in treating solid tumors, including lung cancer, has been limited due to impediments to their efficacy, such as their limited ability to reach tumor tissues, the reduced antitumor activity of tumor-infiltrating NK cells, and the suppressive tumor microenvironment (TME). This comprehensive review provides an in-depth analysis of the cross-talk between the lung cancer TME and NK cells. We highlight the various mechanisms used by the TME to modulate NK-cell phenotypes and limit infiltration, explore the role of the TME in limiting the antitumor activity of NK cells, and discuss the current challenges and obstacles that hinder the success of NK-cell-based immunotherapy for lung cancer. Potential opportunities and promising strategies to address these challenges have been implemented or are being developed to optimize NK-cell-based immunotherapy for lung cancer. Through critical evaluation of existing literature and emerging trends, this review provides a comprehensive outlook on the future of NK-cell-based immunotherapy for treating lung cancer.

A cuproptosis-related lncRNA signature-based prognostic model featuring on metastasis and drug selection strategy for patients with lung adenocarcinoma

Introduction: Lung adenocarcinoma is a common cause of mortality in patients with cancer. Recent studies have indicated that copper-related cell death may not occur in the same way as previously described. Long non-coding RNAs (lncRNAs) play a key role in the occurrence and development of tumors; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and lung adenocarcinoma (LUAD) treatment has not been well established. Our study aimed to construct a model to analyze the prognosis of lung adenocarcinoma in patients using a carcinogenesis-related lncRNA (CR) signature. Methods: The transcriptional profiles of 507 samples from The Cancer Genome Atlas were assessed. Cox regression and co-expression analyses, and the least absolute shrinkage and selection operator (LASSO) were used to filter the CR and develop the model. The expression status of the six prognostic CRs was used to classify all samples into high- and low-risk groups. The overall disease-free survival rate was compared between the two groups. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used to identify the pathways and mechanisms involved in this model. Subsequently, immunotherapy response, sensitivity, and correlation analyses for several anti-tumor medications were performed. In vitro experiments, including qPCR, were conducted in nine lung adenocarcinoma cell lines and 16 pairs of lung adenocarcinoma and para-carcinoma tissues. Results: After confirmation using the ROC curve, patients in the low-risk category benefited from both overall and disease-free survival. Gene Ontology analysis highlighted cell movement in the model. In the in vitro experiments, qPCR results showed the expression levels of six CRs in 16 pairs of carcinoma and para-carcinoma tissues, which were in accordance with the results of the model. AL138778.1 is a protective factor that can weaken the invasion and migration of A549 cells, and AL360270.1 is a hazardous factor that promotes the invasion and migration of A549 cells. According to this model, targeted treatments such as axitinib, gefitinib, linsitinib, pazopanib, and sorafenib may be more appropriate for low-risk patients. Conclusion: Six CR profiles (AL360270.1, AL138778.1, CDKN2A-DT, AP003778.1, LINC02718, and AC034102.8) with predictive values may be used to evaluate the prognosis of patients with lung adenocarcinoma undergoing therapy.

Novaferon gene modification promotes NK92 cell anti-tumor activity

With significant developments in chimeric antigen receptor T-cell therapy, adoptive immunotherapy has unlocked new levels of treatment for malignancies. Natural killer (NK) cells are promising alternative immune effector cells for this strategy. Multiple anti-tumor therapies are largely dependent on type I interferon (IFN) signaling. Type I IFNs enhance NK cell cytotoxicity. Novaferon (nova) is an unnatural, novel IFN-like protein produced by gene shuffling of IFN-α with strong biological activity. To augment the antitumor activity of NK cells, we generated NK92-nova cells that stably express nova. We found that NK92-nova cells mediated enhanced pan-cancer antitumor activity compared to NK92-vec cells. The increased antitumor activity was associated with the enhanced secretion of cytokines, such as IFN-γ, perforin, and granzyme B. Meanwhile, most of the activating receptors were upregulated in the NK92-nova cells. After co-culture with NK92-nova cells, the expression of NKG2D ligands on the HepG2 cells increased, resulting in an enhanced susceptibility of HepG2 cells to NK92 cell-mediated cytolysis. NK92-nova cells significantly inhibited HepG2 tumor growth in a xenograft model without systemic toxicity. Therefore, NK92-nova cells are a novel and safe strategy for cancer immunotherapy.

