The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer

GRB14: A prognostic biomarker driving tumor progression in gastric cancer through the PI3K/AKT signaling pathway by interacting with COBLL1

Gastric cancer (GC) is a prevalent malignancy with a high incidence rate. Growth factor receptor-bound protein 14 (GRB14) is crucial in cell signal transduction and is associated with tumor growth, invasion, and metastasis. The aim of this study is to investigate the impact of GRB14 on GC growth and metastasis. GRB14 expression and prognosis in GC tissues were analyzed using bioinformatics. The GC cell lines, SGC-7901, MGC-803, BGC-823, and normal gastric epithelial cell line (GES-1) were used in this study. Cell viability, cycle progression, and apoptosis were assessed via CCK-8 and flow cytometry. The colony formation, transwell, and wound-healing assays were conducted to evaluate cell proliferation, invasion, and migration. Protein levels involved in the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway were analyzed by Western blot. GRB14 expression was significantly higher in GC tissues than adjacent healthy tissues, correlating with poor prognosis. GRB14 knockdown promoted apoptosis and inhibited cell growth, invasion, and migration, while its overexpression exhibited opposite effects. GRB14 directly interacted with cordon-bleu WH2 repeat protein like 1, facilitating PI3K/AKT signaling in GC cells. This study highlights GRB14's critical role in GC progression and suggests its potential as a therapeutic target.

A Pyroptosis-Related LncRNA Signature for Predicting Prognosis, Immune Features and Drug Sensitivity in Ovarian Cancer

Background

Multiple studies have suggested that lncRNAs and pyroptosis play important roles in ovarian cancer (OC). However, the function of pyroptosis-related lncRNAs (PRLs) in OC is not fully understood.

Methods

Clinical information and RNA-seq data of OC patients (n = 379) were collected from TCGA database. Pearson correlation analysis and univariate Cox analysis were performed to identify prognostic PRLs, respectively. LASSO-COX regression was utilized to construct a prognostic PRLs signature. Kaplan-Meier (K-M) curve analyses and receiver operating characteristics (ROC) were used to evaluate the prognostic prediction of the signature. The association between risk score and tumor microenvironment infiltration, immunotherapy response and chemotherapy sensitivity were also analyzed. In addition, the function of TYMSOS on OC and pyroptosis was experimentally confirmed in cell lines.

Results

Firstly, 32 prognostic PRLs were identified, and a novel prognostic PRLs signature was constructed and validated. Surprisingly, the prognostic PRLs signature could solidly predict the clinical outcome of patients with OC and patients with high-risk score shown a short overall survival. GSEA results suggested that the RPLs were mainly enriched in the inflammatory response pathway, p53 pathway, TGF-β signaling and TNFα signaling. Besides, our results demonstrated that the risk score was significantly associated with patients with immune infiltration, immunotherapy response and the sensitivity of veliparib and metformin. Furthermore, the oncogene effect of TYMSOS on OC by inhibiting pyroptosis was verified by experiments.

Conclusion

This study found that the prognostic PRLs signature may serve as an efficient biomarker in predicting the prognosis, tumor microenvironment infiltration, and sensitivity of chemotherapeutic agents. TYMSOS is a potential biomarker in OC, and it might promote tumor progression by inhibiting pyroptosis.

MARCH5 Promotes the Progression of Thyroid cancer by Regulating Mitochondrial Autophagy Protein FUNDC1-mediated Pyroptosis

MARCH5 is a key regulatory factor in mitochondria. However, the expression and function of MARCH5 in thyroid cancer (TC) are not yet clear. The research explores the role and the potential mechanism of MARCH5 in the tumorigenesis of TC. MARCH5 expression were measured by qRT-PCR and Western blot. CCK-8 kits were used to measure the cell viability. Cell scratch assay and Tanswell assay were used to measure cell migration and invasion, respectively. The pyroptosis related proteins (NLRP3, caspase-1, GSDMD) and mitochondrial autophagy related proteins (LC3-II, p62, parkin, pink1) were detected. The mitochondrial ROS GSH, MDA, and SOD were detected using commercial kits. Finally, a TC mouse model was constructed to detect the role of MARCH5 in tumor growth in vivo. The results displayed that the expression of MARCH5 was increased in TC patients and cells, and was significantly correlated with prognosis. Functional studies have found that MARCH5 inhibits oxidative stress levels and mitochondrial autophagy in TPC-1 cells. Further research has found that MARCH5 promotes the progression of thyroid cancer by degrading FUNDC1 and inhibiting the mitochondrial autophagy mediated pyroptosis pathway, regulating cell proliferation, migration, and invasion in TPC-1 cells. More importantly, interference with MARCH5 inhibits tumor growth and further development of TC in vivo. In conclusion, MARCH5 promotes the progression of thyroid cancer by degrading FUNDC1 and inhibiting the mitochondrial autophagy mediated pyroptosis, regulating cell proliferation, migration, and invasion. This study provides new theoretical basis for the treatment and prevention of TC in clinical practice.

The Effect of Cuproptosis-Related Proteins on Macrophage Polarization in Mesothelioma is Revealed by scRNA-seq

High invasiveness mesothelioma is a malignant tumor of the peritoneum or pleura. The effect of cuproptosis on mesothelioma (MESO) is still unknown, though. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets were used to identify differential genes linked to cuproptosis in mesothelioma. Multigene features were then created to assess the course of the disease. Use single-cell data and in vitro validation to uncover crucial gene regulation mechanisms. In MESO, we found nine differentially expressed genes linked to cuproptosis. Using univariate Cox and LASSO regression techniques, a 3-gene feature (P < 0.05) was created, showing a good predictive potential for survival time. According to the risk score, patients in the low-risk subset had a considerably greater survival rate than those in the high-risk subset (P = 0). The similar survival pattern and prediction performance are also seen in the validation queue. The findings of the drug sensitivity research indicate that in high-risk patients, vinblastine, paclitaxel, gefitinib, and erlotinib are sensitive medications (P < 0.05). Classical monocytes were identified as core cells connected to cuproptosis by the CellChat results. SLC31A1 is implicated in the positive regulation of M2 macrophage polarization, according to cell subtype analysis and in vitro confirmation. Genes linked to cuproptosis have a major influence on tumor immunity and can predict how MESO will progress.

Caspase-7 and Vitamin D Receptor Gene as Key Genes of Hypertension Caused by Pyroptosis in Human

This study aims to elucidate the underlying mechanisms of pyroptosis in hypertension through bioinformatics and machine learning approaches. R language was utilized to integrate differentially expressed genes (DEGs) between hypertension samples and healthy control samples in GSE24752 and GSE75360 datasets, followed by GO analysis, KEGG enrichment analysis, and GSEA. Key genes were screened based on the expression levels of DEGs using logistic regression, LASSO regression, and support vector machine (SVM). A visualized protein-protein interaction regulatory network was constructed, and immune cell infiltration analysis was performed on integrated GEO datasets of hypertensive samples. Collect serum samples from hypertensive subjects and healthy control subjects for RT-qPCR detection of key gene expression. A total of 1005 DEGs were obtained from peripheral blood samples of 13 hypertension cases and 14 control samples. GO analysis, KEGG enrichment analysis, and GSEA revealed that the DEGs function synergistically in various biological pathways. LASSO regression and SVM identified six key genes related to pyroptosis (CASP7 (caspase-7), CYBB, NEK7, NLRP2, RAB5A, VDR (vitamin D receptor)). Immune infiltration analysis showed that activated B cell, effector memory CD8 T cell, immature B cell, MDSC, and T follicular helper cell accounted for the largest proportion of immune cells. RT-qPCR results indicated significantly higher relative expression levels of caspase-7 and vitamin D receptor in hypertensive samples compared to controls. These findings suggest that CASP7 and the vitamin D receptor gene may offer new research targets for the diagnosis and treatment of hypertension, and they also provide fresh evidence for the involvement of pyroptosis in hypertension.

