Development and Validation of a Robust Ferroptosis-Related Prognostic Signature in Lung Adenocarcinoma

Time-restricted feeding protects against septic liver injury by reshaping gut microbiota and metabolite 3-hydroxybutyrate

Liver injury is an independent risk factor for multiple organ dysfunction and high mortality in patients with sepsis. However, the pathological mechanisms and therapeutic strategies for sepsis-associated liver injury have not been fully elucidated. Time-restricted feeding (TRF) is a promising dietary regime, but its role in septic liver injury remains unknown. Using 16S rRNA gene sequencing, Q200 targeted metabolomics, transcriptomics, germ-free mice, Hmgcs2/Lpin1 gene knockout mice, and Aml12 cells experiments, we revealed that TRF can mitigate septic liver injury by modulating the gut microbiota, particularly by increasing Lactobacillus murinus (L. murinus) abundance, which was significantly reduced in septic mice. Further study revealed that live L. murinus could markedly elevate serum levels of metabolite 3-hydroxybutyrate (3-HB) and alleviate sepsis-related injury, while the knockout of the key enzyme for 3-HB synthesis (3-hydroxy-3-methylglutaryl-CoA synthase 2, Hmgcs2) in the liver negated this protective effect. Additionally, serum 3-HB levels were significantly positively correlated with L. murinus abundance and negatively correlated with liver injury indicators in septic patients, demonstrating a strong predictive value for septic liver injury (AUC = 0.8429). Mechanistically, 3-HB significantly inhibited hepatocyte ferroptosis by activating the PI3K/AKT/mTOR/LPIN1 pathway, reducing ACSL4, MDA, LPO, and Fe2+ levels. This study demonstrates that TRF reduces septic liver injury by modulating gut microbiota to increase L. murinus, which elevates 3-HB to activate PI3K/AKT/mTOR/LPIN1 and inhibit hepatocyte ferroptosis. Overall, this study elucidates the protective mechanism of TRF against septic liver injury and identifies 3-HB as a potential therapeutic target and predictive biomarker, thereby providing new insights into the clinical management and diagnosis of septic liver injury.

Multi-omics integration reveals YWHAE as a key mediator of ferroptosis in ARDS

Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by severe hypoxemia and high mortality. Ferroptosis, a form of regulated cell death driven by iron accumulation and lipid peroxidation, has emerged as a critical mechanism in ARDS pathogenesis. However, the molecular regulators of ferroptosis in ARDS remain unclear. This study integrates multi-omics analysis and experimental validation to identify ferroptosis-related targets in ARDS. Bronchoalveolar lavage fluid (BALF) samples from ARDS patients and healthy controls were subjected to proteomics and metabolomics analysis. Transcriptomic data from the GSE243066 dataset and ferroptosis-related gene databases were integrated to identify key genes. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. An LPS-induced ARDS mouse model was established for experimental validation, including Western blotting, histopathology, and ferroptosis-related biochemical assays. Multi-omics analysis identified YWHAE as a ferroptosis-associated gene significantly upregulated in ARDS. Functional enrichment revealed key pathways, including ferroptosis, hypoxia-inducible factor-1 signaling, and oxidative stress responses. Proteomic and transcriptomic integration highlighted 51 overlapping differentially expressed genes, with YWHAE emerging as a central hub in the protein-protein interaction network. Metabolomics analysis further revealed glutathione and cysteine metabolism as critical pathways linked to ferroptosis. In the ARDS mouse model, ferroptosis inhibitor ferrostatin-1 (Fer-1) attenuated LPS-induced lung injury, reduced oxidative stress markers, and downregulated YWHAE expression. This study identifies YWHAE as a novel ferroptosis-related target in ARDS through multi-omics analysis and experimental validation. These findings provide new insights into the molecular mechanisms of ferroptosis in ARDS and highlight YWHAE as a potential therapeutic target for future interventions.

Role of Radiation-related Lung Function Genes in Prognosis and Immune Infiltration of Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a common malignant tumor with no obvious clinical symptoms in its early stages. Patients can be divided into radiotherapysensitive groups (RS) and radiotherapy-resistant groups (RR) due to their varying conditions. The therapeutic effect of radiotherapy is quite different between the two groups. Therefore, this paper explores the role of radiation-related lung function genes in LUAD and its immune landscape.

METHODS

Firstly, we divided LUAD samples from the TCGA cohort into RS and RR groups and analyzed differential expression to obtain differentially expressed genes (DEGs). Then, DEGs and patients' grouping information were input into the weighted co-expression network, and the genes in the radiotherapy-related modules were identified. Furthermore, after the intersection of DEGs and lung function-related genes, the prognosis-related genes were obtained through univariate Cox and Lasso-Cox analyses, respectively, and the risk model was constructed. Finally, the differences in prognosis and immunity of the samples in the risk model were explored. Additionally, we also performed a qPCR experiment on lung function-related genes.

RESULTS

In this paper, radiation-related genes of LUAD were identified through a series of bioinformatics analyses. By conducting enrichment analysis on these genes, several pathways related to LUAD radiation were identified, and DEGs associated with significant prognosis were determined. Furthermore, a radiation-related risk model of LUAD was developed. All samples were divided into high-risk and low-risk groups based on the risk score, and the differences in immune cell infiltration abundance and immune function between these groups were evaluated. The qPCR experimental results demonstrated a significant difference in the expression of genes related to lung function.

CONCLUSION

The prognosis-related genes identified in this paper and the risk model created can serve as a reference for diagnosing and treating LUAD.

Identification of the ferroptosis-related gene signature and the associated regulation axis in lung cancer and rheumatoid arthritis

Patients with Rheumatoid arthritis (RA) have an elevated risk of lung cancer compared to the healthy population. However, there are few studies on the relationship between RA and lung adenocarcinoma (LUAD), especially the mechanisms at the genetic level. In this study, we investigated the link between RA and LUAD regarding Ferroptosis-Related Genes. The RNA-seq data of RA (GSE77298 and GSE 82107) and LUAD(GSE75037) in the Gene Expression Omnibus (GEO) database were obtained. 259 ferroptosis-related genes were obtained from the website ( http://www.zhounan.org/ferrdb/ ).The differential genes obtained from the RA and LUAD datasets were intersected with ferroptosis-related genes to obtain the ferroptosis-related differentially expressed genes (FRDEGs). Next, the mRNA-miRNA network was constructed, then Gene Set Enrichment Analysis (GSEA) for target genes were performed. The CIBERSORT algorithm was used to analyze the immune infiltration. Finally, the results were validated using external datasets (GSE89408 and GSE48780) and The Cancer Genome Atlas (TCGA) dataset. We obtained FRDEGs common to LUAD and RA: FANCD2, HELLS, RRM2, G6PD, VLDLR. These five genes play important roles in the progression of RA and LUAD. They also hold great diagnostic value for both diseases. Also, we found that LUAD and RA share common signaling pathways and similar immune mechanisms.

Characterization of a ferroptosis-related gene signature predicting survival and immunotherapeutic response in lung adenocarcinoma

Lung cancer remains the leading cause of cancer-related death worldwide, and drug resistance represents the main obstacle responsible for the poor mortality and prognosis. Here, to identify a novel gene signature for predicting survival and drug response, we jointly investigated RNA sequencing data of lung adenocarcinoma patients from TCGA and GEO databases, and identified a ferroptosis-related gene signature. The signature was validated in the validation set and two external cohorts. The high-risk group had a reduced survival than the low-risk group (P < 0.05). Moreover, the established gene signature was associated with tumor mutation burden, microsatellite instability, and response to immune checkpoint blockade. In addition, four candidate oncogenes (RRM2, SLC2A1, DDIT4, and VDAC2) were identified to be candidate oncogenes using in silico and wet experiments, which could serve as potential therapeutic targets. Collectively, this study developed a novel ferroptosis-related gene signature for predicting prognosis and drug response, and identified four candidate oncogenes for lung adenocarcinoma.

