Tumor-associated M2 macrophages in the immune microenvironment influence the progression of renal clear cell carcinoma by regulating M2 macrophage-associated genes

Comprehensive assessment of the association between tumor-infiltrating immune cells and the prognosis of renal cell carcinoma

BACKGROUND

According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC.

AIM

To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy.

METHODS

The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of P < 0.05 was regarded as statistically significant.

RESULTS

Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve (P = 1^E−05). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) via the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342–1.791; P < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC.

CONCLUSION

TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.

CTCF-activated FUCA1 functions as a tumor suppressor by promoting autophagy flux and serum α-L-fucosidase serves as a potential biomarker for prognosis in ccRCC

Notably, clear cell renal cell carcinoma (ccRCC) is characterized by a distinct metabolic tumor phenotype that involves the reprogramming of multiple metabolic pathways. Although there is increasing evidence linking FUCA1 to malignancies, its specific role and downstream signaling pathways in ccRCC remain poorly understood. Here we found that FUCA1 expression was significantly downregulated in ccRCC tissues, which also predicts poor prognosis of ccRCCpatients. Moreover, enhancing FUCA1 expression resulted in reduced invasion and migration of ccRCC cells, further indicating its protective role. CHIP-qPCR and luciferase assays showed that CTCF was an upstream transcription factor of FUCA1 and could reverse the effects caused by FUCA1 inactivation. The change in FUCA1 led to changes in the results of various autophagy-related proteins and the mRFP-GFP-LC3 dual fluorescence system, indicating that it may play a role in the fusion stage of autophagy. Protein-protein interaction analysis revealed that FUCA2 exhibited the closest interaction with FUCA1 and strongly predicted the prognosis of ccRCC patients. Additionally, serum AFU encoded by FUCA2 could serve as a valuable predictor for survival in ccRCC patients. FUCA1 suppresses invasion and migration of ccRCC cells, with its activity being modulated by CTCF. FUCA1 regulates the autophagy process in ccRCC cells by influencing the fusion between autophagosomes and lysosomes. FUCA2 shares similarities with FUCA1, and elevated serum AFU levels along with increased expression of FUCA2 are indicative of a favorable prognosis in ccRCC.

Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis

INTRODUCTION

IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis. Unfortunately, molecular biomarkers for IgAN derived from omics studies are still lacking. This research aims to identify critical genes associated with IgAN through large-scale blood transcriptome analysis.

METHODS

We constructed novel blood transcriptome profiles from peripheral blood mononuclear cells (PBMCs) of 53 Chinese IgAN patients and 28 healthy individuals. Our analysis included GO, KEGG, and GSEA for biological pathways. We analyzed immune cell profiles with CIBERSORT and constructed PPI networks with STRING, visualized in Cytoscape. Key differentially expressed genes (DEGs) were identified using CytoHubba and MCODE. We assessed the correlation between gene expressions and clinical data to evaluate clinical significance and identified hub genes through machine learning, validated with an open-access dataset. Potential drugs were explored using the CMap database.

RESULTS

We identified 333 DEGs between IgAN patients and healthy controls, mainly related to immune response and inflammation. Key pathways included NK cell mediated cytotoxicity, complement and coagulation cascades, antigen processing, and B cell receptor signaling. Cytoscape revealed 16 clinically significant genes (including KIR2DL1, KIR2DL3, VISIG4, C1QB, and C1QC, associated with sub-phenotype and prognosis). Machine learning identified two hub genes (KLRC1 and C1QB) for a diagnostic model of IgAN with 0.92 accuracy, validated at 1.00 against the GSE125818 dataset. Sirolimus, calcifediol, and efaproxiral were suggested as potential therapeutic agents.

CONCLUSION

Key DEGs, particularly VISIG4, KLRC1, and C1QB, emerge as potential specific markers for IgAN, paving the way for future targeted personalized treatment options.

Characterization of macrophages in head and neck squamous cell carcinoma and development of MRG-based risk signature