Identification of a pyroptosis-related gene prognostic signature in patients with hepatocellular carcinoma

Background

Pyroptosis has been recently identified as a hallmark of cancer biology; however, the potential of pyroptosis-related genes (PRGs) as prognostic markers has not been fully elucidated in hepatocellular carcinoma (HCC). The aim of this study was to develop a PRG-associated risk signature for prediction prognosis in patients with HCC.

Methods

We identified 35 PRGs from the published literature, and pyroptosis subtypes were identified through bioinformatics methods. The risk score model was established by applying least absolute shrinkage and selection operator (LASSO) Cox regression method in The Cancer Genome Atlas (TCGA) cohort and validated in International Cancer Genome Consortium (ICGC) datasets. Additionally, immune infiltration, enriched pathways, and genomic alterations were compared between the high- and low-risk score subgroups. Finally, a nomogram containing the pyroptosis risk score and other prognosis-related clinical factors was developed for predicting the overall survival of patients with HCC.

Results

Based on the expression profile of PRGs, we determined two pyroptosis-related subtypes (cluster A and cluster B) of HCC associated with different immune characteristics and significantly different prognoses. The risk score model showed that upregulation of GPX4, CASP8, NOD2, and GSDME was associated with poor overall survival (OS), while high expression of NLRP6 was associated with good prognosis. Compared with group with a lower risk score, the group with a high risk score had worse prognosis (P<0.001) and a high level of immune cell infiltration. Functional analysis indicated that the highly expressed genes in the high-risk group were mainly enriched in various signaling pathways, while the genes with low expression in the high-risk group were mainly enriched in different biochemical metabolic translations. Genomic alterations in high-risk and low-risk populations suggested that mutations in the TP53 gene are highly associated with pyroptosis in patients with HCC. A nomogram including risk score and TNM stage demonstrated good prognostic ability in predicting 1-year, 3-year, and 5-year OS.

Conclusions

We developed and verified a prognostic risk model based on PRGs for patients with HCC, which may provide a robust tool for predicting outcomes in this setting.

Leveraging programmed cell death patterns to predict prognosis and therapeutic sensitivity in OSCC

OBJECTIVES

Intricate associations between programmed cell death (PCD) and cancer development and treatment outcomes have been increasingly appreciated. Here, we integrated 12 PCD patterns to construct a novel biomarker, cell death index (CDI), for oral squamous cell carcinoma (OSCC) prognostication and therapeutic prediction.

MATERIALS AND METHODS

Univariate Cox regression, Kaplan-Meier survival, and LASSO analyses were performed to construct the CDI. A nomogram combining CDI and selected clinicopathological parameters was established by multivariate Cox regression. The associations between CDI and immune landscape and therapeutic sensitivity were estimated. Single-cell RNA-seq data of OSCC was used to infer CDI genes in selected cell types and determine their expression along cell differentiation trajectory.

RESULTS

Ten selected PCD genes derived a novel prognostic signature for OSCC. The predictive prognostic performance of CDI and nomogram was robust and superior across multiple independent patient cohorts. CDI was negatively associated with tumor-infiltrating immune cell abundance and immunotherapeutic outcomes. Moreover, scRNA-seq data reanalysis revealed that GSDMB, IL-1A, PRKAA2, and SFRP1 from this signature were primarily expressed in cancer cells and involved in cell differentiation.

CONCLUSIONS

Our findings established CDI as a novel powerful predictor for prognosis and therapeutic response for OSCC and suggested its potential involvement in cancer cell differentiation.

Biological characteristics, immune infiltration and drug prediction of PANoptosis related genes and possible regulatory mechanisms in inflammatory bowel disease

PANoptosis is one of several modes of programmed cell death (PCD) and plays an important role in many inflammatory and immune diseases. The role of PANoptosis in inflammatory bowel disease (IBD) is currently unknown. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified, and pathway enrichment analyses were performed. LASSO regression model construction, a nomogram model, calibration curves, ROC and DCA curves were used to evaluate the predictive value of the model. Predicts transcription factors (TFs) and small-molecule drugs of DE-PRGs were analysed. Model genes and immuno-infiltration were analysed. The PANoptosis features of IBD include 12 genes: OGT, TLR2, GZMB, TLR4, PPIF, YBX3, CASP5, BCL2L1, CASP6, MEFV, GSDMB and BAX. The enrichment analysis suggested that these genes were related to TNF signalling, NF-κB, pyroptosis and necroptosis. Machine learning identified three model genes: OGT, GZMB and CASP5. The nomogram model, calibration curves, ROC and DCA curves have strong predictive value. Immuno-infiltration analysis revealed that immune cell infiltration was increased in patients with IBD, and the model genes were closely related to the infiltration of various immune cells. The TFs associated with DE-PRGs were RELA, NFKB1, HIF1A, TP53 and SP1. In addition, the Connectivity Map (CMap) database identified the top 10 small-molecule compounds, including buspirone, chloroquine, spectinomycin and chlortetracycline. This study indicate that DE-PRGs model genes have good predictive ability for IBD. Moreover, PANoptosis may mediate the process of IBD through TNF signalling, NF-κB, pyroptosis, necroptosis and immune mechanisms. These results present a new horizon for the research and treatment of IBD.

Identification of a pyroptosis-related prognostic model for colorectal cancer and validation of the core gene SPTBN5

BACKGROUND

Pyroptosis, an emerging type of programmed cell death. The mechanisms of pyroptosis mainly include inflammasome-activated pyroptosis and non-inflammasome-activated pyroptosis. Multiple prognostic scoring systems that utilize pyroptosis-related gene expression have been validated as effective predictors of patient outcomes. But the relationship between pyroptosis and colorectal cancer remains unclear. This study has established a gene signature associated with pyroptosis to forecast the prognosis of CRC patients.

METHODS

An analysis of 52 pyroptosis genes was conducted in both CRC and normal colorectal tissues, leading to the discovery of differentially expressed genes (DEGs). Core pyroptosis-related genes were identified using least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic risk score (PRS) for predicting CRC patient outcomes. The TCGA cohort was split into high-risk and low-risk groups based on the PRS, followed by Gene Ontology (GO) and KEGG pathway analyses. Additionally, differences in the enrichment scores of 16 immune cell types and the activity of 13 immune-related pathways were compared. The role of SPTBN5, a core pyroptosis-related gene, was validated through functional experiments on human colorectal adenocarcinoma cells (SW480).

RESULTS

40 differentially expressed genes were identified from 52 pyroptosis genes. A risk model was subsequently developed using 25 core pyroptosis-related genes identified through LASSO Cox regression analysis, and this model was validated in GEO cohorts. GO and KEGG pathway analyses showed that the DEGs are predominantly associated with mineral absorption, thyroid hormone synthesis, and pancreatic secretion. Functional experiments demonstrated that down-regulation of SPTBN5 expression through transfection led to significant decreases in the proliferation, migration, and clonogenicity of SW480 cells.

CONCLUSION

The PRS can identify high-risk CRC patient groups and predict patient prognosis. SPTBN5 may present a potential therapeutic target for CRC.

Butyrate Metabolism-Related Gene Signature in Tumor Immune Microenvironment in Lung Adenocarcinoma: A Comprehensive Bioinformatics Study

BACKGROUND

Experimental results have verified the suppressive impact of butyrate on tumor formation. Nevertheless, there is a limited understanding of the hidden function of butyrate metabolism within the tumor immune microenvironment (TIME) of lung adenocarcinoma (LUAD). This research aimed at digging the association between genes related to butyrate metabolism (butyrate metabolism-related genes [BMRGs) and immune infiltrates in LUAD patients.