A novel signature of seven aging-related genes for risk stratification, prognosis prediction and benefit evaluation of chemotherapy, and immunotherapy in elderly patients with lung adenocarcinoma

Background

Aging, a multifaceted biological process, is thought to be associated with lung adenocarcinoma (LUAD) development and progression. However, it is unclear whether aging-related genes (ARGs) can predict tumor risk, chemotherapy and immunotherapy benefits, and prognosis in LUAD patients at different ages.

Methods

Gene expression datasets and clinical information of LUAD patients were downloaded from TCGA and GEO database. Univariate and multivariate Cox regression, and lasso algorithm were employed to identify the ARG signatures. Patients were stratified into high-risk and low-risk groups to evaluate the predictive accuracy using Kaplan-Meier curves, ROC curves, and time-dependent AUC. A nomogram was established to predict the survival probability. GSEA revealed potential pathways, and CIBERSORT indicated different immunologic status. TIDE score was used to predict the potential tumor response to immune checkpoint inhibitors, and GDSC was employed to evaluate the sensitivity of chemotherapeutic drugs. The correlation of TIDE score and patient age, as well as that of ARGs and patient age was investigated. And cell Culture and RT-qPCR for external validation for key gene.

Results

A novel gene signature based on seven ARGs was established, including BMP15, CD79A, CDKN3, CDX2, COL1A1, DKK1, and GRIK2. Our model demonstrated exceptional prediction accuracy for elderly LUAD patients of 71-90 years old. A nomogram model was constructed to predict the survival probability, and the C-index value was 0.737, indicating our prognostic nomogram model has high accuracy. Through external RT-qPCR validation, we found that CD79A expression in H1299 was higher than that of BEAS-2B. And novel immunotherapy and chemotherapy regimens were accordingly proposed for the elderly LUAD patients.

Conclusion

We identified a novel gene signature based on seven ARGs for risk stratification, prognosis prediction and benefit evaluation of immunotherapy and chemotherapy in elderly LUAD patients.

Metabolism-regulated ferroptosis in cancer progression and therapy

Cancer metabolism mainly includes carbohydrate, amino acid and lipid metabolism, each of which can be reprogrammed. These processes interact with each other to adapt to the complicated microenvironment. Ferroptosis is a regulated cell death induced by iron-dependent lipid peroxidation, which is morphologically different from apoptosis, necrosis, necroptosis, pyroptosis, autophagy-dependent cell death and cuprotosis. Cancer metabolism plays opposite roles in ferroptosis. On the one hand, carbohydrate metabolism can produce NADPH to maintain GPX4 and FSP1 function, and amino acid metabolism can provide substrates for synthesizing GPX4; on the other hand, lipid metabolism might synthesize PUFAs to trigger ferroptosis. The mechanisms through which cancer metabolism affects ferroptosis have been investigated extensively for a long time; however, some mechanisms have not yet been elucidated. In this review, we summarize the interaction between cancer metabolism and ferroptosis. Importantly, we were most concerned with how these targets can be utilized in cancer therapy.

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.

The novel miR-873-5p-YWHAE-PI3K/AKT axis is involved in non-small cell lung cancer progression and chemoresistance by mediating autophagy

Non-small cell lung cancer (NSCLC) encompasses approximately 85% of all lung cancer cases and is the foremost cancer type worldwide; it is prevalent in both sexes and known for its high fatality rate. Expanding scientific inquiry underscores the indispensability of microRNAs in NSCLC. Here, we probed the impact of miR-873-5p on NSCLC development and chemoresistance. qRT‒PCR was used to measure the miR-873-5p level in NSCLC cells with or without chemoresistance. A model of miR-873-5p overexpression was constructed. The proliferation and viability of NSCLC cells were evaluated through CCK8 and colony formation experiments. Cell migration and invasion were monitored via Transwell assays. Western blotting was used to determine the levels of YWHAE, PI3K, AKT, EMT, apoptosis, and autophagy-related proteins. The sensitivity of NSCLC cells to the chemotherapeutic agent gefitinib was assessed. Additionally, the correlation of YWHAE with miR-873-5p was validated via a dual-luciferase reporter assay and RNA immunoprecipitation (RIP). Overexpressed miR-873-5p suppressed migration, proliferation, invasion, and EMT while concurrently stimulating apoptotic processes. miR-873-5p was downregulated in NSCLC cells resistant to gefitinib. Upregulating miR-873-5p reversed gefitinib resistance by inducing autophagy. YWHAE was confirmed to be a downstream target of miR-873-5p. YWHAE overexpression promoted the malignant behaviors of NSCLC cells and boosted tumor growth, while these effects were reversed following miR-873-5p overexpression. Subsequent investigations revealed that overexpressing YWHAE promoted PI3K/AKT pathway activation, with miR-873-5p displaying inhibitory effects on the YWHAE-mediated PI3K/AKT signaling cascade. miR-873-5p affects proliferation, invasion, migration, EMT, autophagy, and chemoresistance in NSCLC by controlling the YWHAE/PI3K/AKT axis.

Lactate score classification of hepatocellular carcinoma helps identify patients with tumors that respond to immune checkpoint blockade therapy

PURPOSE

Hepatocellular carcinoma (HCC) responds poorly to immunotherapy, and the durable response rate is 10-20%. Here, we aim to characterize HCC classifications based on lactate genes to identify patients who may benefit from immunotherapy.

METHODS

Lactate-related genes were applied for HCC classification in the current study, and lactate Cluster 1 (LC1) and lactate Cluster 2 (LC2) were defined. Differential genes from LC1 and LC2 helped define the following lactate phenotype clusters: lactate phenotype Cluster 1 (LPC1), lactate phenotype Cluster 2 (LPC2) and lactate phenotype Cluster 3 (LPC3). Based on the cluster annotation, the lactate score was defined and analyzed to evaluate the immunotherapy response.

RESULTS

All the classified clusters were analyzed, and they showed different immune signatures. The survival rate of LPC3 was higher than that of LPC2 (LPC3 vs. LPC2, P = 0.027) and LPC1 (LPC3 vs. LPC1, P = 0.027). Then, the lactate score was annotated and confirmed to be effective in predicting responses to immune checkpoint blockade therapy.

CONCLUSION

In the current study, we developed a classification system for HCC and defined the lactate score, which was validated to be partially effective in estimating responses among tumor patients.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

CEP55, serving as a diagnostic marker gene for osteosarcoma, triggers the JAK2-STAT3-MMPs axis

Background

Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease's exceptionally rare occurrence and unclear rationale.

Objective

This study's goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy.

Methods

The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways.

Results

Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway.

Conclusion

At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

Introduction of LPIN1 as a potential diagnostic and prognostic biomarker for gastric cancer via integrative bioinformatics analysis of a competing endogenous RNA network and experimental validation

Objectives

Identification of effective biomarkers is crucial for the heterogeneous disease of gastric cancer (GC). Recent studies have focused on the role of pseudogenes regulating gene expression through competing endogenous RNA (ceRNA) networks, however, the pseudogene-associated ceRNA networks in GC remain largely unknown. The current study aimed to construct and analyze a three-component ceRNA network in GC and experimentally validate a ceRNA.

Materials and Methods

A comprehensive analysis was conducted on the RNA-seq and miRNA-seq data of The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) dataset to identify differentially-expressed mRNAs (DEMs), pseudogenes (DEPs), and miRNAs (DEMis). Pseudogene-associated ceRNA and protein-protein interaction (PPI) networks were constructed, and functional enrichment analyses were performed. DEMs and DEPs with degree centralities≥2 were selected for survival analysis. A ceRNA was further selected for experimental validation.

Results

10,145 DEMs, 3576 DEPs, and 66 DEMis were retrieved and a ceRNA network was then constructed by including DEMis with concurrent interactions with at least a DEM and a DEP. Functional enrichment analysis demonstrated that DEMs of the ceRNA network were significantly enriched in cancer-associated pathways. LPIN1 and WBP1L were two mRNAs showing an association with STAD patients overall survival. Expression analysis of LPIN1 showed a significant decrease in GC tumors compared to non-tumor tissues (P=0.003).