Macrophages are immune cells in the TME that can not only inhibit angiogenesis, extracellular matrix remodeling, cancer cell proliferation, and metastasis but also mediate the phagocytosis and killing of cancer cells after activation, making them key targets in anti-tumor immunotherapy. However, there is little research on macrophages and their relation to disease prognosis in HNSCC. Initially, we collected scRNA-seq, bulk RNA-seq, and clinical data. Subsequently, we identified macrophages and distinguished MRGs. Using the K-means algorithm, we performed consensus unsupervised clustering. Next, we used ssGSEA analysis to assess immune cell infiltration in MRG clusters. A risk model was established using multivariate Cox analysis. Then, Kaplan-Meier, ROC curves, univariate and multivariate COX analyses, and C-index was used to validate the predictive power of the signature. The TIDE method was applied to assess the response to immunotherapy in patients diagnosed with HNSCC. In addition, drug susceptibility predictions were made for the GDSC database using the calcPhenotype function. We found that 8 MRGs had prognostic potential. Patients in the MRG group A had a higher probability of survival, and MRG clusters A and B had different characteristics. Cluster A had a higher degree of expression and infiltration in MRG, indicating a closer relationship with MRG. The accuracy of the signature was validated using univariate and multivariate Cox analysis, C-index, and nomogram. Immune landscape analysis found that various immune functions were highly expressed in the low-risk group, indicating an improved response to immunotherapy. Finally, drugs with high sensitivity to HNSCC (such as 5-Fluorouracil, Temozolomide, Carmustine, and EPZ5676) were explored and analyze the malignant characteristics of HNSCC. We constructed a prognostic model using multivariate Cox analysis, consisting of 8 MRGs (TGM2, STC1, SH2D3C, PIK3R3, MAP3K8, ITGA5, ARHGAP4, and AQP1). Patients in the low-risk group may have a higher response to immunotherapy. The more prominent drugs for drug selection are 5-fluorouracil, temozolomide and so on. Malignant features associated with HNSCC include angiogenesis, EMT, and the cell cycle. This study has opened up new prospects for the prognosis, prediction, and clinical treatment strategy of HNSCC.

Pleiotropic physiological functions of Piezo1 in human body and its effect on malignant behavior of tumors

Mechanosensitive ion channel protein 1 (Piezo1) is a large homotrimeric membrane protein. Piezo1 has various effects and plays an important and irreplaceable role in the maintenance of human life activities and homeostasis of the internal environment. In addition, recent studies have shown that Piezo1 plays a vital role in tumorigenesis, progression, malignancy and clinical prognosis. Piezo1 is involved in regulating the malignant behaviors of a variety of tumors, including cellular metabolic reprogramming, unlimited proliferation, inhibition of apoptosis, maintenance of stemness, angiogenesis, invasion and metastasis. Moreover, Piezo1 regulates tumor progression by affecting the recruitment, activation, and differentiation of multiple immune cells. Therefore, Piezo1 has excellent potential as an anti-tumor target. The article reviews the diverse physiological functions of Piezo1 in the human body and its major cellular pathways during disease development, and describes in detail the specific mechanisms by which Piezo1 affects the malignant behavior of tumors and its recent progress as a new target for tumor therapy, providing new perspectives for exploring more potential effects on physiological functions and its application in tumor therapy.

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field is lagging behind other cancers. Despite increased detection of early disease as a consequence of the widespread use of diagnostic CT scans, 25% of patients have disseminated disease at diagnosis. Similarly, around 25% progress to metastatic disease following curatively intended surgery. Surgery is the cornerstone in the treatment of RCC; however, when the disease is disseminated, immunotherapy or immunotherapy in combination with a tyrosine kinase inhibitor is the patient's best option. Immunotherapy is a potent treatment, with durable treatment responses and potential to cure the patient, but only half of the patients benefit from the administered treatment, and there are currently no methods that can identify which patients will respond to immunotherapy. Moreover, there is a need to identify the patients in greatest risk of relapsing after surgery for localized disease and direct adjuvant treatment there. Even though several molecular biomarkers have been published to date, we are still lacking routinely used biomarkers to guide optimal clinical management. The purpose of this review is to highlight some of the most promising biomarkers, discuss the efforts made within this field to date, and describe the barriers needed to be overcome to have reliable and robust predictive and prognostic biomarkers in the clinic for renal cancer.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients' gene expression and clinical data through a variety of techniques to predict patients' outcomes, mechanistic models focus on investigating cells' and molecules' interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors' microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

Lymph node metastasis and tumor-educated immune tolerance: Potential therapeutic targets against distant metastasis

Lymph node metastasis has been shown to positively associated with the prognosis of many cancers. However, in clinical treatment, lymphadenectomy is not always successful, suggesting that immune cells in the tumor and sentinel lymph nodes still play a pivotal role in tumor immunosuppression. Recent studies had shown that tumors can tolerate immune cells through multiple strategies, including tumor-induced macrophage reprogramming, T cells inactivation, production of B cells pathogenic antibodies and activation of regulatory T cells to promote tumor colonization, growth, and metastasis in lymph nodes. We reviewed the bidirectional effect of immune cells on anti-tumor or promotion of cancer cell metastasis during lymph node metastasis, and the mechanisms by which malignant cancer cells modify immune cells to create a more favorable environment for the growth and survival of cancer cells. Research and treatment strategies focusing on the immune system in lymph nodes and potential immune targets in lymph node metastasis were also be discussed.