Significantly different immunological score in lung adenocarcinoma and squamous cell carcinoma and a proposal for a new immune staging system

The spatial landscape of T cells in the microenvironment of stage III lung adenocarcinoma

This study aimed to provide more information for prognostic stratification for patients through an analysis of the T-cell spatial landscape. It involved analyzing stained tissue sections of 80 patients with stage III lung adenocarcinoma (LUAD) using multiplex immunofluorescence and exploring the spatial landscape of T cells and their relationship with prognosis in the center of the tumor (CT) and invasive margin (IM). In this study, multivariate regression suggested that the relative clustering of CT CD4+ conventional T cell (Tconv) to inducible Treg (iTreg), natural regulatory T cell (nTreg) to Tconv, terminal CD8+ T cell (tCD8) to helper T cell (Th), and IM Treg to tCD8 and the relative dispersion of CT nTreg to iTreg, IM nTreg to nTreg were independent risk factors for DFS. Finally, we constructed a spatial immunological score named the GT score, which had stronger prognostic correlation than IMMUNOSCORE® based on CD3/CD8 cell densities. The spatial layout of T cells in the tumor microenvironment and the proposed GT score can reflect the prognosis of patients with stage III LUAD more effectively than T-cell density. The exploration of the T-cell spatial landscape may suggest potential cell-cell interactions and therapeutic targets and better guide clinical decision-making. © 2024 The Pathological Society of Great Britain and Ireland.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer

INTRODUCTION

The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery.

MATERIALS AND METHODS

The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data.

RESULTS

Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma.

CONCLUSION

The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

SLC17A9 as a prognostic biomarker correlated with immune infiltrates in human non-small cell lung cancer

The vesicular nucleotide transporter (SLC17A9) has been overexpressed in various cancers. Nonetheless, little is known about its influence on non-small cell lung cancer (NSCLC), including human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Integrative bioinformatics analysis was performed to investigate the prognostic significance and underlying mechanisms of SLC17A9 in patients with NSCLC. Here, we found that SLC17A9 up-regulation was significantly correlated with overall survival in LUAD and LUSC (P < 0.05). Gene set enrichment analysis and protein-protein interaction results revealed that SLC17A9 up-regulation was linked to metabolic process, the hallmark of MYC targets, DNA repair, coagulation and complement. SLC17A9 expression was negatively associated with overall survival and positively related to most LUSC immune cells and immunoinhibitor (20/23), particularly immuno A2aR, PD-1, and CTLA-4 (P < 0.001). High SLC17A9 was associated with infiltrating levels of B cells, CD4+ T cells, M1 macrophages, and T cell exhaustion checkpoints such as PD-1, CTLA4, and LAG3 in LUAD. Moreover, Real-time PCR, MTS assay, EdU assay, ATP production assays and cell cycle analysis were performed to validate SLC17A9 knockdown in LUAD cells. SLC17A9 knockdown significantly inhibited cell proliferation and ATP levels by affecting P2X1, Cytochrome C, and STAT3 expression in lung cancer cells. In conclusion, the present study suggested that SLC17A9 could potentially serve as a prognostic biomarker and correlated with immune infiltrates in LUAD and LUSC.

Advanced Glycation End Products' Receptor DNA Methylation Associated with Immune Infiltration and Prognosis of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Background

Advanced glycation end products' receptor (AGER) is a multiligand receptor that interacts with a wide range of ligands. Previous studies have shown that abnormal AGER expression is closely related to immune infiltration and tumorigenesis. However, the AGER DNA methylation relationship between prognosis and infiltrating immune cells in LUAD and LUSC is still unclear.

Methods

AGER expression in pan-cancer was obtained by using the UALCAN databases. Kaplan-Meier plotter showed the correlation of AGER mRNA expression levels and clinicopathological parameters. The protein expression levels for AGER were derived from Human Protein Atlas Database Analysis. The copy number, somatic mutation, and DNA methylation of AGER were presented with UCSC Xena database. TIMER platform and TISIDB website were used to show the correlation between AGER expression and tumor immune cell infiltration level.