Conclusion

Our research emphasizes the significant implications of ceRNA networks in the development of new biomarkers for the detection and prognosis of cancer. Further examination is necessary to explore the functional roles of LPIN1 in the pathogenesis of GC.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights

Ferroptosis is closely associated with uterine corpus endometrial carcinoma (UCEC) development. This project aimed to identify new potential biomarkers to predict the prognosis of UCEC. In this work, UCEC transcriptome data along with clinical information was retrieved from the TCGA database including a total of 382 FRGs. We performed univariate Cox regression analysis to evaluate ferroptosis-related genes (FRGs) for prognostic significance. The genes with prognostic significance were then analyzed using LASSO-Cox to construct a prognosis model. The model genes were further characterized through various proteomic analyses and expression detection in clinical samples. A multivariate Cox regression model was constructed containing four FRGs (CDKN1A, CDKN2A, CEBPG, NOS2). Among four FRGs, higher expressions of CDKN2A, CEBPG, and NOS2 were associated with poorer overall survival probability, while higher expression of CDKN1A was associated with better overall survival probability. The area under the receiver operating characteristic curve of the risk model was 0.617, 0.688, and 0.693 for 1 year, 3 years, and 5 years, respectively. Moreover, proteomic analysis showed that the protein expression of CDKN1A, CDKN2A, and CEBPG was higher in tumor tissues than that in normal tissues. Higher protein expression of CDKN1A and CDKN2A predicted poorer survival probability. Besides, CDKN1A protein had an interaction relationship with CDKN2A protein or NOS2 protein. In clinical samples, all four FRGs were upregulated in UCEC tissues, regardless of gene expression or protein expression. Our four FRGs risk model provides new insights for predicting the prognosis of UCEC patients.

Big data analysis identified a telomere-related signature predicting the prognosis and drug sensitivity in lung adenocarcinoma

Telomeres exert a critical role in chromosome stability and aberrant regulation of telomerase may result in telomeres dysfunction and genomic instability, which are involved in the occurrence of cancers. However, limited studies have been performed to fully clarify the immune infiltration and clinical significance of telomeres-related genes (TRGs) in lung adenocarcinoma (LUAD). The number of clusters of LUAD was determined by consensus clustering analysis. The prognostic signature was constructed and verified using TCGA and GSE42127 dataset with Least Absolute Shrinkage and Selection Operator cox regression analysis. The correlation between different clusters and risk-score and drug therapy response was analyzed using TIDE and IMvigor210 dataset. Using several miRNA and lncRNA related databases, we constructed a lncRNA-miRNA-mRNA regulatory axis. We identified 2 telomeres-related clusters in LUAD, which had distinct differences in prognostic stratification, TMB score, TIDE score, immune characteristics and signal pathways and biological effects. A prognostic model was developed based on 21 TRGs, which had a better performance in risk stratification and prognosis prediction compared with other established models. TRGs-based risk score could serve as an independent risk factor for LUAD. Survival prediction nomogram was also developed to promote the clinical use of TRGs risk score. Moreover, LUAD patients with high risk score had a high TMB score, low TIDE score and IC50 value of common drugs, suggesting that high risk score group might benefit from receiving immunotherapy, chemotherapy and target therapy. We also developed a lncRNA KCNQ1QT1/miR-296-5p/PLK1 regulatory axis. Our study identified 2 telomeres-related clusters and a prognostic model in LUAD, which could be helpful for risk stratification, prognosis prediction and treatment approach selection.

Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns

BACKGROUND

Lung adenocarcinoma (LUAD) seriously threatens people's health worldwide. Programmed cell death (PCD) plays a critical role in regulating LUAD growth and metastasis as well as in therapeutic response. However, currently, there is a lack of integrative analysis of PCD-related signatures of LUAD for accurate prediction of prognosis and therapeutic response.

METHODS

The bulk transcriptome and clinical information of LUAD were obtained from TCGA and GEO databases. A total of 1382 genes involved in regulating 13 various PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, alkaliptosis and disulfidptosis) were included in the study. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify PCD-associated differential expression genes (DEGs). An unsupervised consensus clustering algorithm was used to explore the potential subtypes of LUAD based on the expression profiles of PCD-associated DEGs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF) analysis and stepwise multivariate Cox analysis were performed to construct a prognostic gene signature. The "oncoPredict" algorithm was utilized for drug-sensitive analysis. GSVA and GSEA were utilized to perform function enrichment analysis. MCPcounter, quanTIseq, Xcell and ssGSEA algorithms were used for tumor immune microenvironment analysis. A nomogram incorporating PCDI and clinicopathological characteristics was established to predict the prognosis of LUAD patients.

RESULTS

Forty PCD-associated DEGs related to LUAD were obtained by WGCNA analysis and differential expression analysis, followed by unsupervised clustering to identify two LUAD molecular subtypes. A programmed cell death index (PCDI) with a five-gene signature was established by machine learning algorithms. LUAD patients were then divided into a high PCDI group and a low PCDI group using the median PCDI as a cutoff. Survival and therapeutic analysis revealed that the high PCDI group had a poor prognosis and was more sensitive to targeted drugs but less sensitive to immunotherapy compared to the low PCDI group. Further enrichment analysis showed that B cell-related pathways were significantly downregulated in the high PCDI group. Accordingly, the decreased tumor immune cell infiltration and the lower tumor tertiary lymphoid structure (TLS) scores were also found in the high PCDI group. Finally, a nomogram with reliable predictive performance PCDI was constructed by incorporating PCDI and clinicopathological characteristics, and a user-friendly online website was established for clinical reference ( https://nomogramiv.shinyapps.io/NomogramPCDI/ ).

CONCLUSION

We performed the first comprehensive analysis of the clinical relevance of genes regulating 13 PCD patterns in LUAD and identified two LUAD molecular subtypes with distinct PCD-related gene signature which indicated differential prognosis and treatment sensitivity. Our study provided a new index to predict the efficacy of therapeutic interventions and the prognosis of LUAD patients for guiding personalized treatments.

Identification of lncRNAs associated with uterine corpus endometrial cancer prognosis based on the competing endogenous RNA network

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the major malignant tumors of the female reproductive system. However, there are limitations in the currently available diagnostic approaches for UCEC. Long non-coding RNAs (lncRNAs) play important roles in regulating biological processes as competitive endogenous RNA (ceRNA) in tumors. To study the potential of lncRNAs as non-invasive diagnostic tumor markers, RNA-sequencing dataset of UCEC patients from The Cancer Genome Atlas was used to identify differentially expressed genes. A lncRNA-miRNA-mRNA ceRNA network was constructed by differentially expressed lncRNAs, miRNAs and miRNAs. Pathway enrichment and functional analysis for the mRNAs in the constructed ceRNA network provide the direction of future research for UCEC by demonstrating the most affected processes and pathways. Seven potential lncRNA biomarkers (C20orf56, LOC100144604, LOC100190940, LOC151534, LOC727677, FLJ35390, LOC158572) were validated in UCEC patients by quantitative real-time PCR. Notably, LOC100190940 and LOC158572 were identified as novel RNA molecules with unknown functions. Receiver operating characteristic (ROC) curve analysis demonstrated that the combined 7 lncRNAs had a high diagnostic value for UCEC patients with area under curve (AUC) of 0.941 (95% CI: 0.875-0.947). Our study highlights the potential of the validated 7 lncRNAs panel as diagnostic biomarkers in UCEC, providing new insights into the UCEC pathogenesis.