METHODS

Through analyzing The Cancer Genome Atlas dataset (TCGA), the identification of 38 differentially expressed BMRGs was made between LUAD and normal samples. Later, a prognostic signature made up of nine BMRGs was made to evaluate the risk score of LUAD subjects. Notably, high-risk scores emerged as negative prognostic indicators for overall survival in LUAD subjects. Additionally, BMRGs displayed associations with immunocyte infiltration levels, immune pathway activities, and pivotal prognostic hub BMRGs.

RESULTS

One key prognostic BMRG, PTGDS, exhibited a robust correlation with T cells, the chemokine-related pathway, and the TCR signaling pathway. This study suggests that investigating the interplay between butyrate metabolism and T cells could present a promising novel approach to cancer treatment. OncoPredict analysis further unveiled distinct sensitivities of nine medicine in high- and low-risk groups, facilitating the selection of optimal treatment strategies for individual LUAD patients.

CONCLUSIONS

The study establishes that the BMRG signature serves as a sensitive predictive biomarker, providing profound insights into the crucial effect of butyrate metabolism in the context of LUAD TIME.

Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study

BACKGROUND

Breast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has the highest incidence among female malignant tumors. The metastasis of BC occurs through direct and lymphatic spread. Although ocular metastasis is relatively rare, it is a good indicator of a worse prognosis. We used machine learning (ML) to establish a model to analyze the risk factors of BC eye metastasis.

METHODS

The clinical data of 2225 patients with BC from 2003 to 2019 were collected and randomly classified into the training and test sets using a ratio of 7:3. Based on the presence or absence of eye metastasis, the patients with BC were classified into the ocular metastasis (OM) and non-ocular metastasis (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) were conducted. We used six ML algorithms to establish a predictive BC model and used 10-fold cross-validation for internal verification. The area under the receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. In addition, we established a web hazard calculator depending on the best-performing model to facilitate its clinical application. Shapley additive interpretation (SHAP) was used to determine the risk factors and the interpretability of the black box model.

RESULTS

Univariate logistic regression analysis showed that histopathology (other types), axillary lymph node metastasis (ALNM) (> 4), Ca2+, total cholesterol (TC), low-density lipoprotein (LDL), apolipoprotein A (ApoA), carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, CA199, alkaline phosphatase (ALP), and hemoglobin (Hb) were risk factors for BC eye metastasis. Multivariate logistic regression analysis showed that CA153, ApoA, and LDL were hazardous components for BC eye metastasis. LASSO showed that ALNM, LDL, CA125, Hb, ALP, and CA199 were the first six key variables that were useful for the diagnosis of ocular metastasis in breast cancer. Bootstrapped aggregation (BAG) demonstrated the discriminative ability (area under ROC curve [AUC] = 0.992, accuracy = 0.953, sensitivity = 0.987). Based on this, we applied the BAG machine learning model to build an online web computing system to help clinicians assist in determining the risk of BC eye metastasis. In addition, two typical cases are analyzed to determine the interpretability of the model.

CONCLUSION

We used ML to establish a risk prediction model for BC ocular metastasis, and BAG showed the greatest performance. The model can predict the risk of OM in patients with BC, facilitate early and timely diagnosis and treatment, and reduce the burden on society.

Pyroptosis-related lncRNAs are potential biomarkers for predicting prognoses and immune landscapes in patients with gastric adenocarcinoma

OBJECTIVES

The purpose of this study was to investigate the significance of pyroptosis-related lncRNAs (PRlncRNA) in predicting prognoses and immune landscapes of patients with gastric adenocarcinoma (STAD).

METHODS

Transcriptomic data and clinicopathological data were obtained from The Cancer Genome Atlas database. Based on correlation analysis and univariate Cox regression, prognostic PRlncRNA were identified. Subsequently, a PRlncRNA prognostic signature (PRLPS) was generated via least absolute shrinkage and selection operator (LASSO) regression, Kaplan-Meier method, receiver operating characteristic (ROC) curves, principal component analysis, and univariate and multivariate regression. Besides, the clinicopathological characteristics, tumor microenvironment (TME) scores, the immune landscapes in different risk subgroups were explored. Moreover, based on three PRlncRNA, we constructed a competing endogenous RNA (ceRNA) network. Additionally, Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) and Gene Ontology (GO) analysis were performed for biological functional analysis based on the difference between high- and low- risk groups, which also used to screen out potential STAD drugs.

RESULTS

21 PRlncRNA made up the prognostic signature, which had significant value in predicting the overall survival (OS), clinicopathological features, TME, immune checkpoint genes expression, and the response to immune checkpoint inhibitors of patients with STAD. In a addition, we constructed a ceRNA network comprising 3 PRlncRNAs and 69 mRNAs. The function of PRlncRNA was related to cancer-associated pathways. Ten small molecular drugs that might improve the prognosis of patients were screened out by connectivity maps.

CONCLUSIONS

Using PRlncRNA as a prognostic indicator for STAD, we identified predictive biomarkers and immunotherapy targets while refreshing our understanding.

In-depth study of pyroptosis-related genes and immune infiltration in colon cancer

Background

Pyroptosis is a form of regulated necrosis that occurs in many cell and tissue types and plays a critical role in tumor progression. The diagnostic value of pyroptosis-related genes (PRGs) in colon cancer has been widely investigated. In the present study, we explored the relationship between PRG expression and colon cancer.

Methods

We retrieved genomic and clinical data pertaining to The Cancer Genome Atlas-Colon Adenocarcinoma from the UCSC Xena database, along with the corresponding genome annotation information from the GENCODE data portal. Utilising these data and a list of 33 pyrogenic genes, we performed principal component analysis and unsupervised clustering analysis to assess the pyroptosis subtypes. We analysed the differential expression between these subtypes to obtain PRGs, ultimately selecting 10 PRGs. We conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set variation analysis, protein-protein interaction, and immune infiltration analyses of these PRGs. We validated the expression of TNNC1 via immunohistochemistry (IHC) and real-time quantitative PCR.

Results

After rigorous screening, excluding patients with incomplete survival data and unmatched transcriptomes, we refined our study cohort to 431 patients. We performed differential mRNA analysis and identified 445 PRGs, 10 of which were selected as hub genes. These genes were associated with various immune cell types. Specifically, TNNC1 expression was positively associated with immature dendritic cells and NK CD56+ cells. IHC staining indicated higher TNNC1 expression levels in tumor samples. Notably, TNNC1 expression levels were high in all the colon cancer cell lines, particularly in SW480 cells.

Conclusion

In this study, we explored the characteristics of PRGs in colon cancer and identified novel biological targets for early individualised treatment and accurate diagnosis of colon cancer, thus contributing to the advancement of clinical oncology.

Comprehensive analysis of lactylation-related gene sets and mitochondrial functions in gastric adenocarcinoma: implications for prognosis and therapeutic strategies

Gastric adenocarcinoma (STAD) is characterized by high heterogeneity and aggressiveness, leading to poor prognostic outcomes worldwide. This study explored the prognostic significance of lactylation-related gene sets and mitochondrial functions in STAD by integrating large-scale genomic datasets, including TCGA and several GEO datasets. We utilized Spatial transcriptomics and single-cell RNA sequencing to delineate the tumor microenvironment and assess the heterogeneity of cellular responses within the tumor. Additionally, the study identified distinct molecular subtypes within STAD that correspond with unique survival outcomes and immune profiles, enhancing the molecular classification beyond current paradigms. Prognostic models incorporating these molecular markers demonstrated superior predictive capabilities over existing models across multiple validation datasets. Furthermore, our analysis of immune landscapes revealed that variations in lactylation could influence immune cell infiltration and responsiveness, pointing towards novel avenues for tailored immunotherapy approaches. These comprehensive insights provide a foundation for targeted therapeutic strategies and underscore the potential of metabolic and immune modulation in improving STAD treatment outcomes.

