TIMP1 shapes an immunosuppressive microenvironment by regulating anoikis to promote the progression of clear cell renal cell carcinoma

Decoding resistance to immune checkpoint inhibitors in non-small cell lung cancer: a comprehensive analysis of plasma proteomics and therapeutic implications

BACKGROUND

Immune checkpoint inhibitors (ICIs) have shown substantial benefit for patients with advanced non-small cell lung cancer (NSCLC). However, resistance to ICIs remains a major clinical challenge. Here, we perform a comprehensive bioinformatic analysis of plasma proteomic profiles to explore the underlying biology of treatment resistance in NSCLC.

METHODS

The analysis was performed on 388 "resistance-associated proteins" (RAPs) that were previously described as pretreatment plasma proteomic predictors within the PROphet computational model designed to predict ICI clinical benefit in NSCLC. Putative tissue origins of the RAPs were explored using publicly available datasets. Enrichment analyses were performed to investigate RAP-related biological processes. Plasma proteomic data from 50 healthy subjects and 272 patients with NSCLC were compared, where patients were classified as displaying clinical benefit (CB; n=76) or no CB (NCB; n=196). Therapeutic agents targeting RAPs were identified in drug and clinical trial databases.

RESULTS

The RAP set was significantly enriched with proteins associated with lung cancer, liver tissue, cell proliferation, extracellular matrix, invasion, and metastasis. Comparison of RAP expression in healthy subjects and patients with NSCLC revealed five distinct RAP subsets that provide mechanistic insights. The RAP subset displaying a pattern of high expression in the healthy population relative to the NSCLC population included multiple proteins associated with antitumor activities, while the subset displaying a pattern of highest expression in the NCB population included proteins associated with various hallmarks of treatment resistance. Analysis of patient-specific RAP profiles revealed inter-patient diversity of potential resistance mechanisms, suggesting that RAPs may aid in developing personalized therapeutic strategies. Furthermore, examination of drug and clinical trial databases revealed that 17.5% of the RAPs are drug targets, highlighting the RAP set as a valuable resource for drug development.

CONCLUSIONS

The study provides insight into the underlying biology of ICI resistance in NSCLC and highlights the potential clinical value of RAP profiles for developing personalized therapies.

In silico research of coagulation- and fibrinolysis-related genes for predicting prognosis of clear cell renal cell carcinoma

Background

Coagulation- and fibrinolysis-related genes (CFRGs) are involved in tumor progression. However, their regulatory mechanisms in clear cell renal cell carcinoma (ccRCC) remain unclear. The aim of this study was to search for genes related to coagulation and fibrinolytic systems in ccRCC and to investigate their potential role in tumor pathogenesis and progression.

Methods

Differentially expressed genes (DEGs) between ccRCC and control samples, as well as key module genes associated with ccRCC, were extracted from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Differentially expressed CFRGs (DE-CFRGs) were identified by intersecting these DEGs with CFRGs. Prognostic genes were identified through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses of DE-CFRGs. Additional independent prognostic and enrichment analyses were conducted, and potential therapeutic drugs were predicted. In addition, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to validate the expression of prognostic genes.

Results

Sixteen DE-CFRGs were identified by intersecting 3,311 DEGs, 1,719 key module genes, and CFRGs. Four prognostic genes-TIMP1, RUNX1, BMP6, and PROS1-were found to be involved in complement and coagulation cascades and other functional pathways. The prognostic model demonstrated strong predictive power for ccRCC, with stage, risk score, and grade all correlating with prognosis. Additionally, 14 potential drugs, such as tamoxifen citrate and cytarabine, were predicted for therapeutic targeting of the identified prognostic genes. RT-qPCR confirmed that the expression levels of TIMP1, and RUNX1 were significantly upregulated in ccRCC samples, consistent with bioinformatics analysis.

Conclusions

A prognostic model incorporating TIMP1, RUNX1, BMP6, and PROS1 was constructed, offering new insights for prognostic evaluation and therapeutic strategies in ccRCC.

TIMP1 promotes thyroid cancer cell progression through macrophage phenotypic polarization via the PI3K/AKT signaling pathway

Increasing evidence suggests that tissue inhibitor of metalloproteinase 1 (TIMP1) played a pivotal role in immune regulation. Our study focused on examining the expression and function of TIMP1 in humans, particularly in its regulation of tumor-associated macrophages (TAMs) in papillary thyroid carcinoma (PTC). We observed an upregulation of TIMP1 in 16 different types of malignancies, including thyroid cancer. TIMP1 shaped the inflammatory TME in PTC. Inhibiting the expression of TIMP1 has been demonstrated to reduce the malignant biological traits of PTC cells. Furthermore, reducing TIMP1 expression impeded M2 macrophage polarization as well as facilitated M1 macrophage polarization in PTC. ELISA results demonstrated that downregulated TIMP1 expression correlated with decreased levels of IL10 and TGF-β in cell supernatants. Furthermore, the supernatant from polarized macrophages in the TIMP1-silenced group inhibited the motility of wild-type PTC cells. Therefore, TIMP1 may enhance the progression of PTC by stimulating the PI3K/AKT pathway via the secretion of IL10 and TGF-β, consequently influencing M2-type polarization in TAMs.

Identification and experimental verification of a biomarker by combining the unfolded protein response with the immune cells in colon cancer

BACKGROUND

The unfolded protein response (UPR) is associated with immune cells that regulate the biological behavior of tumors. This article aims to combine UPR-associated genes with immune cells to find a prognostic marker and to verify its connection to the UPR.

