An Immune Panel Signature Predicts Prognosis of Lung Adenocarcinoma Patients and Correlates With Immune Microenvironment

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

Bone marrow stromal cell antigen 2: Tumor biology, signaling pathway and therapeutic targeting (Review)

Bone marrow stromal cell antigen 2 (BST2) is a type II transmembrane protein that serves critical roles in antiretroviral defense in the innate immune response. In addition, it has been suggested that BST2 is highly expressed in various types of human cancer and high BST2 expression is related to different clinicopathological parameters in cancer. The molecular mechanism underlying BST2 as a potential tumor biomarker in human solid tumors has been reported on; however, to the best of our knowledge, there has been no review published on the molecular mechanism of BST2 in human solid tumors. The present review focuses on human BST2 expression, structure and functions; the molecular mechanisms of BST2 in breast cancer, hepatocellular carcinoma, gastrointestinal tumor and other solid tumors; the therapeutic potential of BST2; and the possibility of BST2 as a potential marker. BST2 is involved in cell membrane integrity and lipid raft formation, which can activate epidermal growth factor receptor signaling pathways, providing a potential mechanistic link between BST2 and tumorigenesis. Notably, BST2 may be considered a universal tumor biomarker and a potential therapeutical target.

Keratin gene signature expression drives epithelial-mesenchymal transition through enhanced TGF-β signaling pathway activation and correlates with adverse prognosis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) stands as the foremost histological subtype of non-small-cell lung cancer, accounting for approximately 40% of all lung cancer diagnoses. However, there remains a critical unmet need to enhance the prediction of clinical outcomes and therapy responses in LUAD patients. Keratins (KRTs), serving as the structural components of the intermediate filament cytoskeleton in epithelial cells, play a crucial role in the advancement of tumor progression. This study investigated the prognostic significance of the KRT family gene and developed a KRT gene signature (KGS) for prognostic assessment and treatment guidance in LUAD.

Methods

Transcriptome profiles and associated clinical details of LUAD patients were meticulously gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The KGS score was developed based on the expression of five prognostic KRT genes (KRT7, KRT8, KRT17, KRT18, and KRT80), and the upper quartile of the KGS score was chosen as the cutoff. The Kaplan-Meier method was evaluated to compare survival outcomes between KGS-high and KGS-low groups. The underlying mechanism was further investigated by GSEA, GSVA, and other bioinformatic algorithms.

Results

High expression of the KGS signature exhibited a robust association with poorer overall survival (OS) in the TCGA-LUAD dataset (HR: 1.81; 95% CI: 1.35-2.42, P = 0.00011). The association was further corroborated in three external GEO cohorts, including GSE31210 (HR: 3.31; 95% CI: 1.7-6.47, P = 0.00017), GSE72094 (HR: 1.95; 95% CI: 1.34-2.85, P = 0.00057) and GSE26939 (HR: 3.19; 95% CI: 1.74-5.84, P < 0.0001). Interestingly, KGS-high tumors revealed enrichments in TGF-β and WNT-β catenin signaling pathways, exhibited heightened activation of the epithelial-mesenchymal transition (EMT) pathway and proved intensified tumor stemness compared to their KGS-low counterparts. Additionally, KGS-high tumor cells exhibited increased sensitivity to several targeted agents, including gefitinib, erlotinib, lapatinib, and trametinib, in comparison to KGS-low cells.

Conclusion

This study developed a KGS score that independently predicts the prognosis in LUAD. High expression of KGS score, accompanied by upregulation of TGF-β and WNT-β catenin signaling pathways, confers more aggressive EMT and tumor progression.

Screening of Immune-related lncRNAs in Lung Adenocarcinoma and Establishing a Survival Prognostic Risk Prediction Model

OBJECTIVE

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs).

METHODS

LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples.

RESULTS

In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples.

CONCLUSION

We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram

Background

Accurate prognosis is crucial for improving hepatocellular carcinoma (HCC) patients, clinical management, and outcomes post-liver resection. However, the lack of reliable prognostic indicators poses a significant challenge. This study aimed to develop a user-friendly nomogram to predict HCC patients' post-resection prognosis.

Methods

We retrospectively analyzed the data from 1091 HCC patients, randomly split into training (n=767) and validation (n=324) cohorts. Receiver operating characteristic (ROC) curves determined the optimal cut-off value for alpha1-microglobulin (α1MG) and Beta2-microglobulin (β2MG). Kaplan-Meier analysis assessed microglobulin's impact on survival, followed by Cox regression to identify prognostic factors and construct a nomogram. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index), calibration curves, area under the ROC curve (AUC), and decision curve analysis (DCA), and were compared with the BCLC staging system, Edmondson grade, or BCLC stage plus Edmondson grade.