Identification of a prognostic disulfidptosis-related gene signature in hepatocellular cancer

Background

Disulfidptosis regulate various biological processes in cancer. However, there is limited research on the genes related to disulfidptosis in predicting the prognosis of hepatocellular carcinoma (HCC). We aimed to develop a reliable disulfidptosis-related gene signature, which will characterize different HCC subtypes and predict their prognosis.

Methods

The Cancer Genome Atlas (TCGA)-HCC dataset, comprising RNA sequencing data and clinical information, was obtained from the TCGA database. The crucial disulfidptosis-related genes were selected for bioinformatic analysis in HCC. HCC tumor classification was established through a consistent cluster analysis. The prognosis and immune-cell infiltration were investigated in association with a disulfidptosis-related HCC model.

Results

In TCGA-HCC patients, a total of 3,621 prognostic genes and 30 key prognostic disulfidptosis-related genes were identified. Using key prognostic disulfidptosis-related genes, TCGA-HCC patients were categorized into low- and high-risk clusters. The upregulated differentially expressed genes (DEGs) in high-risk cluster 1 (C1) could significantly impact cell cycle, DNA replication, and the p53 signaling pathway, whereas the pathways associated with the downregulated DEGs in high-risk C1 could significantly impact metabolism of xenobiotics by cytochrome P450, the PPAR signaling pathway, and tyrosine metabolism. Furthermore, the immune activity of the high-risk C1 group was different to that of the low-risk cluster 2 (C2) group. The 13 disulfidptosis-related genes were finally screened using least absolute shrinkage and selection operator (LASSO) regression analysis, including ANP32E, BOP1, RPN1, SLC7A11, PPIH, PCBP2, ME1, PRDX1, FLNC, INF2, MYH11, LRPPRC, and HNRNPM.

Conclusions

The genes related to disulfidptosis are closely associated with tumor classification and immunity in patients with HCC. This is the first gene signature related to disulfidptosis demonstrated a strong predictive performance for the prognosis of HCC, which provide new perspectives for the diagnosis and treatment of HCC.

The role of microRNAs in the gastric cancer tumor microenvironment

BACKGROUND

Gastric cancer (GC) is one of the deadliest malignant tumors with unknown pathogenesis. Due to its treatment resistance, high recurrence rate, and lack of reliable early detection techniques, a majority of patients have a poor prognosis. Therefore, identifying new tumor biomarkers and therapeutic targets is essential. This review aims to provide fresh insights into enhancing the prognosis of patients with GC by summarizing the processes through which microRNAs (miRNAs) regulate the tumor microenvironment (TME) and highlighting their critical role in the TME.

MAIN TEXT

A comprehensive literature review was conducted by focusing on the interactions among tumor cells, extracellular matrix, blood vessels, cancer-associated fibroblasts, and immune cells within the GC TME. The role of noncoding RNAs, known as miRNAs, in modulating the TME through various signaling pathways, cytokines, growth factors, and exosomes was specifically examined. Tumor formation, metastasis, and therapy in GC are significantly influenced by interactions within the TME. miRNAs regulate tumor progression by modulating these interactions through multiple signaling pathways, cytokines, growth factors, and exosomes. Dysregulation of miRNAs affects critical cellular processes such as cell proliferation, differentiation, angiogenesis, metastasis, and treatment resistance, contributing to the pathogenesis of GC.

CONCLUSIONS

miRNAs play a crucial role in the regulation of the GC TME, influencing tumor progression and patient prognosis. By understanding the mechanisms through which miRNAs control the TME, potential biomarkers and therapeutic targets can be identified to improve the prognosis of patients with GC.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma

BACKGROUND

Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies.

OBJECTIVE

We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells.

METHODS

The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells.

RESULTS

A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1.

CONCLUSION

The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma

BACKGROUND

Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer.

METHODS

Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated.

RESULTS

A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort.

CONCLUSIONS

This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

Prognostic value and therapeutic potential of NEK family in stomach adenocarcinoma

Never in mitosis gene A-related kinase (NEK) is an 11-membered family of serine/threonine kinases (NEK1-NEK11), which are known to play important roles in the formation and development of cancer. However, few studies have examined the roles of these kinases in the development of stomach adenocarcinoma (STAD). In this study, we conducted a comprehensive analysis of the relationships between the NEKs family members and STAD. The differential expression of the NEK genes in STAD was validated using The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMER) databases, and their prognostic and diagnostic values of NEKs in STAD were assessed using the Kaplan-Meier plotter and TCGA data. The effect of NEK expression on immune cell infiltration in STAD was analysed using the TIMER and TISIDB databases. The expression levels of the majority of the NEK family members were consistently upregulated in STAD, whereas that of NEK10 was downregulated. The upregulation of NEK2/3/4/5/6/8 was closely associated with clinicopathological parameters of patients, and the overexpressed levels of these proteins had good diagnostic value for the disease. NEK1/8/9/10/11 expression correlated with poor overall survival and post-progressive survival, whereas a higher NEK1/6/9/11 level implied worse first progressive survival. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the NEKs may be related to immunological responses. Additionally, our study confirmed that these kinases correlated with immune cell infiltration and different immune infiltration subtypes in STAD. Our results suggest that NEK9 in particular has the potential to be used as a diagnostic and prognostic biomarker of STAD development and progression and an immune target for treatment of the disease. These findings expand our understanding of the biological functions of the NEK family members in STAD.

Prognostic model revealing pyroptosis-related signatures in oral squamous cell carcinoma based on bioinformatics analysis

One of the most common oral carcinomas is oral squamous cell carcinoma (OSCC), bringing a heavy burden to global health. Although progresses have been made in the intervention of OSCC, 5 years survival of patients suffering from OSCC is poor like before regarding to the high invasiveness of OSCC, which causes metastasis and recurrence of the tumor. The relationship between pyroptosis and OSCC remains to be further investigated as pyroptosis in carcinomas has gained much attention. Herein, the key pyroptosis-related genes were identified according to The Cancer Genome Atlas (TCGA) dataset. Additionally, a prognostic model was constructed based upon three key genes (CTLA4, CD5, and IL12RB2) through least absolute shrinkage and selection operator (LASSO) analyses, as well as univariate and multivariate COX regression in OSCC. It was discovered that the high expression of these three genes was associated with the low-risk group. We also identified LAIR2 as a hub gene, whose expression negatively correlated with the risk score and the different immune cell infiltration. Finally, we proved that these three genes were independent prognostic factors linked to overall survival (OS), and reliable consequences could be predicted by this model. Our study revealed the relationship between pyroptosis and OSCC, providing insights into new treatment targets for preventing and treating OSCC.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Hypoxia-based critical gene biomarkers as prognostic reporters for gastric adenocarcinoma

BACKGROUND

Gastric cancer is the most common malignant tumour of the digestive system, yet there is a lack of reported prognostic biomarkers for STAD patients.

METHODS

Transcriptomic expression data of STAD from GEO database, single cell sequencing data from OMIX gastric cancer database. Conservative molecular typing of gastric cancer was constructed using non-negative matrix factorization (NMF). The abundance of 28 immune cells in the tumour samples was assessed using ssGSEA. The R package "oncoPredict" was used to predict chemotherapy response. TIDE website for immunotherapy response prediction. Finally, single cell analysis was performed to clarify the specific type annotation of STAD cells and to analysis their spatial expression.