Construction of a two-gene prognostic model related to ferroptosis in renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a common and aggressive tumor. A newly discovered form of programmed cell death, ferroptosis, plays an important role in tumor development and progression. However, a clear prognostic correlation between Ferroptosis-related genes (FRGs) and RCC has not yet been established. In this study, prognostic markers associated with FRGs were investigated to improve the therapeutic, diagnostic, and preventive strategies available to patients with renal cancer.

METHODS

The present study analyzed the predictive value of 23 FRGs in RCC through bioinformatics techniques, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) tools, Kaplan-Meier survival analysis, Cox regression modeling, tumor mutational burden (TMB), CIBERSORT, and half maximal inhibitory concentration (IC50) difference analysis.

RESULTS

We screened FRGs by differentially expressed genes (DEGs) and overall survival (OS). Four candidate genes were obtained by hybridization. Then, we constructed a two-gene prognostic signature (NCOA4 and CDKN1A) via univariate Cox regression and multivariate stepwise Cox regression, which classified RCC patients into high- and low-risk groups, and patients in the high-risk group were found to have worse OS and progression-free survival (PFS). We also found that patients with higher TNM stage, T stage, and M stage had higher risk scores than those with lower TNM stage, T stage, and M stage (P<0.05). Males had higher risk scores than females. This signature was identified as an independent prognostic indicator for RCC. These results were validated in both the test cohort and the entire cohort. In addition, we also constructed a nomogram that predicted the OS in RCC patients, the consistency index (C-index) of the nomogram was 0.731 [95% confidence interval (CI): 0.672-0.790], the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.728, 0.704, and 0.898 at 1-, 3-, and 5-year, respectively, which shows that nomogram has good prediction ability. and we also analyzed the immune status and drug sensitivity between the high- and low-risk groups.

CONCLUSIONS

We constructed a prognostic model associated with ferroptosis, which may provide clinicians with a reliable predictive assessment tool and offer new perspectives for the future clinical management of RCC.

Viral- and tumor-reactive natural killer cells

When we can understand what natural killer (NK) cells recognize during an encounter with an infectious pathogen or a tumor cell, and when we can understand how the NK cell responds to that encounter, we can then begin to understand the role of NK cells in human health and how to improve upon their role for the prevention and treatment of human disease. In the quest to understand how these cells function in antiviral and antitumoral immunity, there have been previously described mechanisms established for NK cells to participate in clearing viral infections and tumors, including classical NK cell antibody dependent cellular cytotoxicity (ADCC) as well as recognition and elimination of transformed malignant cells through direct ligand interactions. However, it is now clear that there are additional mechanisms by which NK cells can participate in these critical immune tasks. Here we review two recently described types of NK cell recognition and response: the first is to primary infection with herpes virus, recognized and responded to by non-specific Fc bridged cellular cytotoxicity (FcBCC), and the second describes a novel phenotypic and functional response when a subset of NK cells recognize myeloid leukemia.

Insights into the cellular pathophysiology of familial hemophagocytic lymphohistiocytosis

Familial hemophagocytic lymphohistiocytosis (fHLH) encompasses a group of rare inherited immune dysregulation disorders characterized by loss-of-function mutations in one of several genes involved in the assembly, exocytosis, and function of cytotoxic granules within CD8+ T cells and natural killer (NK) cells. The resulting defect in cytotoxicity allows these cells to be appropriately stimulated in response to an antigenic trigger, and also impairs their ability to effectively mediate and terminate the immune response. Consequently, there is sustained lymphocyte activation, resulting in the secretion of excessive amounts of pro-inflammatory cytokines that further activate other cells of the innate and adaptive immune systems. Together, these activated cells and pro-inflammatory cytokines mediate tissue damage that leads to multi-organ failure in the absence of treatment aimed at controlling hyperinflammation. In this article, we review these mechanisms of hyperinflammation in fHLH at the cellular level, focusing primarily on studies performed in murine models of fHLH that have provided insight into how defects in the lymphocyte cytotoxicity pathway mediate rampant and sustained immune dysregulation.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

