Search

Background Lung cancer is one of the most common malignant tumors of the respiratory system in the world. According to the traditional histological classification, it can be divided into many subtypes. In recent years, the incidence rate of lung adenocarcinoma (LUAD) has been rising rapidly. In this study, we identified the biomarkers related to the prognosis of LUAD through the protein-protein interaction (PPI) network analysis, gene set variation analysis (GSVA) and the "CancerSubtypes" software package in R, with a view to having a positive impact on the future treatment and new drug development. Method We obtained the relevant data needed for the study from The Cancer Genome Atlas (TCGA) database and the GEO database. Through GSVA analysis, the gene expression matrix was further transformed into the gene set expression matrix for subsequent research. Then, we applied the package "CancerSubtypes" in R to transform the samples into different subtypes, and established a LUAD-related prognosis model based on the differential expression gene sets (DEGSs) among the subtypes. Finally, we performed functional and pathway enrichment analysis together with PPI network analysis of genes from prognosis related DEGSs. Results A total of 600 LUAD samples were obtained from TCGA database, including 541 tumor samples and 59 normal samples. We screened 507 tumor samples for further classification, including 166 cases of subtype 1, 138 cases of subtype 2, and 203 cases of subtype 3. Subsequently, we identified 63 DEGSs and constructed the prognostic characteristics of LUAD with 4 of them (“T_GSE24634_TREG_VS_TCONV_POST_DAY5_IL4_CONVERSION_UP” “T_GSE25088_WT_VS_STAT6_KO_MACROPHAGE_DN” “T_GSE45365_HEALTHY_VS_MCMV_INFECTION_CD11B_DC_DN” and “T_HALLMARK_MYC_TARGETS_V2”). Finally, we established the corresponding PPI network with 6 subnets, and identified 15 core proteins including CCNB2, KIF2C, TPX2, PES1, BRIX1, NIP7, PSMB4, PSMD12, PSMC3, MPHOSPH10, WDR43, POLA1, MCM4, PAICS and GART. Conclusions In this study, we identified four gene sets related to the prognosis of LUAD and obtained 15 core proteins. This study could provide relevant theoretical basis and guidance for the update of treatment methods and the development of new drugs, related to LUAD and other cancers.

Background: Lung cancer is a common malignant tumor, which is divided into many subtypes. Lung adenocarcinoma (LUAD) is a most common subtype. More and more studies have confirmed that ferroptosis is involved in the occurrence and development of lung cancer. In this paper, we studied the prognostic ferroptosis-related long noncoding RNAs (FRLs) to build a LUAD-related prognosis model. Methods: We first downloaded the relevant data of 598 patients from the TCGA-LUAD dataset of The Cancer Genome Atlas, and then randomly divided them into training group and testing group in a 1:1 ratio. After that, we used Pearson correlation analysis and univariate Cox regression analysis to determine the FRLs related to prognosis. Then, according to the least absolute shrinkage and selection operator (LASSO) algorithm, the risk model was constructed using the optimized prognostic FRLs subset. We further used the receiver operating characteristic (ROC) curve and survival analysis to evaluate the performance of our model, meanwhile, Cox regression analysis was performed to analyze the risk score (RS). Finally, we also carried out gene set enrichment analysis (GSEA) , and differential analysis of immune-related genes and m6a-related genes. Results: In this study, we identified a total of 34 FRLs associated with the prognosis of lung adenocarcinoma, and established a prognostic model with 7 of them. Kaplan-Meier analysis showed that relevant characteristics of patients in high-risk group were correlated with poorer prognosis. The AUC value of our model was quite ideal, indicating that it could accurately predict the prognosis of LUAD patients. Further GSEA results showed that FRLs of individuals in high-risk groups were mainly enriched in cell cycle and related regulatory pathways, while those in low-risk groups were mainly enriched in immune-related pathways. We also employed immune function analysis and immune checkpoints expression analysis, and found that CCR, check-point, HLA, T cell co−inhibition, T cell co−stimulation and Type II IFN Reponse had significant differences between two groups, while most immune checkpoints had higher expression levels in low-risk groups. Conclusion: Our research has proved that FRls could indeed be used as a prognostic feature to build a prognostic model of lung adenocarcinoma. On the basis of this theory, it is of great significance and value to further study new treatment methods.