RESULTS

Hypoxia-score demonstrated excellent prognostic discrimination in TCGA gastric cancer samples. Among multiple deconvolution-based algorithms for immune infiltration, Hypoxia-score presented a general immunosuppressive efficacy across multiple datasets, as evidenced by a broad negative correlation with immune cell infiltration. By the likelihood that each group may have specific drug sensitivity to multiple chemotherapeutic and targeted agents. Results showed that high-risk scoring patients were more sensitive to Staurosporine, Sabutoclax, and AZD8055, while low-risk patients were more sensitive to Bortezomib, Dactinomycin, Docetaxel, Daporinad, Sepantronium, and bromide. In the immunotherapy cohort, the Hypoxia-score presented the ability to discriminate for immunotherapy efficacy. The distribution of Hypoxia-score in single-cell descending space was calculated using AddModuleScore and was found to be distributed across the various cell types annotated in the single-cell analysis. It is suggested that various cells in the tumour microenvironment are involved in hypoxia gene set processes to varying degrees.

CONCLUSION

The Hypoxia-score proves to be a valuable tool for assessing the prognosis of gastric cancer patients and guiding drug treatments, providing significant guidance for clinical diagnosis and treatment in the context of gastric cancer.

Pyroptosis-Derived Long Noncoding RNA Profiles Reveal a Novel Signature for Evaluating the Prognosis of Patients With Lung Adenocarcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) were recently implicated in modifying pyroptosis. Nonetheless, pyroptosis-related lncRNAs and their possible clinical relevance persist largely uninvestigated in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

A sum of 921 samples were collected from three independent data sets. We obtained pyroptosis-related genes from both the Molecular Signatures Database and relevant literature sources and used four machine learning techniques, comprising stepwise Cox, ridge regression, least absolute shrinkage and selection operator, and random forest. Multiple bioinformatics approaches were used to further investigate the underlying mechanisms.

RESULTS

In total, 39 differentially expressed pyroptosis genes were identified by comparing normal and tumor samples. Correlation analysis revealed 933 pyroptosis-related lncRNAs. Furthermore, univariate Cox regression determined 11 lncRNAs that exhibited stable associations with prognosis in the three cohorts, which were used to construct the pyroptosis-derived lncRNA signature. After analyzing the optimal results from four machine learning algorithms, we ultimately selected random forest to develop the pyroptosis-derived lncRNA signature. This signature was proven to be an independent prognostic factor and exhibited robust performance in three cohorts.

CONCLUSION

We provided novel insight and established a pyroptosis-derived lncRNA signature for patients with LUAD, exhibiting strong predictive capabilities in both the training and validation sets.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Background

Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma.

Methods

We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining.

Results

Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses.

Conclusion

The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain.

AIM

To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database.

RESULTS

An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression.

CONCLUSION

We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

Cuprotosis clusters predict prognosis and immunotherapy response in low-grade glioma

Cuprotosis, an emerging mode of cell death, has recently caught the attention of researchers worldwide. However, its impact on low-grade glioma (LGG) patients has not been fully explored. To gain a deeper insight into the relationship between cuprotosis and LGG patients' prognosis, we conducted this study in which LGG patients were divided into two clusters based on the expression of 18 cuprotosis-related genes. We found that LGG patients in cluster A had better prognosis than those in cluster B. The two clusters also differed in terms of immune cell infiltration and biological functions. Moreover, we identified differentially expressed genes (DEGs) between the two clusters and developed a cuprotosis-related prognostic signature through the least absolute shrinkage and selection operator (LASSO) analysis in the TCGA training cohort. This signature divided LGG patients into high- and low-risk groups, with the high-risk group having significantly shorter overall survival (OS) time than the low-risk group. Its predictive reliability for prognosis in LGG patients was confirmed by the TCGA internal validation cohort, CGGA325 cohort and CGGA693 cohort. Additionally, a nomogram was used to predict the 1-, 3-, and 5-year OS rates of each patient. The analysis of immune checkpoints and tumor mutation burden (TMB) has revealed that individuals belonging to high-risk groups have a greater chance of benefiting from immunotherapy. Functional experiments confirmed that interfering with the signature gene TNFRSF11B inhibited LGG cell proliferation and migration. Overall, this study shed light on the importance of cuprotosis in LGG patient prognosis. The cuprotosis-related prognostic signature is a reliable predictor for patient outcomes and immunotherapeutic response and can help to develop new therapies for LGG.

Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion

Background

Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity.

Methods

Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET.

Results

The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells.

Conclusion

To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.

Gasdermins: a dual role in pyroptosis and tumor immunity

The gasdermin (GSDM) protein family plays a pivotal role in pyroptosis, a process critical to the body's immune response, particularly in combatting bacterial infections, impeding tumor invasion, and contributing to the pathogenesis of various inflammatory diseases. These proteins are adept at activating inflammasome signaling pathways, recruiting immune effector cells, creating an inflammatory immune microenvironment, and initiating pyroptosis. This article serves as an introduction to the GSDM protein-mediated pyroptosis signaling pathways, providing an overview of GSDMs' involvement in tumor immunity. Additionally, we explore the potential applications of GSDMs in both innovative and established antitumor strategies.

Developing the novel diagnostic model and potential drugs by integrating bioinformatics and machine learning for aldosterone-producing adenomas

Background: Aldosterone-producing adenomas (APA) are a common cause of primary aldosteronism (PA), a clinical syndrome characterized by hypertension and electrolyte disturbances. If untreated, it may lead to serious cardiovascular complications. Therefore, there is an urgent need for potential biomarkers and targeted drugs for the diagnosis and treatment of aldosteronism. Methods: We downloaded two datasets (GSE156931 and GSE60042) from the GEO database and merged them by de-batch effect, then screened the top50 of differential genes using PPI and enriched them, followed by screening the Aldosterone adenoma-related genes (ARGs) in the top50 using three machine learning algorithms. We performed GSEA analysis on the ARGs separately and constructed artificial neural networks based on the ARGs. Finally, the Enrich platform was utilized to identify drugs with potential therapeutic effects on APA by tARGseting the ARGs. Results: We identified 190 differential genes by differential analysis, and then identified the top50 genes by PPI, and the enrichment analysis showed that they were mainly enriched in amino acid metabolic pathways. Then three machine learning algorithms identified five ARGs, namely, SST, RAB3C, PPY, CYP3A4, CDH10, and the ANN constructed on the basis of these five ARGs had better diagnostic effect on APA, in which the AUC of the training set is 1 and the AUC of the validation set is 0.755. And then the Enrich platform identified drugs tARGseting the ARGs with potential therapeutic effects on APA. Conclusion: We identified five ARGs for APA through bioinformatic analysis and constructed Artificial neural network (ANN) based on them with better diagnostic effects, and identified drugs with potential therapeutic effects on APA by tARGseting these ARGs. Our study provides more options for the diagnosis and treatment of APA.

Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC

BACKGROUND

Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC).

MATERIALS AND METHODS

The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset.

RESULTS

According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset.

CONCLUSION

The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.

The Role of Cuproptosis Key Factor FDX1 in Gastric Cancer

BACKGROUND

Gastric cancer is a common malignant tumor of the digestive tract, both domestically and internationally. It has high incidence and mortality rates, posing a significant threat to human health. The levels of blood copper are elevated in patients with gastric cancer. However, the exact relationship between copper overload and the malignant phenotype of gastric cancer is still unclear. This study aims to investigate the role of the Cuproptosis-related factor FDX1 in the conversion of gastric cancer to a malignant phenotype.

METHODS

Firstly, the relative mRNA and protein expression levels of FDX1 in gastric cancer were detected. Secondly, lentiviral transfection of gastric cancer cell lines was performed, and the effects of FDX1 functional intervention on the proliferation, invasion and migration of gastric cancer cells were assessed by CCK-8, colony formation, EdU proliferation, cell scratch and Transwell assays. Thirdly, the differential alteration of genes after overexpression of FDX1 was also analyzed by transcriptome sequencing. Finally, we assessed the tumour-forming capacity in vivo by the xenograft model.

