Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Yu P
PROVIDER: S-EPMC8256358 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Growing studies have implicated the association of ubiquitination-related genes (UbRGs) with the cancer progression and the long-term survival of patients. However, the prognostic values of UbRGs in lung adenocarcinoma (LUAD) have not been investigated. Our study aimed to establish a ubiquitination-related model for prognosis prediction and internal mechanism investigation. The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from TCGA and GEO datasets. Differentially expressed genes (DEGs) were screened between LUAD specimens and nontumor specimens. Kaplan-Meier analysis and univariate assays were carried out on DEGs to preliminarily screen survival-related UbRGs. Then, the LASSO Cox regression model was applied to develop a multigene signature, which was then demonstrated in two GEO datasets by the use of Kaplan-Meier, ROC, and Cox analyses. We estimated the immune cell infiltration in tumor microenvironment via CIBERSORT and immunotherapy response through the TIDE algorithm. In this study, a total of 71 ubiquitination-related DEGs were identified. Nine UbRGs, including TUBA4A, TRIM2, PLK1, ARRB1, TRIM58, PLK1, ARRB1, CCNB1, TRIM6, PTTG1, and CCT2, were included to establish a risk model, which was validated in TCGA and GEO datasets. The multivariate assays demonstrated that the 9-UbRGs signature was a robust independent prognostic factor in the overall survival of LUAD patients. The abundance of CD8 T cells, activated CD4 T memory cells, resting NK cells and macrophages was higher in the high-risk group, and the TMB of high-risk group was statistically higher than the low-risk group. Multiple drugs approved by FAD, targeting UbRGs, were available for the treatment of LUAD. Overall, we identified a nine ubiquitination-related gene signature, and the signature may be applied to be a potential biomarker for CD8 T cells response and clinical responses to immune checkpoint inhibitors for LUAD.
| S-EPMC9398812 | biostudies-literature
Project description:We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.
| S-EPMC6914845 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.
| S-EPMC9013292 | biostudies-literature
Project description:The existence of tumor heterogeneity and complex carcinogenic mechanisms in lung adenocarcinoma (LUAD) make the most commonly used TNM staging system unable to well-interpret the prognosis of patients. Using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA) database, we constructed an immune signature based on a multivariate Cox analysis (stepwise model). We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients according to the pRRophetic algorithm. Gene-set variation analysis (GSVA) was used to reveal pathway enrichment between groups. Moreover, immune microenvironment landscape was described by single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT and systematically correlated with genomic of these patients. A prognostic nomogram combining the immune signature and TNM stage to predict the prognosis was developed by multivariate Cox regression. The novel signature with four immune-related genes (MAL, MS4A1, OAS1, and WFDC2) had good robustness, which can accurately distinguish between high- and low-risk patients. Compared with low-risk patients, high-risk patients with a worse prognosis (5-year OS: 46.5 vs. 59.4%, p = 0.002) could benefit more from immunotherapy and the application of common chemotherapeutic agents such as cisplatin and paclitaxel (Wilcoxon test, all p < 0.05). There were significant differences in tumor immune microenvironment and metabolic pathways between the two groups. Additionally, the constructed nomogram had reliable predictive performance with the C-index of 0.725 (95% CI = 0.668-0.781) in the development set (n = 500), 0.793 (95% CI = 0.728-0.858) in the internal validation set (n = 250) and 0.679 (95% CI = 0.644-0.714) in the external validation set (n = 442). The corresponding calibration curves also showed good consistency. To sum up, we developed an immune-related gene signature and comprehensively evaluated LUAD immune landscape and metabolic pathways. Effective differentiation of high- and low-risk patients and accurate construction of nomogram would be helpful to the development of individualized treatment strategies.