Results

The expression level of AGER was significantly reduced in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Low expression of AGER was significantly correlated with histology, stage, lymph node metastasis, and tumor protein 53 (TP53) mutation and could be used as a potential indicator of poor prognosis of LUAD and LUSC. Moreover, AGER expression was positively correlated with the infiltrating immune cells. Further analysis showed that copy number variation (CNV), mutation, and DNA methylation were involved in AGER downregulation. In addition, we also found that hypermethylated AGER was significantly correlated with tumor-infiltrating lymphocytes.

Conclusion

AGER may be a candidate for the prognostic biomarker of LUAD and LUSC related to tumor immune microenvironment.

Extracellular vesicles from PBDE-47 treated M(LPS) THP-1 macrophages modulate the expression of markers of epithelial integrity, EMT, inflammation and muco-secretion in ALI culture of airway epithelium

AIMS

The lung epithelial cells form a physical barrier to the external environment acting as the first line of defence against potentially harmful environmental stimuli. These cells interact with several other cellular components, of which macrophages are some of the most relevant. We analysed the effects of the PBDE-47 on the microRNA cargo of THP-1 macrophage like derived small Extracellular Vesicles (sEVs) and the effects on A549 lung epithelial cells.

MAIN METHODS

sEVs from M(LPS) THP-1 macrophage-like cells after PBDE-47 treatment (sEVs^PBDE+LPS) were characterized by nanoparticle tracking analysis and their microRNA cargo studied by qPCR. Confocal microscopy was applied to study sEVs cellular uptake by A549 cells. The expression of tight junctions (TJs), adhesion molecules, inflammation markers and mucus production in A549 cultured in air liquid interface (ALI) conditions were studied by Real Time PCR and confocal microscopy.

KEY FINDINGS

sEVs^PBDE+LPS microRNA cargo analysis showed that the PBDE-47 modulated the expression of the miR-15a-5p, miR29a-3p, miR-143-3p and miR-122-5p. Furthermore, ALI cultured A549 cells incubated with sEVs^PBDE+LPS showed that zonula occludens-1 (p ≤ 0.04), claudin (p ≤ 0.02), E-cadherin (p ≤ 0.006) and Vimentin (p ≤ 0.0008) mRNAs were increased in A549 cells after sEVs^PBDE+LPS treatment. Indeed, Interleukin (IL)-8 (p ≤ 0.008) and mucin (MUC5AC and MUC5B) (p ≤ 0.03 and p ≤ 0.0001) mRNA expression were up- and down-regulated, respectively.

SIGNIFICANCE

PBDE-47 treated macrophages secrete sEVs with altered microRNA cargo that affect the mRNA expression of TJs, adhesion molecules, cytokines and EMT markers damaging the normal function of the lung epithelium, potentially contributing to the development of lung diseases.

Heterogeneous pattern of gene expression driven by TTN mutation is involved in the construction of a prognosis model of lung squamous cell carcinoma

Objective

To investigate the impact that TTN mutation had on the gene heterogeneity expression and prognosis in patients with lung adenocarcinoma.

Methods

In this study, the Cancer Genome Atlas (TCGA) dataset was used to analyze the TTN mutations in lung adenocarcinoma. Lung adenocarcinoma data was collected from the TCGA database, clinical information of patients was analyzed, and bioinformatics statistical methods were applied for mutation analysis and prognosis survival analysis. The results were verified using the GEO dataset.

Results

The incidence of TTN mutations in lung adenocarcinoma was found to be 73%, and it was related to the prognosis of lung adenocarcinoma. Ten genes were screened with significant contributions to prognosis. A prognosis model was constructed and verified by LASSO COX analysis in the TCGA and GEO datasets based on these ten beneficial factors. The independent prognostic factor H2BC9 for TTN mutation-driven gene heterogeneity expression was screened through multi-factor COX regression analysis.

Conclusion

Our data showed that the gene heterogeneity expression, which was driven by TTN mutations, prolonged the survival of lung adenocarcinoma patients and provided valuable clues for the prognosis of TTN gene mutations in lung adenocarcinoma.

A clinical nomogram based on absolute count of lymphocyte subsets for predicting overall survival in patients with non-small cell lung cancer

BACKGROUND

The absolute count of lymphocyte subsets (ACLS) is correlated to the prognosis of multiple malignancies. This study aimed to combine the ACLS with the clinicopathological parameters to develop a nomogram to accurately predict the prognosis of non-small cell lung cancer (NSCLC) patients.