STARD12/14 are diagnostic and prognostic biomarkers of lung adenocarcinoma associated with epigenetic regulation, immune infiltration and ferroptosis

Background: Metabolic reprogramming plays an important role in tumor progression and antitumor immunity. START domain-containing proteins (STARDs) are responsible for lipid metabolism. However, the underlying functions of STARDs in lung adenocarcinoma (LUAD) have not been clarified yet. Methods: Oncomine, UALCAN, TCGA and CPTAC were used to explore the expression landscape and clinicopathological characteristics of STARDs in LUAD. Diagnostic and prognostic values were assessed by Kaplan-Meier Plotter, Cox regression analysis, and ROC curve. GeneMANIA, GO, KEGG and GSEA were applied for exploring the potential biological functions. Epigenetic process, including mutation and m6A modification were analyzed by cBioPortal and TCGA. TIMER, TISIDB and TCGA cohort provided an immune signature. The correlation between STARDs expression and ferroptosis was analyzed by TCGA. Finally, the STARDs expression were confirmed by RT-qPCR and western blot. Results: STARD5/10/14 were overexpressed in LUAD compared with normal, while STARD4/7/8/11/12/13 were relatively low. STARD5/12/14 levels were positively related to clinical and lymph node stage. Survival analysis showed high STARD12 expression was associated with favorable overall survival, disease special survival as well as disease free survival, while STARD14 showed the opposite. GSEA analysis found STARD12 and STARD14 were associated with glycolysis, oxidative phosphorylation and tumor related signaling pathways. STARD12 co-expressed genes participated in cell cycle and DNA replication, and STARD14 were enriched in ECM-receptor interaction. Both STARD12 and STARD14 were corelated with epigenetic regulation, especially TP53 mutation and m6A modification. STARD12 expression was positively correlated with TMB level. The level of STARD12 was significantly associated with the abundance of infiltrating immune cells, including B cells, CD8+T cells, macrophages, dendritic cells, and chemokine, receptor, MHC, immunostimulatory related genes. STARD14 was negatively associated with the infiltration of CD8+T cells, while positively with CCL28 and immune checkpoints, including CTLA4 as well as PD-L2. In addition, STARD12/14 could regulate the ferroptosis related genes. Conclusion: STARD12 and STARD14 were expected to be potential biomarkers for LUAD, which were associated with epigenetic regulation, immune infiltration and ferroptosis.

Identification and Validation of Ferroptosis-Related Genes in Patients with Acute Spinal Cord Injury

Ferroptosis plays crucial roles in the pathology of spinal cord injury (SCI). The purpose of this study was to identify differentially expressed ferroptosis-related genes (DE-FRGs) in human acute SCI by bioinformatics analysis and validate the hub DE-FRGs in non-SCI and SCI patients. The GSE151371 dataset was downloaded from the Gene Expression Omnibus and difference analysis was performed. The differentially expressed genes (DEGs) in GSE151371 overlapped with the ferroptosis-related genes (FRGs) obtained from the Ferroptosis Database. A total of 41 DE-FRGs were detected in 38 SCI samples and 10 healthy samples in GSE151371. Then, enrichment analyses of these DE-FRGs were performed for functional annotation. The GO enrichment results showed that upregulated DE-FRGs were mainly associated with reactive oxygen species and redox reactions, and the KEGG enrichment analysis indicated involvement in some diseases and ferroptosis pathways. Protein-protein interaction (PPI) analysis and lncRNA-miRNA-mRNA regulatory network were performed to explore the correlations between genes and regulatory mechanisms. The relationship between DE-FRGs and differentially expressed mitochondria-related genes (DE-MRGs) was also analyzed. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the hub DE-FRGs in clinical blood samples from acute SCI patients and healthy controls. Consistent with the bioinformatics results, qRT-PCR of the clinical samples indicated similar expression levels of TLR4, STAT3, and HMOX1. This study identified DE-FRGs in blood samples from SCI patients, and the results could improve our understanding of the molecular mechanisms of ferroptosis in SCI. These candidate genes and pathways could be therapeutic targets for SCI.

Transcriptome profiling and ceRNA network of small extracellular vesicles from resting and degranulated mast cells

Aim: This study aimed to investigate the transcriptomic characteristics and interactions between competitive endogenous RNAs (ceRNAs) within small extracellular vesicles (sEVs) derived from mast cells (MCs). Methods: Transcriptome sequencing analyzed lncRNA, circRNA and mRNA expression in resting and degranulated MC-derived sEVs. Constructed ceRNA regulatory network through correlation analysis and target gene prediction. Results: Differentially expressed 1673 mRNAs, 173 lncRNAs and 531 circRNAs were observed between resting and degranulated MCs-derived sEVs. Enrichment analysis revealed involvement of neurodegeneration, infection and tumor pathways. CeRNA networks included interactions between lncRNA-miRNA, circRNA-miRNA and miRNA-mRNA, targeting genes in the hippo and wnt signaling pathways linked to tumor immune regulation. Conclusion: This study provides valuable insights into MC-sEV molecular mechanisms, offering significant data resources for further investigations.

Pannexin1 channels in the liver: an open enemy

Pannexin1 proteins form communication channels at the cell plasma membrane surface, which allow the transfer of small molecules and ions between the intracellular compartment and extracellular environment. In this way, pannexin1 channels play an important role in various cellular processes and diseases. Indeed, a plethora of human pathologies is associated with the activation of pannexin1 channels. The present paper reviews and summarizes the structure, life cycle, regulation and (patho)physiological roles of pannexin1 channels, with a particular focus on the relevance of pannexin1 channels in liver diseases.

A novel DNA methylation signature to improve survival prediction of progression-free survival for testicular germ cell tumors

This study aimed to develop a nomogram for predicting the progression-free survival (PFS) of testicular germ cell tumors (TGCT) patients based on DNA methylation signature and clinicopathological characteristics. The DNA methylation profiles, transcriptome data, and clinical information of TGCT patients were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox, lasso Cox, and stepwise multivariate Cox regression were applied to identify a prognostic CpG sites-derived risk signature. Differential expression analysis, functional enrichment analysis, immunoinfiltration analysis, chemotherapy sensitivity analysis, and clinical feature correlation analysis were performed to elucidate the differences among risk groups. A prognostic nomogram integrating CpG sites-derived risk signature and clinicopathological features was further established and evaluated likewise. A risk score model based on 7 CpG sites was developed and found to exhibit significant differences among different survival, staging, radiotherapy, and chemotherapy subgroups. There were 1452 differentially expressed genes between the high- and low-risk groups, with 666 being higher expressed and 786 being lower expressed. Genes highly expressed were significantly enriched in immune-related biological processes and related to T-cell differentiation pathways; meanwhile, down-regulated genes were significantly enriched in extracellular matrix tissue organization-related biological processes and involved in multiple signaling pathways such as PI3K-AKT. As compared with the low-risk group, patients in the high-risk group had decreased lymphocyte infiltration (including T-cell and B-cell) and increased macrophage infiltration (M2 macrophages). They also showed decreased sensitivity to etoposide and bleomycin chemotherapy. Three clusters were obtained by consensus clustering analysis based on the 7 CpG sites and showed distinct prognostic features, and the risk scores in each cluster were significantly different. Multivariate Cox regression analysis found that the risk scores, age, chemotherapy, and staging were independent prognostic factors of PFS of TGCT, and the results were used to formulate a nomogram model that was validated to have a C-index of 0.812. Decision curve analysis showed that the nomogram model was superior to other strategies in the prediction of PFS of TGCT. In this study, we successfully established CpG sites-derived risk signature, which might serve as a useful tool in the prediction of PFS, immunoinfiltration, and chemotherapy sensitivity for TGCT patients.

Integrated analysis of ferroptosis-related gene signature for overall survival prediction in Asian patients with hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most prevalent types of cancers in Asia. Accumulating evidence suggests that ferroptosis is a non-apoptotic form of cell death, and has played an important role in cancer biology.

METHODS

Based on the manually curated ferroptosis-related gene set and TCGA-LIHC dataset of Asian patients, we used DESeq2, Kaplan-Meier analysis, and univariate Cox regression to identify differentially expressed ferroptosis-related genes with significantly prognostic capacity. A risk signature was constructed based on the selected genes for predicting the survival of HCC patients in Asia. The survival prediction accuracy was confirmed by the time-dependent receiver operating characteristic (ROC) curve analysis. Gene set variation analysis (GSVA) was used to explore the functional associations of the signature. Ferroptosis potential index (FPI) and xCell algorithm was applied to quantify ferroptosis and immune cell infiltration, respectively. Two independent datasets from the GEO and the ICGC database were used for external validation.