| S-EPMC7424034 | biostudies-literature
Project description:BackgroundDERL3 has been implicated as an essential element in the degradation of misfolded lumenal glycoproteins induced by endoplasmic reticulum (ER) stress. However, the correlation of DERL3 expression with the malignant phenotype of lung adenocarcinoma (LUAD) cells is unclear and remains to be elucidated. Herein, we investigated the interaction between the DERL3 and LUAD pathological process.MethodsThe Cancer Genome Atlas (TCGA) database was utilized to determine the genetic alteration of DERL3 in stage I LUAD. Clinical LUAD samples including carcinoma and adjacent tissues were obtained and were further extracted to detect DERL3 mRNA expression via RT-qPCR. Immunohistochemistry was performed to evaluate the protein expression of DERL3 in LUAD tissues. The GEPIA and TIMER website were used to evaluate the correlation between DERL3 and immune cell infiltration. We further used the t-SNE map to visualize the distribution of DERL3 in various clusters at the single-cell level via TISCH database. The potential mechanisms of the biological process mediated by DERL3 in LUAD were conducted via KEGG and GSEA.ResultsIt was indicated that DERL3 was predominantly elevated in carcinoma compared with adjacent tissues in multiple kinds of tumors from the TCGA database, especially in LUAD. Immunohistochemistry validated that DERL3 was also upregulated in LUAD tissues compared with adjacent tissues from individuals. DERL3 was preliminarily found to be associated with immune infiltration via the TIMER database. Further, the t-SNE map revealed that DERL3 was predominantly enriched in plasma cells of the B cell population. It was demonstrated that DERL3 high-expressed patients presented significantly worse response to chemotherapy and immunotherapy. GSEA and KEGG results indicated that DERL3 was positively correlated with B cell activation and unfolded protein response (UPR).ConclusionOur findings indicated that DERL3 might play an essential role in the endoplasmic reticulum-associated degradation (ERAD) process in LUAD. Moreover, DERL3 may act as a promising immune biomarker, which could predict the efficacy of immunotherapy in LUAD.
| S-EPMC9585215 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Most patients are diagnosed at an advanced stage, therefore it is crucial to identify novel prognostic biomarkers for LUAD. As important regulatory cells, inducible regulatory T cells (iTregs) play a vital role in immune suppression and are important for the maintenance of immune homeostasis. This study explored the prognostic value and therapeutic effects of iTreg-related genes in LUAD. Data for LUAD patients, including immune infiltration data, RNA sequencing data, and clinical features, were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and Tumor Immune Single-cell Hub 2 databases. Immune-related subgroups with different infiltration patterns and iTreg-related genes were identified through univariate and multivariate Cox regression analyses and weighted correlation network analysis. Functional enrichment analyses were performed to explore the underlying mechanisms of iTreg-related genes. A prognostic risk signature was constructed using Cox regression analysis with the least absolute shrinkage and selection operator penalty. The ESTIMATE algorithm was applied to determine the immune status of LUAD patients. We applied the constructed signature to predict chemosensitivity and performed single-cell RNA sequencing analysis. The infiltration of iTregs was identified as an independent factor for predicting patient outcomes. We constructed a prognostic signature based on seven iTreg-related genes (GIMAP5, SLA, MS4A7, ZNF366, POU2AF1, MRPL12, and COL5A1), which was applied to subdivide patients into high- and low-risk subgroups. Our results revealed that patients in the iTreg-related low-risk subgroup had a better prognosis and possibly greater sensitivity to traditional chemotherapy. Our study provides a novel iTreg-related signature to elucidate the mechanisms underlying LUAD prognosis and promote individualized chemotherapy treatment.
| S-EPMC10823293 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is characterized by high infiltration and rapid growth. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cell-related genes in LUAD. Signature genes were screened out from 166 stem cell-related genes according to the least absolute shrinkage operator (LASSO) and subsequently multivariate Cox regression analysis, and then established risk model. Immune infiltration and nomogram model were used to evaluate the clinical efficacy of signature. A signature consisting of 10 genes was used to dichotomize the LUAD patients into two groups (cutoff, 1.314), and then validated in GSE20319 and GSE42127. There was a significant correlation between signature and clinical characteristics. Patients with high-risk had a shorter overall survival. Furthermore, significant differences were found in multiple immune cells between the high-risk group and low-risk group. A high correlation was also reflected between signature and immune infiltration. What's more, the signature could effectively predict the efficacy of chemotherapy in patients with LUAD, and a nomogram based on signature might accurately predict the prognosis of patients with LUAD. The signature-based of stem cell-related genes might be contributed to predicting prognosis of patients with LUAD.