RESULTS

FDX1 is significantly upregulated in gastric cancer. The inhibition of FDX1 function results in the suppression of malignant phenotypic transformation in gastric cancer cells. Conversely, overexpression of FDX1 function leads to alterations in tumor-related signaling pathways and the tumor microenvironment.

CONCLUSION

FDX1 plays a significant role in the malignant phenotypic transformation of gastric cancer cells. Further investigation into the regulatory mechanism of FDX1 in the malignant transformation of gastric cancer will enhance our understanding of the involvement of Cuproptosis in gastric cancer.

Expression of pyroptosis-associated genes and construction of prognostic model for thyroid cancer

Background

Thyroid cancer (THCA) is a common endocrine malignancy. This study aimed to explore the expression of pyroptosis-related genes in THCA and establish a prognosis prediction model.

Methods

Differentially expressed pyroptosis-related genes (DEPRGs) were identified in The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). Subsequently, these genes were subjected to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A least absolute shrinkage and selection operator (LASSO) regression model was employed to establish a DEPRG signature, and its reliability was validated through survival analysis and receiver operating characteristic (ROC) curves. Patients in TCGA-THCA cohort were stratified into two risk groups. The biological functions of the genes between the two risk groups were assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Finally, expression of DEPRGs was validated using datasets from the Gene Expression Omnibus (GEO) and the Human Protein Atlas (HPA) databases.

Results

Six DEPRGs were identified in TCGA dataset. Through LASSO Cox regression analysis, we determined IL6, TP63, NOD1, and BAX to be significant. Kaplan-Meier survival analysis demonstrated that patients with THCA expressing high levels of NOD1 and classified as low-risk individuals exhibited prolonged survival. The multifactorial ROC curves yielded area under the curve (AUC) scores exceeding 0.7 for risk score, age, and T-stage, affirming their significance as independent prognostic factors as determined by multivariate analysis. Additionally, we observed elevated expression levels of BAX and NOD1 in THCA using data derived from the HPA database and the GEO dataset.

Conclusions

We established a novel DEPRG signature for predicting the prognosis of THCA, potentially offering a promising therapeutic marker for THCA treatment.

A novel oxidative stress-related gene signature as an indicator of prognosis and immunotherapy responses in HNSCC

PURPOSE

To identify molecular subtypes of oxidative stress-related genes in head and neck squamous cell carcinoma (HNSCC) and to construct a scoring model of oxidative stress-related genes.

METHODS

R language based scRNA-seq and bulk RNA-seq analyses were used to identify molecular isoforms of oxidative stress-related genes in HNSCC. An oxidative stress-related gene scoring (OSRS) model was constructed, which were verified through online data and immunohistochemical staining of clinical samples.

RESULTS

Using TCGA-HNSCC datasets, nine predictive genes for overall patient survival, rarely reported in previous similar studies, were screened. AREG and CES1 were identified as prognostic risk factors. CSTA, FDCSP, JCHAIN, IFFO2, PGLYRP4, SPOCK2 and SPINK6 were identified as prognostic factors. Collectively, all genes formed a prognostic risk signature model for oxidative stress in HNSCC, which were validated in GSE41613, GSE103322 and PRJEB23709 datasets. Immunohistochemical staining of SPINK6 in nasopharyngeal cancer samples validated the gene panel. Subsequent analysis indicated that subgroups of the oxidative stress prognostic signature played important roles during cellular communication, the immune microenvironment, the differential activation of transcription factors, oxidative stress and immunotherapeutic responses.

CONCLUSIONS

The risk model might predict HNSCC prognosis and immunotherapeutic responses.

Application of pyroptosis score in the treatment and prognosis evaluation of gastric cancer

Pyroptosis is a kind of proinflammatory programmed cell death mediated by inflammasome. It affects the occurrence and development of gastric cancer through different ways, showing dual effects. On the one hand, inflammasome-mediated inflammatory response is highly likely to participate in the formation and development of early tumors; on the other hand, drugs can inhibit the deterioration process of tumor proliferation, invasion and metastasis through activating the pathways of inflammasome and pyroptosis. Recently, many agents based on pyroptosis have been found to inhibit gastric cancer by promoting the secondary pyroptosis pathway, regulating NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome and inhibiting caspase-1. The establishment of cell pyrodeath models can predict the prognosis of gastric cancer patients. Most of the models show that gastric cancer patients with high pyroptosis level have better prognosis and longer overall survival. Pyroptosis scores can also be used to predict the response of gastric cancer patients to immunotherapy and to screen potential anti-gastric cancer drugs. Therefore, in-depth understanding of the potential mechanism of pyroptosis affecting the progression of gastric cancer and the role of pyroptosis score in the treatment and prognosis assessment of gastric cancer will be helpful to find a new and effective method for the treatment of gastric cancer.

Comparative analysis identifies genetic and molecular factors associated with prognostic clusters of PANoptosis in glioma, kidney and melanoma cancer

The importance of inflammatory cell death, PANoptosis, in cancer is increasingly being recognized. PANoptosis can promote or inhibit tumorigenesis in context-dependent manners, and a computational approach leveraging transcriptomic profiling of genes involved in PANoptosis has shown that patients can be stratified into PANoptosis High and PANoptosis Low clusters that have significant differences in overall survival for low grade glioma (LGG), kidney renal cell carcinoma (KIRC) and skin cutaneous melanoma (SKCM). However, the molecular mechanisms that contribute to differential prognosis between PANoptosis clusters require further elucidation. Therefore, we performed a comprehensive comparison of genetic, genomic, tumor microenvironment, and pathway characteristics between the PANoptosis High and PANoptosis Low clusters to determine the relevance of each component in driving the differential associations with prognosis for LGG, KIRC and SKCM. Across these cancer types, we found that activation of the proliferation pathway was significantly different between PANoptosis High and Low clusters. In LGG and SKCM, we also found that aneuploidy and immune cell densities and activations contributed to differences in PANoptosis clusters. In individual cancers, we identified important roles for barrier gene pathway activation (in SKCM) and the somatic mutation profiles of driver oncogenes as well as hedgehog signaling pathway activation (in LGG). By identifying these genetic and molecular factors, we can possibly improve the prognosis for at risk-stratified patient populations based on the PANoptosis phenotype in LGG, KIRC and SKCM. This not only advances our mechanistic understanding of cancer but will allow for the selection of optimal treatment strategies.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Introduction

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME.

Methods

This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model.

Result

Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation.

Discussion

This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

Immune landscape and prognostic gene signatures in gastric cancer: implications for cachexia and clinical outcomes

Cachexia, a debilitating condition that worsens patient outcomes, often accompanies gastric cancer, a malignancy that is prevalent worldwide. The extensive research explored the interconnected molecular and immune aspects of stomach cancer, with a particular emphasis on cachexia. By employing the GEO database, we identified genes that were expressed differently in gastric cancer patients suffering from cachexia. Following the analysis of Weighted Gene Co-expression Network (WGCNA), gene modules intricately linked to particular immune cells were revealed, indicating a significantly disrupted tumor microenvironment. A strong predictive model was developed, centered around key genes such as CAMK4, SLC37A2, and BCL11B. Surprisingly, this particular model not only showed better predictive abilities in comparison to conventional clinical factors but also exhibited a strong connection with increased infiltration of macrophages and T cells. These discoveries suggest the presence of an immune-suppressing and tumor-promoting atmosphere among individuals at a greater risk. Moreover, the utilization of Gene Set Enrichment Analysis (GSEA) established a connection between the genes linked to our risk score and vital immune-related pathways, thereby strengthening the pivotal involvement of immunity in the development of gastric cancer. To summarize, our discoveries provide a more profound comprehension of the molecular and immune mechanisms that support cachexia in gastric cancer, presenting a hopeful basis for upcoming advancements in treatment.