RESULTS

The ferroptosis-related signature could accurately predict the survival outcomes of HCC patients in Asian (p value < 0.0001). We showed that the signature was an independent factor and was beneficial in elevating risk stratification of current clinicopathologic features, such as the amount of alpha-fetoprotein (AFP) and residual tumor classification. Functional characterization showed that critical processes in tumorigenesis belonged to the high-risk groups, for example inflammatory response, which may be the main driver of HCC. The high-risk group had higher FPIs and infiltrations of macrophages and T-helper cells than the low-risk group. Furthermore, two independent cohorts confirmed the prognostic value of our signature.

CONCLUSION

Overall, our results demonstrated potential application of ferroptosis-related genes as independent biomarkers in Asian HCC patients. Targeting ferroptosis may be clinically useful beyond known clinicopathological factors and provide benefit in immunotherapy.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

Ferroptosis is a recently established type of iron-dependent programmed cell death. Growing studies have focused on the function of ferroptosis in cancers, including lung adenocarcinoma (LUAD). However, the factors involved in the regulation of ferroptosis-related genes are not fully understood. In this study, we collected data from lung adenocarcinoma datasets of the Cancer Genome Atlas (TCGA-LUAD). The expression profiles of 60 ferroptosis-related genes were screened, and two differentially expressed ferroptosis subtypes were identified. We found the two ferroptosis subtypes can predict clinical outcomes and therapeutic responses in LUAD patients. Furthermore, key long non-coding RNAs (lncRNAs) were screened by single factor Cox and least absolute shrinkage and selection operator (LASSO) based on which co-expressed with the 60 ferroptosis-related genes. We then established a risk score model which included 13 LUAD ferroptosis-related lncRNAs with a multi-factor Cox regression. The risk score model showed a good performance in evaluating the outcome of LUAD. What's more, we divided TCGA-LUAD tumor samples into two groups with high- and low-risk scores and further explored the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration among different LUAD tumor risk score groups and evaluate the predictive ability of risk score for immunotherapy benefit. Our findings provide good support for immunotherapy in LUAD in the future.

Prognosis and personalized treatment prediction in lung adenocarcinoma: An in silico and in vitro strategy adopting cuproptosis related lncRNA towards precision oncology

Background: There is a rapid increase in lung adenocarcinomas (LUAD), and studies suggest associations between cuproptosis and the occurrence of various types of tumors. However, it remains unclear whether cuproptosis plays a role in LUAD prognosis. Methods: Dataset of the TCGA-LUAD was treated as training cohort, while validation cohort consisted of the merged datasets of the GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Ten studied cuproptosis-related genes (CRG) were used to generated CRG clusters and CRG cluster-related differential expressed gene (CRG-DEG) clusters. The differently expressed lncRNA that with prognosis ability between the CRG-DEG clusters were put into a LASSO regression for cuproptosis-related lncRNA signature (CRLncSig). Kaplan-Meier estimator, Cox model, receiver operating characteristic (ROC), time-dependent AUC (tAUC), principal component analysis (PCA), and nomogram predictor were further deployed to confirm the model's accuracy. We examined the model's connections with other forms of regulated cell death, including apoptosis, necroptosis, pyroptosis, and ferroptosis. The immunotherapy ability of the signature was demonstrated by applying eight mainstream immunoinformatic algorithms, TMB, TIDE, and immune checkpoints. We evaluated the potential drugs for high risk CRLncSig LUADs. Real-time PCR in human LUAD tissues were performed to verify the CRLncSig expression pattern, and the signature's pan-cancer's ability was also assessed. Results: A nine-lncRNA signature, CRLncSig, was built and demonstrated owning prognostic power by applied to the validation cohort. Each of the signature genes was confirmed differentially expressed in the real world by real-time PCR. The CRLncSig correlated with 2,469/3,681 (67.07%) apoptosis-related genes, 13/20 (65.00%) necroptosis-related genes, 35/50 (70.00%) pyroptosis-related genes, and 238/380 (62.63%) ferroptosis-related genes. Immunotherapy analysis suggested that CRLncSig correlated with immune status, and checkpoints, KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28, were linked closely to our signature and were potentially suitable for LUAD immunotherapy targets. For those high-risk patients, we found three agents, gemcitabine, daunorubicin, and nobiletin. Finally, we found some of the CRLncSig lncRNAs potentially play a vital role in some types of cancer and need more attention in further studies. Conclusion: The results of this study suggest our cuproptosis-related CRLncSig can help to determine the outcome of LUAD and the effectiveness of immunotherapy, as well as help to better select targets and therapeutic agents.

Identification of CISD1 as a Prognostic Biomarker for Breast Cancer

Background

Although CISD1 (CDGSH iron sulfur domain 1) is upregulated in many cancer types, the potential role of CISD1 in breast cancer is still unclear. The purpose of this study was to investigate its clinical significance in breast cancer.

Methods

We obtained 1109 breast cancer samples and 113 normal samples from The Cancer Genome Atlas (TCGA) and GTEx databases to demonstrate the relationship between CISD1 expression and pancancer characteristics. We analysed the relationship between CISD1 and breast cancer using the t-test and the chi-square test to evaluate the expression level of CISD1 and its clinical significance in breast cancer. The prognostic value of CISD1 in breast cancer was determined by Kaplan‒Meier and Cox regression analyses. The biological pathways were screened by gene set analysis and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA), of which the correlation between the level of immune infiltration and the expression of CISD1 in breast cancer was then analysed. Finally, we verified the conclusion by qPCR, immunohistochemistry, and CCK8.

Results

CISD1 is highly expressed in breast cancer patients. In addition, we verified a higher expression of CISD1 expressed in the BRCA (breast cancer) cell line, whereas CISD1 has a high diagnostic value, with an AUC of 0.718. Kaplan‒Meier survival and Cox regression analyses showed that high expression of CISD1 was independently associated with adverse clinical outcomes. In turn, GO and KEGG analyses showed that most genes were related to rRNA metabolic process, rRNA processing. Moreover, PCR and immunohistochemistry showed that CISD1 in breast cancer tissues was upregulated significantly, with CCK8 results showing that the proliferation of breast cancer cells decreased after CISD1 knockout.

Conclusion

A high level of CISD1 is associated with poor prognosis and immune infiltration in breast cancer.

An Inflammatory Response-Related Gene Signature Can Predict the Prognosis and Impact the Immune Status of Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) accounts for a cancer with high heterogeneity and poor prognostic outcome. Nonetheless, it is still unknown about the relation between inflammatory response-related genes (IRGs) and LUAD. This study used LASSO-Cox regression for establishing the multigene prognostic signature based on TCGA and the GSE31210 cohorts. In addition, gene set enrichment analysis (GSEA) was performed for GO and KEGG analyses. By contrast, single-sample GSEA (ssGSEA) investigated immune cell infiltration scores as well as the immune pathway activity. We also conducted qRT-PCR and IHC to evaluate prognostic gene expression at protein and mRNA levels within LUAD and adjacent healthy samples. As a result, a novel prognostic signature involving 10 IRGs was identified. Furthermore, the signature has been validated as being important in functional analysis, TME, drug sensitivity, and prognosis prediction in LUAD. Moreover, prognostic genes showed significant expression at protein and mRNA levels in LUAD compared with normal samples. The signature involving 10 IRGs could potentially predict LUAD prognosis.