METHODS

Univariate cox analysis was used to screen prognostically relevant UPRs and further screened for key UPRs among them by machine learning. ssGSEA was used to calculate immune cell abundance. Univariate cox analysis was used to screen for prognostically relevant immune cells. Multivariate cox analysis was used to calculate UPR_score and Tumor Immune Microenvironment score (TIME_score). WGCNA was used to screen UPR-Immune-related (UI-related) genes. Consensus clustering analysis was used to classify patients into molecular subtype. Based on the UI-related genes, we classified colon adenocarcinoma (COAD) samples by cluster analysis. Single-cell analysis was used to analyze the role of UI-related genes. We detected the function of TIMP1 by cell counting and transwell. Immunoblotting was used to detect whether TIMP1 was regulated by key UPR genes.

RESULTS

Combined UPR-related genes and immune cells can determine the prognosis of COAD patients. Cluster analysis showed that UI-related genes were associated with clinical features of COAD. Single-cell analysis revealed that UI-related genes may act through stromal cells. We defined three key UI-related genes by machine learning algorithms. Finally, we found that TIMP1, regulated by key genes of UPR, promoted colon cancer proliferation and metastasis.

CONCLUSIONS

We found that TIMP1 was a prognostic marker and experimentally confirmed that TIMP1 was regulated by key genes of UPR.

Prognostic impact and immunotherapeutic implications of NETosis-related prognostic model in clear cell renal cell carcinoma

BACKGROUND

The ramifications of necroptosis on the prognostication of clear cell renal cell carcinoma (ccRCC) remain inadequately expounded.

METHODS

A prognostic model delineating the facets of necroptosis in ccRCC was constructed, employing a compendium of algorithms. External validation was effectuated using the E-MTAB-1980 dataset. The exploration of immune infiltration scores was undertaken through the exploitation of multiple algorithms. Single-cell RNA sequencing data were procured from the GSE171306 dataset. Real-time quantitative PCR (RT-qPCR) was engaged to scrutinize the differential expression of SLC25A37 across cancer and paracancer tissues, as well as diverse cell lines. Assessments of proliferative and metastatic alterations in 769-P and 786-O cells were accomplished through Cell Counting Kit-8 (CCK8) and wound healing assays.

RESULTS

The necroptosis-related signature (NRS) emerges as a discerning metric, delineating patients' immune attributes, tumor mutation burden, immunotherapy response, and drug susceptibility. Single-cell RNA sequencing analysis unveils the marked enrichment of SLC25A37 in tumor cells. Concurrently, RT-qPCR discloses the overexpression of SLC25A37 in both ccRCC tissues and cell lines. SLC25A37 knockdown mitigates the proliferative and metastatic propensities of 769-P and 786-O cells, as evidenced by CCK8 and wound healing assays.

CONCLUSION

The NRS assumes a pivotal role in ascertaining the prognosis, tumor mutation burden, immunotherapy response, drug susceptibility, and immune cell infiltration features of ccRCC patients. SLC25A37 emerges as a putative player in immunosuppressive microenvironments, thereby providing a prospective avenue for the design of innovative immunotherapeutic targets for ccRCC.

Deciphering anoikis resistance and identifying prognostic biomarkers in clear cell renal cell carcinoma epithelial cells

This study tackles the persistent prognostic and management challenges of clear cell renal cell carcinoma (ccRCC), despite advancements in multimodal therapies. Focusing on anoikis, a critical form of programmed cell death in tumor progression and metastasis, we investigated its resistance in cancer evolution. Using single-cell RNA sequencing from seven ccRCC patients, we assessed the impact of anoikis-related genes (ARGs) and identified differentially expressed genes (DEGs) in Anoikis-related epithelial subclusters (ARESs). Additionally, six ccRCC RNA microarray datasets from the GEO database were analyzed for robust DEGs. A novel risk prognostic model was developed through LASSO and multivariate Cox regression, validated using BEST, ULCAN, and RT-PCR. The study included functional enrichment, immune infiltration analysis in the tumor microenvironment (TME), and drug sensitivity assessments, leading to a predictive nomogram integrating clinical parameters. Results highlighted dynamic ARG expression patterns and enhanced intercellular interactions in ARESs, with significant KEGG pathway enrichment in MYC + Epithelial subclusters indicating enhanced anoikis resistance. Additionally, all ARESs were identified in the spatial context, and their locational relationships were explored. Three key prognostic genes-TIMP1, PECAM1, and CDKN1A-were identified, with the high-risk group showing greater immune infiltration and anoikis resistance, linked to poorer prognosis. This study offers a novel ccRCC risk signature, providing innovative approaches for patient management, prognosis, and personalized treatment.

Assessing the role of programmed cell death signatures and related gene TOP2A in progression and prognostic prediction of clear cell renal cell carcinoma

Kidney Clear Cell Carcinoma (KIRC), the predominant form of kidney cancer, exhibits a diverse therapeutic response to Immune Checkpoint Inhibitors (ICIs), highlighting the need for predictive models of ICI efficacy. Our study has constructed a prognostic model based on 13 types of Programmed Cell Death (PCD), which are intertwined with tumor progression and the immune microenvironment. Validated by analyses of comprehensive datasets, this model identifies seven key PCD genes that delineate two subtypes with distinct immune profiles and sensitivities to anti-PD-1 therapy. The high-PCD group demonstrates a more immune-suppressive environment, while the low-PCD group shows better responses to PD-1 treatment. In particular, TOP2A emerged as crucial, with its inhibition markedly reducing KIRC cell growth and mobility. These findings underscore the relevance of PCDs in predicting KIRC outcomes and immunotherapy response, with implications for enhancing clinical decision-making.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.