Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

RBPMS-AS1 sponges miR-19a-3p to restrain cervical cancer cells via enhancing PLCL1-mediated pyroptosis

Cervical cancer (CC) poses a threat to human health. Enhancing pyroptosis can prevent the proliferation and epithelial-mesenchymal transition (EMT) of tumor cells. This study aims to reveal the candidates that modulate pyroptosis in CC. Accordingly, the common microRNAs (miRNAs/miRs) that were sponged by RBPMS antisense RNA 1 (RBPMS-AS1) and could target Phospholipase C-Like 1 (PLCL1) were intersected. The expression of PBPMS-AS1/miR-19a-3p (candidate miRNA)/PLCL1 was predicted in cervical squamous cell carcinoma (CESC), by which the expression location of RBPMS-AS1 and the binding between RBPMS-AS1/PLCL1 and miR-19a-3p were analyzed. The targeting relationship between RBPMS-AS1/PLCL1 and miR-19a-3p was confirmed by dual-luciferase reporter assay. After the transfection, cell counting kit-8 assay, colony formation assay, quantitative reverse transcription PCR, and Western blot were implemented for cell viability and proliferation analysis as well as gene and protein expression quantification analysis. Based on the results, RBPMS-AS1 and PLCL1 were lowly expressed, yet miR-19a-3p was highly expressed in CESC. RBPMS-AS1 overexpression diminished the proliferation and expressions of N-cadherin, vimentin, and miR-19a-3p, yet enhanced those of E-cadherin, PLCL1, and pyroptosis-relevant proteins (inteleukin-1β, caspase-1, and gasdermin D N-terminal). However, the above RBPMS-AS1 overexpression-induced effects were counteracted in the presence of miR-19a-3p. There also existed a targeting relationship and negative interplay between PLCL1 and miR-19a-3p. In short, RBPMS-AS1 sponges miR-19a-3p and represses the growth and EMT of CC cells via enhancing PLCL1-mediated pyroptosis.

Research progress on the effect of pyroptosis on the occurrence, development, invasion and metastasis of colorectal cancer

Pyroptosis is a type of programmed cell death mediated by gasdermines (GSDMs). The N-terminal domain of GSDMs forms pores in the plasma membrane, causing cell membrane rupture and the release of cell contents, leading to an inflammatory response and mediating pyrodeath. Pyroptosis plays an important role in inflammatory diseases and malignant tumors. With the further study of pyroptosis, an increasing number of studies have shown that the pyroptosis pathway can regulate the tumor microenvironment and antitumor immunity of colorectal cancer and is closely related to the occurrence, development, treatment and prognosis of colorectal cancer. This review aimed to explore the molecular mechanism of pyroptosis and the role of pyroptosis in the occurrence, development, treatment and prognosis of colorectal cancer (CRC) and to provide ideas for the clinical diagnosis and treatment of CRC.

Comprehensive analysis of PPP4C's impact on prognosis, immune microenvironment, and immunotherapy response in lung adenocarcinoma using single-cell sequencing and multi-omics

Background

Elevated PPP4C expression has been associated with poor prognostic implications for patients suffering from lung adenocarcinoma (LUAD). The extent to which PPP4C affects immune cell infiltration in LUAD, as well as the importance of associated genes in clinical scenarios, still requires thorough investigation.

Methods

In our investigation, we leveraged both single-cell and comprehensive RNA sequencing data, sourced from LUAD patients, in our analysis. This study also integrated datasets of immune-related genes from InnateDB into the framework. Our expansive evaluation employed various analytical techniques; these included pinpointing differentially expressed genes, constructing WGCNA, implementing Cox proportional hazards models. We utilized these methods to investigate the gene expression profiles of PPP4C within the context of LUAD and to clarify its potential prognostic value for patients. Subsequent steps involved validating the observed enhancement of PPP4C expression in LUAD samples through a series of experimental approaches. The array comprised immunohistochemistry staining, Western blotting, quantitative PCR, and a collection of cell-based assays aimed at evaluating the influence of PPP4C on the proliferative and migratory activities of LUAD cells.