Background: TME (Tumor microenvironment) plays a key role in the occurrence and development of lung cancer. Further research on TME will provide more comprehensive insights into relevant prognostic biomarkers and potential therapeutic targets. The purpose of this study is to develop a better prognosis model to predict the OS (overall survival) time of LUAD patients by identifying the TME component in lung cancer (especially lung adenocarcinoma) and comparing it with previous similar research results. Methods: The original LUAD related data was from TCGA (the Cancer Genome Atlas). The DEGs (differentially expressed genes) related to TME in tumor tissues and normal tissues were calculated respectively. Then, NMF (nonnegative matrix factorization) clustering was applied to identify different subtypes. Univariate Cox regression analysis and lasso regression analysis were performed to screen genes with prognostic significance to construct the prognostic characteristics. Finally, ROC (receiver operating characteristic) curve and DCA (decision curve analysis) were used to verify the model both internally and externally. Results: Finally, we constructed a LUAD prognosis model containing five TME related genes (including C1QTNF6, PLEK2, FURIN, TM6SF1 and IGF2BP1). In our model, the survival time of high-risk group was indeed shorter. The prediction accuracy of the model was further verified by an independent cohort (GSE13213) in GEO (the Gene Expression Omnibus). In addition, we also integrated relevant clinical factors and drew a prognosis nomograph. The results showed that the patients in the high-risk group had less immune cell infiltration, more fibroblasts in the tissues, and poorer prognosis.

Background: Researches have shown that tumor microenvironment (TME) can regulate the proliferation and metastasis of solid tumors, and has an important impact on the immunotherapy of solid tumors. Based on the transcriptome data of colon adenocarcinoma (COAD), we studied the prognostic role of TME related genes, established and verified the prognostic model of TME related signatures. Methods: We Obtained relevant data from TCGA (the Cancer Genome Atlas) to calculate TME related genes which expressed differentially in distinct tissues. Then, we employed NMF (nonnegative matrix factorization) clustering method to get different clusters. Univariate Cox regression and Lasso regression analysis were used to screen genes with prognostic significance, the prognosis model and corresponding nomogram were completed according to the risk genes. Finally, the ROC (Receiver operating characteristic) and the DCA (Decision curve analysis) curves were used for internal and external verification. Results: Our research obtained a gene panel consisting of 15 TME related genes (FOXD1, FSCN1, PRAME, SOX12, ATP6V1C2, EPHB4, CD36, BANK1, NOL3, DPP7, FAM24B, AEN, CCNF, PSRC1 and F2RL2). The established model clearly showed the survival time of patients in different risk groups based on the former genes. Kaplan Meier survival analysis showed that the survival time of high-risk group was significantly shorter than that of low-risk group. The ROC curve’s value of AUC with this prognostic signature was basically over 0.65. In addition, we further validated the model with GSE39582 from GEO (the Gene Expression Omnibus) database. Univariate analysis and multivariate Cox regression analysis proved that the risk score we established in this study was an independent risk factor affecting the prognosis of COAD patients. Conclusion: The signature set we established could more accurately and effectively evaluate the prognosis of colon cancer patients. And the analysis of TME could also provide new opportunities for the diagnosis, treatment and prognosis of patients with COAD.

Cuproptosis is a special form of cell death. Bladder cancer, especially Bladder Urothelial Carcinoma (BLCA), is one of the ten most common cancer types in the world. So far, the potential role of cuproptosis in BLCA is unclear. In the present study, we systematically evaluated the copper poisoning mediated patterns of 509 BLCA samples based on 19 validated copper poisoning related genes (CRGs) using data downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Kaplan Meier method was used to analyze the overall survival rate (OS) of different risk groups. Gene Set Variation Analysis (GSVA) was used to study the functional differences between different cuproptosis clusters (CRG clusters). Single sample gene set enrichment analysis (ssGSEA) was used to explore the potential relationship between CRGclusters and immune status. We used GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis to study various cellular biochemical processes. Finally, we established a prognostic model to predict patients’ survival outcomes and to further analyze the correlation between the predictive characteristics of BLCA patients and various treatment response. In this study, we derived two CRGclusters and geneclusters, and also established a model to quantify the risk score of individual BLCA patients, which was found to be closely associated with various clinical characteristics and could precisely predict the prognosis of BLCA patients. We believe that through our study, quantitative analysis of cuproptosis mediated patterns in a single sample may help to improve our understanding of the multi-omics characteristics of BLCA and guide future treatment regimens.