| S-EPMC7814011 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD), a malignancy with high incidence and mortality rates worldwide, contains multiple genomic and epigenomic abnormalities. And the useful tumor markers associated with these abnormalities need further investigation. Whereas apoptosis is a form of programmed cell death, the expression of apoptosis-related genes in LUAD and its relationship with prognosis is unclear. In the present study, we identified 64 differentially expressed apoptosis-related genes (DEARGs) that were differentially expressed between LUAD tissue and normal lung tissue. Based on these DEARGs, all LUAD cases were classified into two subtypes using The Cancer Genome Atlas (TCGA) cohort to assess the prognostic value of apoptosis-related genes for survival. An 11-gene signature was established by applying the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression method to construct a multigene prediction model and classify all LUAD patients in the TCGA cohort into high or low AS-score groups. Patients in the low AS-score group had significantly higher survival and prognosis than those in the high AS-score group. Taking the median risk score of the AS-score, LUAD patients in the GSE68465 cohort were divided into two risk groups, low and high. The overall survival (OS) time was longer in the low AS-score group. Combined with clinical characteristics, the AS-score was an independent predictor of LUAD patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that the differential genes between the two groups were mainly enriched in cellular immunity. Further analysis revealed higher immune checkpoint protein expression and higher tumor mutational burden (TMB) in the high AS-score group, suggesting better efficacy of immunotherapy in the high AS-score group than the low AS-score group. And the high AS-score group was better in chemotherapy and targeted therapy efficiency. In conclusion, the AS-score constructed based on apoptosis-related genes can predict the prognosis of LUAD patients and provide some guidance for the antitumor treatment of LUAD patients.
| S-EPMC9094710 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.
| S-EPMC8183047 | biostudies-literature
Project description:Background: Although a majority of early-stage lung adenocarcinoma (es-LUAD) patients have a favorable prognosis, there are still some cases with a risk of recurrence and metastasis. Cuproptosis is a new form of death that differs from other programmed cell death. However, no study has been reported for setting a prognostic model of es-LUAD using cuproptosis pattern-related genes. Methods: Using multiple R packages, the data from the GEO database was processed, and es-LUAD patients was classified into two patterns based on cuproptosis-related genes. Key differentially expressed genes (DEGs) in the two patterns were screened to construct a prognostic signature to assess differences in biological processes and immunotherapy responses in es-LUAD. Tumor microenvironment (TME) in es-LUAD was analyzed using algorithms such as TIMER and ssGSEA. Then, a more accurate nomogram was constructed by combining risk scores with clinical factors. Results: Functional enrichment analysis revealed that DEGs in two patterns were correlated with organelle fission, nuclear division, chromosome segregation, and cycle-related pathways. Univariate Cox regression and Lasso-Cox regression analyses identified six prognostic genes: ASPM, CCNB2, CDC45, CHEK1, NCAPG, and SPAG5. Based on the constructed model, we found that the high-risk group patients had higher expression of immune checkpoints (CTLA4, LAG3, PD-L1, TIGIT and TIM3), and a lower abundance of immune cells. Lastly, the nomogram was highly accurate in predicting the 1-, 3-, and 5-year survival status of patients with es-LUAD based on risk scores and clinical factors. Conclusion: The cuproptosis pattern-related signature can serve as a potential marker for clinical decision-making. It has huge potential in the future to guide the frequency of follow-up and adjuvant therapy for es-LUAD patients.
| S-EPMC9515444 | biostudies-literature