Interactions between 14-3-3 Proteins and Actin Cytoskeleton and Its Regulation by microRNAs and Long Non-Coding RNAs in Cancer

14-3-3s are a family of structurally similar proteins that bind to phosphoserine or phosphothreonine residues, forming the central signaling hub that coordinates or integrates various cellular functions, thereby controlling many pathways important in cancer, cell motility, cell death, cytoskeletal remodeling, neuro-degenerative disorders and many more. Their targets are present in all cellular compartments, and when they bind to proteins they alter their subcellular localization, stability, and molecular interactions with other proteins. Changes in environmental conditions that result in altered homeostasis trigger the interaction between 14-3-3 and other proteins to retrieve or rescue homeostasis. In circumstances where these regulatory proteins are dysregulated, it leads to pathological conditions. Therefore, deeper understanding is needed on how 14-3-3 proteins bind, and how these proteins are regulated or modified. This will help to detect disease in early stages or design inhibitors to block certain pathways. Recently, more research has been devoted to identifying the role of MicroRNAs, and long non-coding RNAs, which play an important role in regulating gene expression. Although there are many reviews on the role of 14-3-3 proteins in cancer, they do not provide a holistic view of the changes in the cell, which is the focus of this review. The unique feature of the review is that it not only focuses on how the 14-3-3 subunits associate and dissociate with their binding and regulatory proteins, but also includes the role of micro-RNAs and long non-coding RNAs and how they regulate 14-3-3 isoforms. The highlight of the review is that it focuses on the role of 14-3-3, actin, actin binding proteins and Rho GTPases in cancer, and how this complex is important for cell migration and invasion. Finally, the reader is provided with super-resolution high-clarity images of each subunit of the 14-3-3 protein family, further depicting their distribution in HeLa cells to illustrate their interactions in a cancer cell.

Immunometabolism in the tumor microenvironment and its related research progress

The tumor immune microenvironment has been a research hot spot in recent years. The cytokines and metabolites in the microenvironment can promote the occurrence and development of tumor in various ways and help tumor cells get rid of the surveillance of the immune system and complete immune escape. Many studies have shown that the existence of tumor microenvironment is an important reason for the failure of immunotherapy. The impact of the tumor microenvironment on tumor is a systematic study. The current research on this aspect may be only the tip of the iceberg, and a relative lack of integrity, may be related to the heterogeneity of tumor. This review mainly discusses the current status of glucose metabolism and lipid metabolism in the tumor microenvironment, including the phenotype of glucose metabolism and lipid metabolism in the microenvironment; the effects of these metabolic methods and their metabolites on three important immune cells Impact: regulatory T cells (Tregs), tumor-associated macrophages (TAM), natural killer cells (NK cells); and the impact of metabolism in the targeted microenvironment on immunotherapy. At the end of this article,the potential relationship between Ferroptosis and the tumor microenvironment in recent years is also briefly described.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma

Background. Lung cancer is emerging as one of most deadly diseases, and the mortality rate was still high with 5-year overall survival rate less than 20%. Aging is referred as protumorigenic state, and it plays a significant role in cancer development. Methods. Molecular subtype of lung cancer was identified by consensus cluster analysis. Prognostic signature was constructed using LASSO cox regression analysis. CeRNA network was constructed to explore lncRNA-miRNA-mRNA regulatory axis. Results. A total of 27 differentially expressed aging-related genes (ARGs) were obtained in LUAD. Three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy were identified. We also developed an aging-related prognostic signature that had a better performance in predicting the1-year, 3-year, and 5-year overall survival of LUAD. Further analysis suggested a significant correlation between prognostic signature gene expression and clinical stage, immune infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also identified the lncRNA UCA1/miR-143-3p/CDK1 regulatory axis in LUAD. Conclusion. Our study identified three clusters of TCGA-LUAD patients with significant difference in prognosis, immune infiltration, chemotherapy, and targeted therapy. We also developed an aging-related prognostic signature that had a good performance in the prognosis of LUAD.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis

Background

As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.

Methods

We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.

Results

We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.

Conclusion

In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.

Identification of ATG7 as a Regulator of Proferroptosis and Oxidative Stress in Osteosarcoma

Background

Ferroptosis has gained significant attention from oncologists as a vital outcome of oxidative stress. The aim of this study was to develop a prognostic signature that was based on the ferroptosis-related genes (FRGs) for osteosarcoma patients and explore their specific role in osteosarcoma.

Methods

The training cohort dataset was extracted from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Different techniques like the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, multivariate Cox regression analyses, and the Kaplan-Meier (KM) survival analyses were utilized to develop a prognostic signature. Then, the intrinsic relationship between the developed gene signature and the infiltration levels of the immune cells was further investigated. An external validation dataset from the Gene Expression Omnibus (GEO) database was employed to assess the predictive ability of the developed gene signature. Subsequently, the specific function of potential FRG in affecting the oxidative stress reaction and ferroptosis of osteosarcoma cells was identified.

Results

A prognostic signature based on 5 FRGs (CBS, MUC1, ATG7, SOCS1, and PEBP1) was developed, and the patients were classified into the low- and high-risk groups (categories). High-risk patients displayed poor overall survival outcomes. The risk level was seen to be an independent risk factor for determining the prognosis of osteosarcoma patients (p < 0.001, hazard ratio: 7.457, 95% CI: 3.302-16.837). Additionally, the risk level was associated with immune function, which might affect the survival status of osteosarcoma patients. Moreover, the findings of the study indicated that the expression of ATG7 was related to the regulation of oxidative stress in osteosarcoma. Silencing the ATG7 gene promoted the proliferation and migration in osteosarcoma cells, suppressing the oxidative stress and ferroptosis process.

Conclusions

A novel FRG signature was developed in this study to predict the prognosis of osteosarcoma patients. The results indicated that ATG7 might regulate the process of oxidative stress and ferroptosis in osteosarcoma cells and could be used as a potential target to develop therapeutic strategies for treating osteosarcoma.

Identification of cuproptosis-related subtypes in lung adenocarcinoma and its potential significance

Cuproptosis is a novel and unique cell death mode that has attracted significant interest in recent years. Little is currently known about whether cuproptosis-related genes (CRGs) are associated with the pathophysiology and survival of patients with lung adenocarcinoma (LUAD). The present study sought to characterize the transcriptional and genetic alteration of CRGs in LUAD and its potential significance in the tumor microenvironment and predicting the prognosis of LUAD. The secondary eventual aim was to study the role of CRGs in predicting immunotherapy response and its clinical value combined with the TNM stage. We found that several CRGs, including FDX1, DLD, SLC31A1, and MTF1, were enriched in macrophages in our single-cell RNA-seq data. Three distinct molecular subtypes were identified and correlated with clinicopathological characteristics, prognosis, biological pathways, and tumor microenvironment (TME) in LUAD. We developed a cuproptosis-related gene score (CRG_score) and validated it in three independent cohorts and clinical subtypes. The low CRG_score group, characterized by a greater immune score, immunophenoscore (IPS), lower tumor immune dysfunction and exclusion (TIDE) score, and T-cell dysfunction score, had a better prognosis, suggesting that the low CRG_score group responded more favorably to immunotherapy, which was validated in the anti-PD-1/L1 immunotherapy cohort (IMvigor210). In contrast, the high CRG_score group was more sensitive to targeted therapy and chemotherapy, with a higher cancer stem cell (CSC) index and lower half-maximal inhibitory concentration (IC50) for many drugs. Given the established crosstalk between CRG_score and tumor TNM stage, we developed an accurate nomogram for clinical application of the CRG_score. Taken together, our rigorous and comprehensive examination of CRGs in LUAD identified their potential functions in TME, clinicopathological characteristics, drug sensitivity, and prognosis. These findings improve the current understanding of cuproptosis in LUAD, paving the way for more accurate prognosis assessment and tailored treatment for this patient population.