METHODS

This retrospective study included a training cohort (n = 1685) and validation cohort (n = 337) with NSCLC patients treated in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine between January 2018 and January 2021. Cox regression were conducted to identify factors associated with overall survival. The nomogram was built based on 10 significant factors, and evaluated by the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve.

RESULTS

In the training cohort, the multivariate cox proportional hazard regression analysis showed that the independent factors for overall survival (OS) included age, brain metastases, hepatic metastases, respiratory system diseases, clinical stages, surgery, absolute count (AC) of CD3⁺, CD4⁺, CD8⁺, and NK cells, which were all applied in the nomogram. The C-index of the nomogram to predict OS was 0.777 (95% CI, 0.751-0.802) in training cohort and 0.822 (95% CI, 0.798-0.846) in validation cohort. The area under the ROC showed a good discriminative ability in both cohorts. Calibration curves presented an excellent consistence between the nomogram predicted probability and actual observation.

CONCLUSIONS

We established a prognostic nomogram to predict OS of the NSCLC patient. This nomogram provided a more quantitative, scientific and objective basis for accurate diagnosis and individual management of NSCLC patients.

Interpretable deep learning survival predictive tool for small cell lung cancer

Background

Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope.

Methods

By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance.

Results

The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use.

Conclusion

The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

TIAM2 Contributes to Osimertinib Resistance, Cell Motility, and Tumor-Associated Macrophage M2-like Polarization in Lung Adenocarcinoma

Background: Osimertinib-based therapy effectively improves the prognosis of lung adenocarcinoma (LUAD) patients with epidermal growth factor receptor mutations. However, patients will have cancer progression after approximately one year due to the occurrence of drug resistance. Extensive evidence has revealed that lipid metabolism and tumor-associated macrophage (TAM) are associated with drug resistance, which deserves further exploration. Methods: An osimertinib resistance index (ORi) was built to investigate the link between lipid metabolism and osimertinib resistance. The ORi was constructed and validated using TCGA and GEO data, and the relationship between ORi and immune infiltration was discussed. Weighted gene co-expression network analysis based on the M2/M1 macrophage ratio determined the hub gene TIAM2 and the biological function of TIAM2 in LUAD was verified in vitro. Results: ORi based on nine lipid metabolism-related genes was successfully constructed, which could accurately reflect the resistance of LUAD patients to osimertinib, predict the prognosis, and correlate with M2-like TAM. Additionally, TIAM2 was found to increase osimertinib tolerance, enhance cell motility, and promote M2-like TAM polarization in LUAD. Conclusions: The lipid metabolism gene is strongly connected with osimertinib resistance. TIAM2 contributes to osimertinib resistance, enhances cell motility, and induces M2-like TAM polarization in LUAD.

Development and validation of a prognosis prediction model based on 18 endoplasmic reticulum stress-related genes for patients with lung adenocarcinoma

Background

Endoplasmic reticulum (ER) stress had a crucial impact on cell survival, proliferation, and metastasis in various cancers. However, the role of ER stress in lung adenocarcinoma remains unclear.

Method

Gene expression and clinical data of lung adenocarcinoma (LUAD) samples were extracted from The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. ER stress score (ERSS) was constructed based on hub genes selected from 799 ER stress-related genes by least absolute shrinkage and selection operator (LASSO) regression. A Cox regression model, integrating ERSS and the TNM stage, was developed to predict overall survival (OS) in TCGA cohort and was validated in GEO cohorts. Gene set enrichment analysis (GSEA), single-sample GSEA (ssGSEA), and gene mutation analyses were performed to further understand the molecular features of ERSS. The tumor immune infiltration was evaluated by ESTIMATE, CIBERSORT, and xCell algorithms. The receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the risk model. p< 0.05 was considered statistically significant.