Results

In lung cancer, elevated expression levels of PPP4C were observed, correlating with poorer patient prognoses. Validation of increased PPP4C levels in LUAD specimens was achieved using immunohistochemical techniques. Experimental investigations have substantiated the role of PPP4C in facilitating cellular proliferation and migration in LUAD contexts. Furthermore, an association was identified between the expression of PPP4C and the infiltration of immune cells in these tumors. A prognostic framework, incorporating PPP4C and immune-related genes, was developed and recognized as an autonomous predictor of survival in individuals afflicted with LUAD. This prognostic tool has demonstrated considerable efficacy in forecasting patient survival and their response to immunotherapeutic interventions.

Conclusion

The involvement of PPP4C in LUAD is deeply intertwined with the tumor's immune microenvironment. PPP4C's over-expression is associated with negative clinical outcomes, promoting both tumor proliferation and spread. A prognostic framework based on PPP4C levels may effectively predict patient prognoses in LUAD, as well as the efficacy of immunotherapy strategy. This research sheds light on the mechanisms of immune interaction in LUAD and proposes a new strategy for treatment.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma

Background

The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells.

Methods

This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis.

Results

In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells.

Conclusion

The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Anoikis resistance regulates immune infiltration and drug sensitivity in clear-cell renal cell carcinoma: insights from multi omics, single cell analysis and in vitro experiment

Background

Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC).

Methods

Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC.

Results

ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy.

Conclusion

This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

GPX3-Mediated Oxidative Stress Affects Pyrimidine Metabolism Levels in Stomach Adenocarcinoma via the AMPK/mTOR Pathway

Background

Amino acid metabolism, including ATP production, nucleotide synthesis, and redox homeostatic processes, are associated with proliferation and differentiation of tumor cells. This study aimed to identify novel prognostic biomarkers and potential therapeutic targets of amino acid metabolism-related genes for stomach adenocarcinoma (STAD).

Methods

RNA sequencing transcriptome data in the TCGA-STAD (training set) and GTEx datasets (validation set) were used. The LIMMA R program enabled the differentially expressed amino acid metabolism-related genes (AAMRGs) to be found. A prognostic risk score model based on clinical phenotypic features was built using LASSO regression and step multi-Cox analyses. Gene set enrichment analysis (GSEA) was used to find potential molecular pathways associated with STAD. Hierarchical cluster analysis was used to evaluate pyrimidine metabolism. Cultured STAD cells assessed the proliferation of STAD and upregulation of GPX3 expression by CCK8 and flow cytometry. Transwell and wound healing assays assessed the impact of GPX3 on invasion and migration of STAD cells. Western blot and qRT-PCR were used to measure changes in pyrimidine metabolism-related markers and active molecules involved in the AMPK/mTOR signaling pathway.

Results

Three AAMRGs, DNMT1, F2R, and GPX3, could independently predict the course of STAD. Pyrimidine metabolism appeared to be significantly associated with these by GSEA and clustering analyses. Pyrimidine metabolism was negatively correlated with GPX3. Functional studies using an overexpressed GPX3 plasmid showed an enhanced migration and invasion of STAD cells as well as the expression of genes associated with pyrimidine metabolism and the AMPK/mTOR signaling pathway. By using a CAD siRNA, it was found that that GPX3 affected 5-fluorouracil resistance during de novo synthesis of pyrimidine through the CAD-UMPS signaling axis.

Conclusions

GPX3 which regulates the level of pyrimidine metabolism through the AMPK/mTOR pathway was found to be closely associated with STAD. Our findings demonstrate GPX3 is a reliable biomarker for the prognosis of amino acid metabolism and a probable target for STAD therapy.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Background

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).

Methods

We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.

Results

We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.

Conclusion

Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.