Prognostic Modeling of Lung Adenocarcinoma Based on Hypoxia and Ferroptosis-Related Genes

Background. It is well known that hypoxia and ferroptosis are intimately connected with tumor development. The purpose of this investigation was to identify whether they have a prognostic signature. To this end, genes related to hypoxia and ferroptosis scores were investigated using bioinformatics analysis to stratify the risk of lung adenocarcinoma. Methods. Hypoxia and ferroptosis scores were estimated using The Cancer Genome Atlas (TCGA) database-derived cohort transcriptome profiles via the single sample gene set enrichment analysis (ssGSEA) algorithm. The candidate genes associated with hypoxia and ferroptosis scores were identified using weighted correlation network analysis (WGCNA) and differential expression analysis. The prognostic genes in this study were discovered using the Cox regression (CR) model in conjunction with the LASSO method, which was then utilized to create a prognostic signature. The efficacy, accuracy, and clinical value of the prognostic model were evaluated using an independent validation cohort, Receiver Operator Characteristic (ROC) curve, and nomogram. The analysis of function and immune cell infiltration was also carried out. Results. Here, we appraised 152 candidate genes expressed not the same, which were related to hypoxia and ferroptosis for prognostic modeling in The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort, and these genes were further validated in the GSE31210 cohort. We found that the 14-gene-based prognostic model, utilizing MAPK4, TNS4, WFDC2, FSTL3, ITGA2, KLK11, PHLDB2, VGLL3, SNX30, KCNQ3, SMAD9, ANGPTL4, LAMA3, and STK32A, performed well in predicting the prognosis in lung adenocarcinoma. ROC and nomogram analyses showed that risk scores based on prognostic signatures provided desirable predictive accuracy and clinical utility. Moreover, gene set variance analysis showed differential enrichment of 33 hallmark gene sets between different risk groups. Additionally, our results indicated that a higher risk score will lead to more fibroblasts and activated CD4 T  cells but fewer myeloid dendritic cells, endothelial cells, eosinophils, immature dendritic cells, and neutrophils. Conclusion. Our research found a 14-gene signature and established a nomogram that accurately predicted the prognosis in patients with lung adenocarcinoma. Clinical decision-making and therapeutic customization may benefit from these results, which may serve as a valuable reference in the future.

A ferroptosis-related gene signature and immune infiltration patterns predict the overall survival in acute myeloid leukemia patients

Targeted therapy for acute myeloid leukemia (AML) is an effective strategy, but currently, there are very limited therapeutic targets for AML treatment. Ferroptosis is strongly related to drug resistance and carcinogenesis. However, there are few reports about ferroptosis in AML. This article explores the relationship between ferroptosis-related gene (FRG) expression and prognosis in AML patients from the FerrDb and the Cancer Genome Atlas (TCGA) databases. The ferroptosis-related gene ARNTL was observed to have high expression and poor prognosis in AML. Receiver operating characteristic curve (ROC) analysis revealed the predictive accuracy of the signature. The area under the time-dependent ROC curve (AUC) was 0.533 at one year, 0.619 at two years, and 0.622 at three years within the training cohort. Moreover, we found that the ARNTL expression is closely associated with tumor-infiltrating immune cells like the macrophages and NK cells. Inhibiting the ARNTL expression suppressed colony formation and induced ferroptosis in AML cells. Overall, the survival prediction model constructed based on ARNTL accurately predicted the survival in AML patients, which could be a potential candidate for diagnosing and treating AML.

A pan-cancer analysis reveals the genetic alterations and immunotherapy of Piezo2 in human cancer

Background: Piezo2 is a transmembrane-spanning ion channel protein implicated in multiple physiological processes, including cell proliferation and angiogenesis in many cell types. However, Piezo2 was recognized as representing a double-edged sword in terms of tumor growth. The prognostic and immunotherapeutic roles of Piezo2 in pan-cancer have not been reported.

Methods: In this study, several databases available including the UCSC Xena database, HPA, TIDE, GSEA, and cBioportal were used to investigate the expression, alterations, associations with immune indicators, and prognostic roles of Piezo2 across pan-cancer. R software and Perl scripts were used to process the raw data acquired from the UCSC Xena database.

Results: Based on processed data, our results suggested that Piezo2 expression levels were tissue-dependent in different tumor tissues. Meanwhile, the survival analysis reflected that patients suffering from KIRC, LUAD, and USC with high Piezo2 expression had good OS, while those suffering from KIRP and SARC with high Piezo2 expression had poor OS. In addition, our results showed that Piezo2 expression was associated with the infiltration of CD4⁺ T memory cells, mast cells, and dendritic cells. These results suggested that Piezo2 may involve tumor progression by influencing immune infiltration or regulating immune cell function. Further analysis indicated that Piezo2 could influence TME by regulating T-cell dysfunction. We also found that gene mutation was the most common genetic alteration of Piezo2. The GSEA analysis revealed that Piezo2 was associated with calcium ion transport, the activation of the immune response, antigen processing and presentation pathways.

Conclusion: Our study showed the expression and prognostic features of Piezo2 and highlighted its associations with genetic alterations and immune signatures in pan-cancer. Moreover, we provided several novel insights for further research on the therapeutic potential of Piezo2.

Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes

Background: Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited.

Methods: First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and we classified these samples into the high- and low-risk groups in the light of the median risk score. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis were performed to assess the accuracy of this signature. Significantly, the data from CPTAC was used to validate the prediction model externally. Furthermore, we evaluated the immune microenvironment in this model via single-sample gene set enrichment analysis (ssGSEA).

Results: Among the 150 FRGs, 6 differentially expressed genes (DEGs) based on TCGA had a relationship with the prognosis of EC patients, namely, TP53, AIFM2, ATG7, TLR4, PANX1 and MDM2. The survival curve indicated a higher survival probability in the low-risk group. Moreover, the FRGs-based signature acted well in the prediction of overall survival (OS). The results of external verification confirmed the prediction model we established. Finally, ssGSEA revealed significant differences in the abundance of 16 immune cells infiltration and the activity of 13 immune functions between different risk groups.

Conclusion: We identified a novel ferroptosis-related gene signature which could concisely predict the prognosis and immunotherapy in EC patients.

Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma

Programmed cell death (PCD) is a process that regulates the homeostasis of cells in the body, and it plays an important role in tumor immunity. However, the expression profile and clinical characteristics of PCD-related genes remain unclear. In this study, we comprehensively analysed the PCD genes with the tumor microenvironment (TME), drug sensitivity, immunothearapy response, and evaluated their prognostic value through systematic bioinformatics methods.We identified 125 PCD-related regulatory factors, which were expressed differently in lung adenocarcinoma (LUAD) and normal lung tissues. 32 PCD related prognostic genes associated with LUAD were identified by univariate Cox analysis. 23 PCD-related gene signature was constructed, and all LUAD patients in the Cancer Genome Atlas (TCGA) dataset were stratified as low-risk or high-risk groups according to the risk score. This signature had a powerful prognostic value, which was validated in three independent data sets and clinical subtypes. Additionally, it has unique properties in TME. Further analysis showed that different risk groups have different immune cell infiltration, immune inflammation profile, immune pathways, and immune subtypes. In addition, the low-risk group had a better immunotherapy response with higher levels of multiple immune checkpoints and lower Tumor immune dysfunction and exclusion (TIDE) score, while the high-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In short, this is the first model to predict the prognosis and immunological status of LUAD patients based on PCD-related genes. It may be used as a predictor of immunotherapy response to achieve customized treatment of LUAD.

Multiomics Integrated Analysis Identifies SLC24A2 as a Potential Link between Type 2 Diabetes and Cancer

Background

So far, type 2 diabetes (T2D) is considered as an independent risk factor for various cancers, but the underlying mechanism remains unclear. Methods. SLC24A2 was first identified as a key gene strongly associated with fasting plasma glucose (FPG). Then, overlapped differentially expressed genes (DEGs) between T2D verse control and SLC24A2-high verse SLC24A2-low were extracted and imported into weighted correlation network analysis. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for functional enrichment analysis of DEGs. Least absolute shrinkage and selection operator was utilized to build a T2D prediction model. Timer and K-M plotters were employed to find the expression and prognosis of SLC24A2 in pan cancer.

Results

Interestingly, both DEGs between T2D verse control and SLC24A2-high verse SLC24A2-low enriched in cancer-related pathways. Moreover, a total of 3719 overlapped DEGs were divided into 8 functional modules. Grey module negatively correlated with T2D and FPG and was markedly involved in ribosome biogenesis. Ten SLC24A2-related genes (RRP36, RPF1, GRWD1, FBL, EXOSC5, BCCIP, UTP14A, TWISTNB, TBL3, and SKIV2L) were identified as hub genes, based on which the LASSO model accurately predicts the occurrence of T2D (AUC = 0.841). In addition, SLC24A2 was only expressed in islet β cells and showed abnormal expression in 17 kinds of cancers and significantly correlated with the prognosis of 10 kinds of cancers.