RAI2 acts as a tumor suppressor with functional significance in gastric cancer

Metastasis of gastric cancer (GC) is one of the major causes of death among GC patients. GC metastasis involves numerous biological processes, yet the specific molecular biological mechanisms have not been elucidated. Here, we report a novel tumor suppressor, retinoic acid-induced 2 (RAI2), which is located in the Xp22 region of the chromosome and plays a role in inhibiting GC growth and invasion. In this study, integrated analysis of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and immunohistochemistry staining data suggested that RAI2 expression in GC samples was low. Moreover, the immune infiltration analysis indicated that low expression of RAI2 in GC was associated with a higher intensity of tumor-infiltrating lymphocytes (TILs) and an abundance of Programmed death ligand 1 (PD-L1) expression. Gene set enrichment analysis (GSEA) analysis further revealed that RAI2 regulated some pathways including the GAP junction, focal adhesion and ECM receptor interaction pathway, immune regulation, PI3K-Akt signaling, MAPK signaling, cell cycle, and DNA replication. Furthermore, the knockdown of RAI2 promoted GC cell proliferation, migration, and invasion in vitro. Taken together, these results suggest that the tumor suppressor RAI2 could be a potential target for the development of anti-cancer strategies in GC.

Identification of pyroptosis-related lncRNA subtype and signature predicts the prognosis in bladder cancer

Pyroptosis is a new type of programmed cell death involved in all stages of tumorigenesis. Herein, a comprehensive study was conducted to evaluate the prognostic significance of pyroptosis-related lncRNAs in bladder cancer. Consensus clustering analysis was performed to identify the subclusters of bladder cancer. The prognostic pyroptosis-related lncRNA signature was constructed using LASSO Cox regression analysis. Consensus clustering identified 2 clusters of bladder cancer. Interestingly, significant differences in the ESTIMAE score, immune cell infiltration and immune checkpoint expression were obtained between the 2 clusters. A signature consisting of 11 pyroptosis-related lncRNAs was established and it had a good performance in predicting the overall survival rate of bladder cancer, with an AUC of 0.713. Moreover, pyroptosis-related lncRNA signature acted as a risk factor in bladder cancer. Bladder cancer patients with high-risk score had a higher tumor grade and higher clinical stage. A significant correlation was obtained between the risk score and immune cell infiltration. The expression of most checkpoints was higher in bladder cancer patients with high-risk score. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets in bladder cancer.

A novel pyroptosis-related signature predicts prognosis and indicates immunotherapy in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) has been recognized as a frequently occurring oral malignant tumor. Pyroptosis plays an extremely important role in the occurrence and development of cancer, but the role of pyroptosis in OSCC remains unclear.

METHODS

OSCC-related data were obtained from the TCGA and GEO databases. A PSscore risk model was constructed through LASSO regression analysis. The GEO database was utilized as the validation set of the model. The "ESTIMATE" and "CIBERSORT" algorithms were utilized to additionally evaluate the relationship between the immune cell score and PSscore. TIDE and IPS algorithms were used to assess patient response to immunotherapy. In addition, Western blot analysis and MTT assay was used to further validate key genes.

RESULTS

Comprehensive bioinformatics analysis showed that a low-PSscore had a significant survival advantage, richer immune cell infiltration, more active immune-related pathways, higher TME scores, and lower tumor purity. The results of TIDE and IPS analysis indicated that the high-PSscore group had higher immune escape potential and was less sensitive to immunotherapy. In contrast, the low-PSscore group patients might be more sensitive to PD1 and CTLA4 + PD1 immunotherapy. Univariate and multivariate COX results indicated that PSscore was an independent prognostic factor in OSCC patients. Another important finding is that BAK1 is a potential target of OSCC and is related to the Nod-like receptor signaling pathway. Knockdown of BAK1 can significantly reduce the proliferation of OSCC cells.

CONCLUSION

The PSscore model could be utilized as a powerful prognostic indicator and can help in the development of new immunotherapies.

Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors.

METHODS

The study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted.

RESULTS

In this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score.

CONCLUSION

This study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

The Gum-Gut Axis: Periodontitis and the Risk of Gastrointestinal Cancers

Periodontitis has been linked to an increased risk of various chronic non-communicable diseases, including gastrointestinal cancers. Indeed, dysbiosis of the oral microbiome and immune-inflammatory pathways related to periodontitis may impact the pathophysiology of the gastrointestinal tract and its accessory organs through the so-called "gum-gut axis". In addition to the hematogenous spread of periodontal pathogens and inflammatory cytokines, recent research suggests that oral pathobionts may translocate to the gastrointestinal tract through saliva, possibly impacting neoplastic processes in the gastrointestinal, liver, and pancreatic systems. The exact mechanisms by which oral pathogens contribute to the development of digestive tract cancers are not fully understood but may involve dysbiosis of the gut microbiome, chronic inflammation, and immune modulation/evasion, mainly through the interaction with T-helper and monocytic cells. Specifically, keystone periodontal pathogens, including Porphyromonas gingivalis and Fusobacterium nucleatum, are known to interact with the molecular hallmarks of gastrointestinal cancers, inducing genomic mutations, and promote a permissive immune microenvironment by impairing anti-tumor checkpoints. The evidence gathered here suggests a possible role of periodontitis and oral dysbiosis in the carcinogenesis of the enteral tract. The "gum-gut axis" may therefore represent a promising target for the development of strategies for the prevention and treatment of gastrointestinal cancers.

A novel immunogenomic classification for prognosis in non-small cell lung cancer

OBJECTIVE

To facilitate immunotherapy and prognostic assessment of non-small cell lung cancer (NSCLC), we established a novel immunogenomic classification to provide valid identification criteria.

METHODS

The immune enrichment scores were calculated by single sample gene set enrichment analysis (ssGSEA) and clustered into Immunity_L and Immunity_H, and the reliability of this classification was demonstrated. Immune microenvironment score and immune cell infiltration analysis of NSCLC were also performed. Randomly divided into training group and test group, a prognosis-related immune profile was developed using least absolute shrinkage and selection operator (LASSO) and stepwise COX proportional hazards model to construct a prognostic mode.

RESULTS

The risk score for this immune profile was identified as an independent prognostic factor and can be used as a powerful prognostic tool to refine tumor immunotherapy. Our study identified two NSCLC classifications based on immunomic profiling, Immunity_H and Immunity_L.

CONCLUSION

In conclusion, Immunogenomic classification can distinguish the immune status of different types of NSCLC patients and contribute to the immunotherapy of NSCLC patients.

A novel and robust pyroptosis-related prognostic signature predicts prognosis and response to immunotherapy in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a highly malignant gastrointestinal tumor, has a poor prognosis and high mortality rate. Pyroptosis could regulate tumor cell proliferation, invasion, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of pyroptosis-related genes (PRGs) in ESCC remains unclear. This study selected 33 PRGs, and finally identified 29 PRGs that were differentially expressed between ESCC and normal esophageal tissues. The genetic mutation variation landscape of PRG in ESCC was also summarised. Based on consensus clustering for the 33 PRGs, all ESCC patients could be divided into two subtypes. Functional enrichment analysis revealed that these 33 PRGs were mainly involved in cytokine production, interleukin-1 production, and the NOD-like receptor signalling pathway. We created a prognostic PRG signature based on least absolute shrinkage and selection operator regression and Cox regression analysis with good survival prediction ability in both GEO and TCGA cohorts. Combined with the clinical characteristics, signature-based risk score was found to be an independent factor for predicting the OS of ESCC patients. A nomogram with enhanced precision for forecasting ESCC was established based on various independent prognostic elements. Significant correlation was observed between prognostic PRGs and immune-cell infiltration, tumor mutation burden, microsatellite instability, immune checkpoint, and drug sensitivity. Finally, we validated the expression of four PRGs in ESCC cell lines and tissues samples. In conclusion, the PRGs exerted significant effects on tumor immunity and prognosis of ESCC.

