A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma

Wu, Jie; Li, Lan; Zhang, Huibo; Zhao, Yaqi; Zhang, Haohan; Wu, Siyi; Xu, Bin

doi:10.1038/s41388-021-01853-y

Cited by 149 publications

(116 citation statements)

References 49 publications

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“…Immunotherapy is a novel treatment for LUAD. Identifying a novel method that can classify patients who might benefit from immunotherapy remains a tremendous challenge ( Camidge et al, 2019 ; Yu et al, 2019 ; Yu et al, 2020 ; Wu et al, 2021 ). Recent studies have demonstrated numerous differences between the GA and the immune microenvironment; the GA can participate in the activation of the NLRP3 inflammasome, sensing nucleic acids, and influencing interferon (IFN)-inducible GTPases ( Vey et al, 1994 ; Gonugunta et al, 2017 ; Chen and Chen, 2018 ), highlighting its potential in predicting immune-related anticancer therapy.…”

Section: Discussionmentioning

confidence: 99%

Prognosis and Immunotherapy Response With a Novel Golgi Apparatus Signature-Based Formula in Lung Adenocarcinoma

Jiang

Ouyang

Zhang

et al. 2022

Front. Cell Dev. Biol.

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The Golgi apparatus (GA) is a cellular organelle that participates in the packaging, modification, and transport of proteins and lipids from the endoplasmic reticulum to be further fabricated before being presented to other cellular components. Recent studies have demonstrated that GA facilitates numerous cellular processes in cancer development. Therefore, this study aimed to establish a novel lung adenocarcinoma (LUAD) risk evaluation model based on GA gene signatures. In this study, we used TCGA-LUAD (n = 500) as the training cohort and GSE50081 (n = 127), GSE68465 (442), and GSE72094 (398) as the validation cohorts. Two immunotherapy datasets (GSE135222 and GSE126044) were also obtained from a previous study. Based on machine algorithms and bioinformatics methods, a GA gene-related risk score (GARS) was established. We found that the GARS independently predicted the prognosis of LUAD patients and remained effective across stages IA to IIIA. Then, we identified that the GARS was highly correlated with mutations in P53 and TTN. Further, this study identified that GARS is related to multiple immune microenvironmental characteristics. Furthermore, we investigated GSE135222 and GSE126044 and found that a lower GARS may be indicative of an improved therapeutic effect of PD-1/PD-L1 therapy. We also found that high GARS may lead to a better response to multiple anticancer drugs. Finally, we established a nomogram to better guide clinical application. To our knowledge, this is the first study to demonstrate a novel GA signature-based risk score formula to predict clinical prognosis and guide the treatment of LUAD patients.

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Section: Discussionmentioning

confidence: 99%

Prognosis and Immunotherapy Response With a Novel Golgi Apparatus Signature-Based Formula in Lung Adenocarcinoma

Jiang

Ouyang

Zhang

et al. 2022

Front. Cell Dev. Biol.

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“…We found that the normal tissues had increased stromal and reduced immune scores as opposed to the OC tissues, which suggested that the remodeled ECM and alternate immune cells could have a crucial function in OC tumorigenesis. Previous studies show that high stromal and immune scores are linked to dismal survival in gastric cancer ( 24 ), and better survival in lung adenocarcinoma ( 3 ). However, we discovered that an elevated immune score was a significant factor for dismal prognosis and an elevated stromal score was linked to good OS in OC patients.…”

Section: Discussionmentioning

confidence: 99%

“…One of the primary explanations is the absence of effectual predictive markers for prognosis. Recently, an increasing body of evidence shows gene expression signature assays to estimate survival among tumor patients (2)(3)(4). Thus, the identification and development of new immune-related predictive markers which can predict patient prognosis more accurately and outline individualized treatment plans may have high clinical value.…”

Section: Introductionmentioning

confidence: 99%

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

et al. 2021

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BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

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“…We further examined the predictive efficacy of immunotherapy in the TCGA cohort. IPS analysis and TIDE analysis were two methods most frequently applied in bioinformatic studies (Wu et al, 2021;Xu R. et al, 2021;Xu Y. et al, 2021;Zhao et al, 2021;Zuo et al, 2021). The IPS analysis results suggested that the SRLncSig might present a better performance in predicting CTLA4 blockade therapy.…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

Jiang

Chen

et al. 2021

Front. Cell Dev. Biol.

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The immune microenvironment plays a critical role in tumor biology. As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancers (GCs). Long non-coding RNAs (lncRNAs) have been revealed to participate in this process. In this study, we aimed to develop a stemness-related lncRNA signature (SRLncSig) with guiding significance for immunotherapy. Three cohorts (TCGA, Zhongshan, and IMvigor210) were enrolled for analysis. A list of stemness-related lncRNAs (SRlncRNAs) was collected by co-expression strategy under the threshold of coefficient value >0.35 and p-value < 0.05. Cox and Lasso regression analysis was further applied to find out the SRlncRNAs with prognosis-predictive value to establish the SRLncSig in the TCGA cohort. IPS and TIDE algorithms were further applied to predict the efficacy of SRLncSig in TCGA and Zhongshan cohorts. IMvigor210 was composed of patients with clinical outcomes of immunotherapy. The results indicated that SRLncSig not only was confirmed as an independent risk factor for GCs but also identified as a robust indicator for immunotherapy. The patient with a lower SRLncSig score was more likely to benefit from immunotherapy, and the results were highly consistent in three cohorts. In conclusion, our study not only could clarify the correlations between stemness and immunotherapy in GC patients but also provided a model to guide the applications of immunotherapy in clinical practice.

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A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma

Cited by 149 publications

References 49 publications

Prognosis and Immunotherapy Response With a Novel Golgi Apparatus Signature-Based Formula in Lung Adenocarcinoma

Prognosis and Immunotherapy Response With a Novel Golgi Apparatus Signature-Based Formula in Lung Adenocarcinoma

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

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