Results

Patients with high β2MG (≥2.395mg/L) had worse overall survival (OS). The nomogram integrated β2MG, BCLC stage, Edmondson grade, microvascular invasion (MVI), and serum carbohydrate antigen 199 (CA199) levels. C-index for training and validation cohorts (0.712 and 0.709) outperformed the BCLC stage (0.660 and 0.657), Edmondson grade (0.579 and 0.564), and the combination of BCLC stage with Edmondson grade (0.681 and 0.668), improving prognosis prediction. Calibration curves demonstrated good agreement between predicted and observed survival. AUC values exceeded 0.700 over time, highlighting the nomogram's discriminative ability. DCA revealed superior overall net income compared to other systems, emphasizing its clinical utility.

Conclusion

Our β2MG-based nomogram accurately predicts HCC patients' post-resection prognosis, aiding intervention and follow-up planning. Significantly, our nomogram surpasses existing prognostic indicators, including BCLC stage, Edmondson grade, and the combination of BCLC stage with Edmondson grade, by demonstrating superior predictive performance.

Identification and panoramic analysis of drug response-related genes in triple negative breast cancer using as an example NVP-BEZ235

Taking NVP-BEZ235 (BEZ235) as an example to screen drug response-related genes (DRRGs) and explore their potential value in triple-negative breast cancer (TNBC). Through high-throughput technique, multidimensional transcriptome expression data (mRNA, miRNA and lncRNA) of BEZ235-treated and -untreated MDA-MB-468 cell lines were obtained. Combined with transcriptome data of the MDA-MB-468 cells and TCGA-TNBC tissues, differential gene expression analysis and WGCNA were performed to identify DRRGs associated with tumor trait by simulating the drug response microenvironment (DRM) of BEZ235-treated patients. Based on DRRGs, we constructed a ceRNA network and verified the expression levels of three key molecules by RT-qPCR, which not only demonstrated the successful construction of a BEZ235-treated cell line model but also explained the antitumor mechanism of BEZ235. Four molecular subtypes related to the DRM with survival difference were proposed using cluster analysis, namely glycolysis subtype, proliferation depression subtype, immune-suppressed subtype, and immune-activated subtype. A novel prognostic signature consisting of four DRRGs was established by Lasso-Cox analysis, which exhibited outstanding performance in predicting overall survival compared with several excellent reported signatures. The high- and low-risk groups were characterized by enrichment of metabolism-related pathways and immune-related pathways, respectively. Of note, the low-risk group had a better response to immune checkpoint blockade. Besides, pRRophetic analysis found that patients in the low-risk group were more sensitive to methotrexate and cisplation, whereas more resistant to BEZ235, docetaxel and paclitaxel. In conclusion, the DRRGs exemplified by BEZ235 are potential biomarkers for TNBC molecular typing, prognosis prediction and targeted therapy. The novel DRRGs-guided strategy for predicting the subtype, survival and therapy efficacy, might be also applied to more cancers and drugs other than TNBC and BEZ235.

Construction and experimental validation of an acetylation-related gene signature to evaluate the recurrence and immunotherapeutic response in early-stage lung adenocarcinoma

BACKGROUND

Acetylation is a reversible epigenetic process, playing an important role in the initiation and progression of malignant tumors. However, the prognosis value of acetylation-related genes in the early-stage lung adenocarcinoma (LUAD) remains obscure.

MATERIALS AND METHODS

The acetylation-related genes were collected and clustered based on transcriptome sequencing of the patients with early-stage LUAD from the Cancer Genome Atlas. The genomic divergence analysis, protein-protein interaction network construction, Lasso regression, and univariate Cox regression were used to identify the significant biomarkers for the recurrence of the early-stage LUAD. The multivariate Cox regression was used to establish the predictive model. Gene Expression Omnibus was systemically retrieved and four independent datasets were used for external validation. 23 early-stage LUAD samples were collected from the local hospital to detect the expression difference of the genes in the model. Transfection assays were performed to verify the regulatory ability of the screened gene to the proliferation of LUAD cell lines. The single-cell RNA sequencing of the early-stage LUAD patients and two lung cancer cohorts receiving immunotherapy were utilized to explore the predictive ability of the established model to immunotherapeutic sensitivity.

RESULTS

The clustering based on acetylation-related genes was significantly associated with the recurrence (P < 0.01) and immune infiltration statuses. Through a series of bioinformatical and machine learning methods, RBBP7 and YEATS2 were ultimately identified. Accordingly, a novel gene signature containing RBBP7 and YEATS2 was developed to evaluate the recurrence-free survival of early-stage LUAD, which was then validated in five independent cohorts (pooled hazard ratio = 1.88, 95% confidence interval = 1.49-2.37) and 23 local clinical samples (P < 0.01). The knock-down of YEATS2 obviously suppressed proliferation of H1975 and HCC-827 cells. Single-cell RNA sequencing analyses indicated that RBBP7 and YEATS2 were both associated with the tumor immune response, and the prognosis signature could predict the immunotherapeutic response in two cohorts receiving immunotherapy (P < 0.05; P < 0.01).

CONCLUSIONS

Totally, an acetylation-related gene signature is constructed, helping to evaluate the recurrence and immunotherapeutic effectiveness of early-stage LUAD patients.