Divulging a Pleiotropic Role of Succinate Receptor SUCNR1 in Renal Cell Carcinoma Microenvironment

The succinate receptor, SUCNR1, has been attributed to tumor progression, metastasis, and immune response modulation upon its activation via the oncometabolite succinate. Nonetheless, little is known about the prognostic relevance of SUCNR1 and its association with tumor immune infiltrates and microbiota in renal cell carcinoma (RCC). Herein, publicly available platforms including Human Protein Atlas, cBioPortal, TIMER2.0, and TISIDB were utilized to depict a divergent implication of SUCNR1 in the immune microenvironment of clear cell RCC (KIRC) and papillary RCC (KIRP); the two major subtypes of RCC. Our results showed that the SUCNR1 expression level was augmented in RCC compared to other solid cancers, yet with opposite survival rate predictions in RCC subtypes. Consequently, a higher expression level of SUCNR1 was associated with a good disease-specific survival rate (p = 5.797 × 10^-5) in KIRC patients albeit a poor prognostic prediction in KIRP patients (p = 1.9282 × 10^-3). Intriguingly, SUCNR1 was mainly correlated to immunomodulators and diverse immune infiltrates in KIRP. Additionally, the SUCNR1 was mostly associated with a repertoire of microbes including beneficial bacteria that likely influenced a better disease-specific survival rate in KIRC. Our findings illustrate a significant novel subtype-specific role of SUCNR1 in RCC which potentially modulates tumor immune infiltration and microbiome signature, hence altering the prognosis of cancer patients.

Prognostic Model for Clear-cell Renal Cell Carcinoma Based on Natural Killer Cell-related Genes

BACKGROUND

Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies.

METHODS

From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101). With the univariate Cox analysis and LASSO-Cox regression algorithm, a NKRPS was built to evaluate patients' prognosis. Receiver operating characteristic (ROC) curves and calibration curves were drawn to estimate the predictive power of the prognostic model. Then, tumor microenvironment (TME), tumor mutational burden (TMB), sensitization to immune checkpoint inhibitors (ICIs) therapy and targeted drug treatment were analyzed in ccRCC patients.

RESULTS

Nine genes (BID, CCL7, CSF2, IL23A, KNSTRN, RHBDD3, PIK3R3, RNF19B and VAV3) were identified to construct a NKRPS. High-risk group displayed undesirable survival compared to low-risk group (P < .05). Moreover, the area under the curve (AUC) of ROC at 1-, 3- and 5-year were 0.766, 0.755, and 0.757, respectively. High-risk group was characterized by superior immune infiltration, higher TMB, and higher expression of 5 ICI-related genes. Additionally, this model enabled to predict the sensitivity of patients to chemotherapy drugs.

CONCLUSION

NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate individualized treatment and medical decision making.

Beyond Cancer: Regulation and Function of PD-L1 in Health and Immune-Related Diseases

Programmed Cell Death 1 Ligand 1 (PD-L1, CD274, B7-H1) is a transmembrane protein which is strongly involved in immune modulation, serving as checkpoint regulator. Interaction with its receptor, Programmed Cell Death Protein 1 (PD-1), induces an immune-suppressive signal, which modulates the activity of T cells and other effector cells. This mediates peripheral tolerance and contributes to tumor immune escape. PD-L1 became famous due to its deployment in cancer therapy, where blockage of PD-L1 with the help of therapeutic antagonistic antibodies achieved impressive clinical responses by reactivating effector cell functions against tumor cells. Therefore, in the past, the focus has been placed on PD-L1 expression and its function in various malignant cells, whereas its role in healthy tissue and diseases apart from cancer remained largely neglected. In this review, we summarize the function of PD-L1 in non-cancerous cells, outlining its discovery and origin, as well as its involvement in different cellular and immune-related processes. We provide an overview of transcriptional and translational regulation, and expression patterns of PD-L1 in different cells and organs, and illuminate the involvement of PD-L1 in different autoimmune diseases as well as in the context of transplantation and pregnancy.

Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all kidney cancers and has a poor prognosis. Recent studies have shown that copper-dependent, regulated cell death differs from previously known death mechanisms (apoptosis, ferroptosis, and necroptosis) and is dependent on mitochondrial respiration (Tsvetkov et al., Science, 2022, 375 (6586), 1254–1261). Studies also suggested that targeting cuproptosis may be a novel therapeutic strategy for cancer therapy. In ccRCC, both cuproptosis and lncRNA were critical, but the mechanisms were not fully understood. The aim of our study was to construct a prognostic profile based on cuproptosis-associated lncRNAs to predict the prognosis of ccRCC and to study the immune profile of clear cell renal cell carcinoma (ccRCC).

Methods: We downloaded the transcriptional profile and clinical information of ccRCC from The Cancer Genome Atlas (TCGA). Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-associated lncRNAs and to construct a risk prognostic model. In addition, the predictive performance of the model was validated and recognized by an integrated approach. We then also constructed a nomogram to predict the prognosis of ccRCC patients. Differences in biological function were investigated by GO, KEGG, and immunoassay. Immunotherapy response was measured using tumor mutational burden (TMB) and tumor immune dysfunction and rejection (TIDE) scores.

Results: We constructed a panel of 10 cuproptosis-associated lncRNAs (HHLA3, H1-10-AS1, PICSAR, LINC02027, SNHG15, SNHG8, LINC00471, EIF1B-AS1, LINC02154, and MINCR) to construct a prognostic prediction model. The Kaplan–Meier and ROC curves showed that the feature had acceptable predictive validity in the TCGA training, test, and complete groups. The cuproptosis-associated lncRNA model had higher diagnostic efficiency compared to other clinical features. The analysis of Immune cell infiltration and ssGSEA further confirmed that predictive features were significantly associated with the immune status of ccRCC patients. Notably, the superimposed effect of patients in the high-risk group and high TMB resulted in shorter survival. In addition, the higher TIDE scores in the high-risk group suggested a poorer outcome for immune checkpoint blockade response in these patients.

Conclusion: The ten cuproptosis-related risk profiles for lncRNA may help assess the prognosis and molecular profile of ccRCC patients and improve treatment options, which can be further applied in the clinic.

What Happens to the Immune Microenvironment After PD-1 Inhibitor Therapy?

The fruitful results of tumor immunotherapy establish its indispensable status in the regulation of the tumorous immune context. It seems that the treatment of programmed cell death receptor 1 (PD-1) blockade is one of the most promising approaches for cancer control. The significant efficacy of PD-1 inhibitor therapy has been made in several cancer types, such as breast cancer, lung cancer, and multiple myeloma. Even so, the mechanisms of how anti-PD-1 therapy takes effect by impacting the immune microenvironment and how partial patients acquire the resistance to PD-1 blockade have yet to be studied. In this review, we discuss the cross talk between immune cells and how they promote PD-1 blockade efficacy. In addition, we also depict factors that may underlie tumor resistance to PD-1 blockade and feasible solutions in combination with it.

An immune-related lncRNA risk coefficient model to predict the outcomes in clear cell renal cell carcinoma

Objective: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs.

Methods: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups.

Results: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy.

Conclusion: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.

Neutrophil and Natural Killer Cell Interactions in Cancers: Dangerous Liaisons Instructing Immunosuppression and Angiogenesis

The tumor immune microenvironment (TIME) has largely been reported to cooperate on tumor onset and progression, as a consequence of the phenotype/functional plasticity and adaptation capabilities of tumor-infiltrating and tumor-associated immune cells. Immune cells within the tumor micro (tissue-local) and macro (peripheral blood) environment closely interact by cell-to-cell contact and/or via soluble factors, also generating a tumor-permissive soil. These dangerous liaisons have been investigated for pillars of tumor immunology, such as tumor associated macrophages and T cell subsets. Here, we reviewed and discussed the contribution of selected innate immunity effector cells, namely neutrophils and natural killer cells, as "soloists" or by their "dangerous liaisons", in favoring tumor progression by dissecting the cellular and molecular mechanisms involved.