Results

One hundred fifty-seven differentially expressed genes (DEGs) were identified between tumor and para-carcinoma tissues, and 45 of them significantly correlated with OS. Next, we identified 18 hub genes and constructed ERSS by LASSO regression. Multivariate analysis demonstrated that higher ERSS (p< 0.0001, hazard ratio (HR) = 3.8, 95%CI: 2.8–5.2) and TNM stage (p< 0.0001, HR = 1.55, 95%CI: 1.34–1.8) were independent predictors for worse OS. The prediction model integrating ERSS and TNM stage performed well in TCGA cohort (area under the curve (AUC) at five years = 0.748) and three GEO cohorts (AUC at 5 years = 0.658, 0.717, and 0.739). Pathway enrichment analysis showed that ERSS significantly correlated with unfolded protein response. Meanwhile, pathways associated with the cell cycle, growth, and metabolism were significantly enriched in the high ERSS group. Patients with SMARCA4, TP53, and EGFR mutations showed significantly higher ERSS (p = 4e−04, 0.0027, and 0.035, respectively). Tissues with high ERSS exhibited significantly higher infiltration of M1 macrophages, activated dendritic cells, and lower infiltration of CD8+ T cells and B cells, which indicate an activated tumor antigen-presenting but suppressive immune response status.

Conclusion

We developed and validated an ER stress-related risk model that exhibited great predictive value for OS in patients with LUAD. Our work also expanded the understanding of the role of ER stress in LUAD.

Expression of Mucin Family Proteins in Non-Small-Cell Lung Cancer and its Role in Evaluation of Prognosis

Lung cancer is still the major contributor to cancer-related mortality. Over 85% of patients suffer from non-small-cell lung cancer (NSCLC). Mucins (MUCs) are large glycoproteins secreted or membrane-bound produced by epithelial cells in normal and malignant tissues. They are the major components of the mucous gel that covers the surface of the respiratory epithelium. Certain MUCs have been used or proposed to act as biomarkers for lung cancer. Nevertheless, the expression, messenger ribonucleic acid (mRNA) levels, and the prognostic value of MUCs in NSCLC are yet to be investigated systematically. In this research, the biological information of MUC proteins in patients with NSCLC was examined using a series of databases. The results based on gene expression profiling interactive analysis (GEPIA) illustrated that the expression of MUC3A, MUC4, MUC5B, MUC13, MUC16, and MUC21 mRNAs was remarkably upmodulated in lung adenocarcinoma (LUAD) patients, whereas the MUC1 expression was downregulated in lung squamous cell carcinoma (LUSC) patients. Kaplan–Meier plotter (KM Plotter) analysis revealed that elevated mRNA expression levels of MUC3A and MUC16 were linked to unfavourable overall survival (OS) in NSCLC, while increased mRNA expression of MUC1 and MUC15 was linked to good OS, especially in LUAD patients. In addition, differential expression of MUC1, MUC3A/3B, MUC8, MUC12, MUC15, and MUC16 mRNA was linked to the prognoses of NSCLC patients with varied clinical-pathological subtypes. Genetic alterations of MUCs in NSCLC primarily involved mutations, fusion, amplification, deep deletion, and multiple alterations according to cancer genomics (cBioPortal). Therefore, we propose that combinations of MUC proteins can act as prognostic biomarkers and demonstrate the therapeutic potential for NSCLC-related therapy.

The crossroads of adenosinergic pathway and epithelial-mesenchymal plasticity in cancer

Epithelial-mesenchymal transition (EMT) is a key mechanism related to tumor progression, invasion, metastasis, resistance to therapy and poor prognosis in several types of cancer. However, targeting EMT or partial-EMT, as well as the molecules involved in this process, has remained a challenge. Recently, the CD73 enzyme, which hydrolyzes AMP to produce adenosine (ADO), has been linked to the EMT process. This relationship is not only due to the production of the immunosuppressant ADO but also to its role as a receptor for extracellular matrix proteins, being involved in cell adhesion and migration. This article reviews the crosstalk between the adenosinergic pathway and the EMT program and the impact of this interrelation on cancer development and progression. An in silico analysis of RNAseq datasets showed that several tumor types have a significant correlation between an EMT score and NT5E (CD73) and ENTPD1 (CD39) expressions, with the strongest correlations in prostate adenocarcinoma. Furthermore, it is evident that the cooperation between EMT and adenosinergic pathway in tumor progression is context and tumor-dependent. The increased knowledge about this topic will help broaden the view to explore new treatments and therapies for different types of cancer.

Prognostic and Immunological Role of FAT Family Genes in Non-Small Cell Lung Cancer

Background

The FAT atypical cadherin 1/2/3/4 (FAT1/2/3/4) has been linked to the occurrence and development of various cancers. However, the prognostic and immunological role of FAT1/2/3/4 in non-small cell lung cancer (NSCLC) has not been clarified.