Conclusion

Taken together, SLC24A2 may link T2D and cancer by influencing the ribosome function of islet β cells and play different prognostic roles in different cancers.

Construction and validation of a robust ferroptosis-associated gene signature predictive of prognosis in lung adenocarcinoma

To construct and validate a ferroptosis-associated signature predictive of prognosis in lung adenocarcinoma (LUAD), and systematically evaluate the underlying molecular connections in cancer biology.We retrieved mRNAs sequencing profiles of LUAD from the cancer genome atlas (TCGA) data portal and clinical information from the cBio Cancer Genomics Portal. The differentially expressed ferroptosis-associated genes (DEFAGs) were screened between normal samples and LUAD by packages "limma" in R. Then the total TCGA cohort was randomly divided into training set and testing set. Based on the training set, a DEFAG signature was built and further validated in the test set, the total TCGA cohort and other independent cohorts from the gene expression omnibus data portal. A nomogram was constructed and validated, and the correlation between high-risk group and cancer biology was further evaluated.We initially identified 68 DEFAGs from TCGA cohort. A 6 DEFAG signature was built and further validated in the test set, the total TCGA cohort and other 2 independent cohorts including GSE31210 and GSE72094 from gene expression omnibus data portal. Further exploration indicated that high-risk group combined with TP53 mutation harbored the most unfavorable prognosis while low-risk group with TP53 wild-type status had the most favorable survival advantage over other groups. Moreover, high-risk group was associated with higher cancer stemness, tumor mutation burden, and CD274 (programmed cell death 1 ligand 1) expression.We constructed a robust ferroptosis-associated gene signature and a nomogram predictive of prognosis in LUAD, and provided a new perspective on associations between ferroptosis and cancer.

Identification and Validation of a Prognostic Immune-Related Gene Signature in Esophageal Squamous Cell Carcinoma

Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.

Functions, Roles, and Biological Processes of Ferroptosis-Related Genes in Renal Cancer: A Pan-Renal Cancer Analysis

Ferroptosis is a cell death process discovered in recent years, highly related to cancer, acute kidney injury, and other diseases. In this study, a pan-renal cancer analysis of ferroptosis-associated genes in renal cancer was performed to construct a multigene joint signature for predicting prognosis in renal cancer patients. First, gene expression profiles were downloaded from the TCGA and GTEx databases to search for genes significantly associated with renal cancer prognosis through differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis. Thereafter, the gene-set enrichment analysis (GSEA) was used to identify the biological processes in which ferroptosis-associated genes might be involved. Weighted gene co-expression network analysis resulted in 4,434 differentially expressed genes (DEGs) and 42 co-expression modules, among which ferroptosis-related genes were distributed in 11 gene modules. The survival analysis screening resulted in three DEGs associated with renal cancer prognosis, namely SLC7A11, HMOX1, and MT1G. Specifically, SLC7A11 and HMOX1 were upregulated in renal cancer tissues, while MT1G was downregulated. Receiver operating characteristic (ROC) curves, combined with Kaplan–Meier and Cox regression analysis, revealed that high expression of SLC7A11 was a prognostic risk factor for four different renal cancers, that low expression of HMOX1 was a poor prognostic marker for patients, and that increased expression of MT1G increased the prognostic risk for three additional classes of renal cancer patients, except for renal papillary cell carcinoma. The GSEA results showed that the ferroptosis-related genes from these screens were mainly associated with signaling pathways related to tumor progression and tumor immunity. This study provides potential biological markers for prognosis prediction in renal cancer patients with different subtypes, and these results imply that ferroptosis is highly associated with renal carcinogenesis progression.

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Background

In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA).

Results

Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas.

Conclusions

These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

Immune infiltration and a ferroptosis-related gene signature for predicting the prognosis of patients with cholangiocarcinoma

Cholangiocarcinoma (CHOL) is a digestive tract tumor with high malignancy and poor prognosis and is extremely challenging to treat. At present, induced cell death holds great promise in tumor therapy. Ferroptosis is a recently proposed pattern of programmed cell death, and numerous studies have shown that it is intimately involved in tumors. However, the roles of differentially expressed ferroptosis-related genes (DEFRGs) in CHOL have not been investigated. Our study was based on The Cancer Genome Atlas (TCGA) database, and DEFRGs were obtained to construct a prognostic riskScore model of CHOL by univariate and multivariate Cox regression analyses. Subsequently, the model was evaluated by nomogram construction, survival analysis, receiver operating characteristic (ROC) analysis, and exploration of the immune microenvironment. The mRNA and protein expression levels of each gene in the model were validated by the Gene Expression Omnibus (GEO) database, quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) staining. Our study found that the construction of a nomogram confirmed the predictive value of the model for overall survival (OS), and it was confirmed to have high diagnostic value by ROC analysis. Our experimental results were almost consistent with our bioinformatics results. In conclusion, we found that the prognostic model showed extremely high diagnostic and prognostic value and could predict the possibility of immunotherapy, thus providing a new direction for individualized treatment of patients with CHOL.

Transmembrane p24 trafficking protein 2 regulates inflammation through the TLR4/NF-κB signaling pathway in lung adenocarcinoma

Background

To investigate the role of transmembrane p24 trafficking protein 2 (TMED2) in lung adenocarcinoma (LUAD) and determine whether TMED2 knockdown could inhibit LUAD in vitro and in vivo.

Methods

TIMER2.0, Kaplan-Meier plotter, gene set enrichment analysis (GSEA), Target Gene, and pan-cancer systems were used to predict the potential function of TMED2. Western blotting and immunohistochemistry were performed to analyze TMED2 expression in different tissues or cell lines. The proliferation, development, and apoptosis of LUAD were observed using a lentivirus-mediated TMED2 knockdown. Bioinformatics and western blot analysis of TMED2 against inflammation via the TLR4/NF-κB signaling pathway were conducted.

Results

TMED2 expression in LUAD tumor tissues was higher than that in normal tissues and positively correlated with poor survival in lung cancer and negatively correlated with apoptosis in LUAD. The expression of TMED2 was higher in tumors or HCC827 cells. TMED2 knockdown inhibited LUAD development in vitro and in vivo and increased the levels of inflammatory factors via the TLR4/NF-κB signaling pathway. TMED2 was correlated with TME, immune score, TME-associated immune cells, their target markers, and some mechanisms and pathways, as determined using the TIMER2.0, GO, and KEGG assays.

Conclusions

TMED2 may regulate inflammation in LUAD through the TLR4/NF-κB signaling pathway and enhance the proliferation, development, and prognosis of LUAD by regulating inflammation, which provide a new strategy for treating LUAD by regulating inflammation.

Identification of a ferroptosis-related gene signature for predicting the prognosis of cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) is a highly intractable malignancy with poor prognosis. Ferroptosis, a newly explored type of programmed cell death, plays a critical role in the initiation and progression of a tumor. Herein, we aimed to identify a ferroptosis-related risk model to evaluate the prognosis of CCA.

METHODS

Differentially expressed genes (DEGs) were retrieved from three GEO cohorts. Univariate and LASSO analysis were employed to build a ferroptosis-related gene signature. Next, the predictive value was assessed in a training and a validation cohort. Metascape Online analysis, ESTIMATE and CIBERSORT algorithms, and ssGSEA were employed to perform the functional analysis between different risk groups. Finally, the expression of prognostic genes was validated with RT-qPCR.

RESULTS

We identified 51 differentially expressed ferroptosis genes and established the prognostic signature containing five ferroptosis-related genes. The K-M curves and the ROC curves revealed a favorable predictive efficacy of the prognostic signature. Functional enrichment analysis indicated that immune-related responses were greatly enriched between different risk groups. Five prognostic genes were also differentially expressed in CCA cell lines.

CONCLUSIONS

We developed a novel ferroptosis-related gene signature for CCA with high predictive accuracy. The analysis of the immune infiltration status may provide a potential therapeutic alternative to CCA.