A comprehensive analysis of FBN2 in bladder cancer: A risk factor and the tumour microenvironment influencer

Bladder cancer (BLCA) is a common and difficult-to-manage disease worldwide. Most common type of BLCA is urothelial carcinoma (UC). Fibrillin 2 (FBN2) was first discovered while studying Marfan syndrome, and its encoded products are associated with elastin fibres. To date, the role of FBN2 in BLCA remains unclear. The authors first downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The patients were divided into high FBN2 expression and low FBN2 expression groups, and the survival curve, clinical characteristics, tumour microenvironment (TME), and immune cell differences were analysed between the two groups. Then, the differentially expressed genes (DEGs) were filtered, and functional enrichment for DEGs was performed. Finally, chemotherapy drug susceptibility analysis based on the high and low FBN2 groups was conducted. The authors found upregulated expression of FBN2 in BLCA and proved that FBN2 could be an independent prognostic factor for BLCA. TME analysis showed that the expression of FBN2 affects several aspects of the TME. The upregulated expression of FBN2 was associated with a high stromal score, which may lead to immunosuppression and be detrimental to immunotherapy. In addition, the authors found that NK cells resting, macrophage M0 infiltration, and other phenomena of immune cell infiltration appeared in the high expression group of FBN2. The high expression of FBN2 was related to the high sensitivity of some chemotherapy drugs. The authors systematically investigated the effects and mechanisms of FBN2 on BLCA and provided a new understanding of the role of FBN2 as a risk factor and TME influencer in BLCA.

MFAP2 enhances cisplatin resistance in gastric cancer cells by regulating autophagy

Background

Cisplatin (CDDP) is of importance in cancer treatment and widely used in advanced gastric cancer (GC). However, its clinical usage is limited due to its resistance, and the regulatory mechanism of CDDP resistance in GC has not yet been fully elucidated. In this study, we first conducted a comprehensive study to investigate the role of MFAP2 through bioinformatics analysis.

Methods

The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were applied to downloadgene expression data and clinicopathologic data, and the differentially expressed genes (DEGs) were further analyzed. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and survival analysis were conducted. Furthermore, according to the clinicopathological characteristics of TCGA, clinical correlation analysis was conducted, and a receiver operating characteristic curve (ROC) was plotted.

Results

We revealed that FAP, INHBA and MFAP2 were good diagnostic factors of GC. However, the mechanism of MFAP2 in GC remains elusive, especially in the aspect of chemotherapy resistance. We developed the CDDP-resistant cell line, and found that MFAP2 was upregulated in CDDP-resistant cells, and MFAP2-knockdown improved CDDP sensitivity. Finally, we found that MFAP2 enhanced CDDP resistance by inducing autophagy in drug-resistant cell lines.

Conclusions

The above results suggested that MFAP2 could affect the chemotherapy resistance by altering the level of autophagy in GC patients as a potential therapeutic target.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma

Introduction

Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear.

Methods

Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes.

Results

Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system.

Discussion

Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

Screening for autophagy/hypoxia/ferroptosis/pyroptosis-related genes of tendon injury and repair in a rat model after celecoxib and lactoferrin treatment

BACKGROUND

Tendon injuries are among the most common musculoskeletal disorders. Celecoxib possesses an effective anti-inflammatory activity in the tendon injury treatment. Lactoferrin has a great potential for the tendon regeneration. However, the efficacy of celecoxib combined with lactoferrin in the treatment of tendon injury has not been reported. In this study, we aimed to investigate the effect of celecoxib and lactoferrin on tendon injury and repair, and screen for the crucial genes associated with the tendon injury and repair.

METHODS

The rat tendon injury models were established and divided into four groups: normal control group (n = 10), tendon injury model group (n = 10), celecoxib treatment group (n = 10), and celecoxib + lactoferrin treatment group (n = 10). Then, RNA sequencing was performed to identify differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) in celecoxib treatment group and celecoxib + lactoferrin treatment group. Next, autophagy/hypoxia/ferroptosis/pyroptosis-related DEmRNAs were further identified. Subsequently, functional enrichment, protein-protein interaction (PPI) network and transcriptional regulatory network construction for these genes were performed.

RESULTS

The animal study demonstrated that combinational administration of celecoxib with lactoferrin rescued the harmful effects caused by celecoxib in the treatment of tendon injury. Compared to tendon injury model group, 945 DEmRNAs, 7 DEmiRNAs and 34 DElncRNAs were obtained in celecoxib treatment group, and 493 DEmRNAs, 8 DEmiRNAs and 21 DElncRNAs were obtained in celecoxib + lactoferrin treatment group, respectively. Subsequently, 376 celecoxib + lactoferrin treatment group-specific DEmRNAs were determined. Then, 25 DEmRNAs associated with autophagy/hypoxia/ferroptosis/pyroptosis were identified.

CONCLUSIONS

Several genes, such as, Ppp1r15a, Ddit4, Fos, Casp3, Tgfb3, Hspb1 and Hspa8, were identified to be associated with tendon injury and repair.

A novel pyroptosis-related prognostic signature for lung adenocarcinoma: Identification and multi-angle verification

Background: Lung adenocarcinoma (LUAD) is an aggressive disease of heterogeneous characteristics with poor prognosis and high mortality. Pyroptosis, a newly uncovered type of programmed cell death with an inflammatory nature, has been determined to hold substantial importance in the progression of tumors. Despite this, the knowledge about pyroptosis-related genes (PRGs) in LUAD is limited. This study aimed to develop and validate a prognostic signature for LUAD based on PRGs.

Methods: In this research, gene expression information from The Cancer Genome Atlas (TCGA) served as the training cohort and data from Gene Expression Omnibus (GEO) was utilized as the validation cohort. PRGs list was taken from the Molecular Signatures Database (MSigDB) and previous studies. Univariate Cox regression and Lasso analysis were then conducted to identify prognostic PRGs and develop a LUAD prognostic signature. The Kaplan-Meier method, univariate and multivariate Cox regression models were employed to assess the independent prognostic value and forecasting accuracy of the pyroptosis-related prognostic signature. The correlation between prognostic signature and immune infiltrating was analyzed to examine the role in tumor diagnosis and immunotherapy. Further, RNA-seq as well as quantitative real-time polymerase chain reaction (qRT-PCR) analysis in separate data sets was applied in order to validate the potential biomarkers for LUAD.

Results: A novel prognostic signature based on 8 PRGs (BAK1, CHMP2A, CYCS, IL1A, CASP9, NLRC4, NLRP1, and NOD1) was established to predict the survival of LUAD. The prognostic signature proved to be an independent prognostic factor of LUAD with satisfactory sensitivity and specificity in the training and validation sets. High-risk scores subgroups in the prognostic signature were significantly associated with advanced tumor stage, poor prognosis, less immune cell infiltration, and immune function deficiency. RNA sequencing and qRT-PCR analysis confirmed that the expression of CHMP2A and NLRC4 could be used as biomarkers for LUAD.

Conclusion: We have successfully developed a prognostic signature consisting of eight PRGs that providing a novel perspective on predicting prognosis, assessing infiltration levels of tumor immune cells, and determining the outcomes of immunotherapy for LUAD.