Methods

The association of FAT1/2/3/4 mutations with tumor mutation burden (TMB), tumor immunity in the microenvironment, and response to ICIs in NSCLC was investigated. Whole-exome sequencing data of lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) samples from the Cancer Genome Atlas (TCGA), and an immunotherapy data set comprising mutation and survival data of 75 NSCLC patients were analyzed. Two independent pan-cancer cohorts with large samples were used to validate the prognostic value of FAT1/2/3/4 mutations in immunotherapy.

Results

A high mutation rate of FAT1/2/3/4 (57.3%, 603/1052) was observed in NSCLC patients. TMB was significantly higher in samples with mutated FAT1/2/3/4 compared to samples with wildtype FAT1/2/3/4 (P < .05). FAT2 mutation was found to be an independent prognostic biomarker in LUAD. FAT1/2/3/4 were aberrantly expressed in LUAD and LUSC, and high FAT2 expression strongly correlated with high PD-L1 levels in LUAD. Moreover, LUAD patients with FAT1 mutations showed significantly high activated dendritic cells infiltration, whereas those with FAT2/3/4 mutations had high infiltration of CD8⁺ T-cells, M1 macrophages, activated memory CD4⁺ T-cells, and helper follicular T-cells. It was also observed that FAT1/2/4 mutations were significantly associated with better enhanced objective response and durable clinical benefit, whereas FAT1/2/3 mutations correlated with longer progression-free survival in ICI-treated NSCLC cohort. FAT1/4 mutations were related to better overall survival in pan-cancer patients treated with ICIs.

Conclusions

FAT family genes are potential prognostic and immunological biomarkers and correlate with response to ICIs in NSCLC.

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

Background

Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear.

Materials and methods

Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation.

Results

We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580–0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature.

Conclusion

We found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09296-8.

Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade

In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

CD73 Severed as a Potential Prognostic Marker and Promote Lung Cancer Cells Migration via Enhancing EMT Progression

To investigate the expression levels and prognostic value of CD73 in lung cancer. And moreover, to identify the effect and potential mechanism of CD73 on lung cancer cells proliferation and migration. CD73 expression levels in lung cancer were analyzed base on GEPIA2 and GEO database. GEPIA2 and Kaplan-Meier Plotter (KM Plotter) was used to analyzed the correlation between CD73 expression and prognosis. GEO dataset were analyzed via GEO2R. CD73 overexpression cell model was construction via recombinant lentivirus transfection into A549 and NCI-H520 cells. CCK8 assay were used to investigate cells proliferation. Migration and invasion ability were evaluated by scratch and transwell methods. Base on GEPIA2, GSE32683, GSE116959 and GSE37745 dataset, we found that CD73 expression were significant higher in tumor tissues of lung adenocarcinoma (LUAD) compared with that in non-tumor normal tissues and in lung squamous cell carcinoma (LUSC), while there were no significant difference of CD73 expression between LUSC and normal control tissues. Interestingly, a high CD73 level predict poor overall survival (OS) of LUSC. However, GEPIA2 and KM plotter showed the opposite conclusion of prognostic value of CD73 in LUAD. By using cell experiments, we found that CD73 overexpression promoted proliferation and migration of LUAD A549 cells. However, there was no significant effect of CD73 overexpression on LUSC NCI-H520 cells. Furthermore, CD73 overexpression facilitates epithelial to mesenchymal transition (EMT) progression of A549 cells. In conclusion, our results indicated that CD73 expression were increased in LUAD and might be an poor prognostic marker for LUSC patients. CD73 play an important role in LUAD cells proliferation and migration. These data allowed to support CD73 as a therapeutic target for LUAD.

Integrative Multi-Omics Analysis of Identified NUF2 as a Candidate Oncogene Correlates With Poor Prognosis and Immune Infiltration in Non-Small Cell Lung Cancer

Background

Lung cancer is one of the most common malignant tumors and the leading causes of cancer-related deaths worldwide. As a component of the nuclear division cycle 80 complex, NUF2 is a part of the conserved protein complex related to the centromere. Although the high expression of NUF2 has been reported in many different types of human cancers, the multi-omics analysis in non-small cell lung cancer (NSCLC) of NUF2 remains to be elucidated.

Methods

In this analysis, NUF2 expression difference analysis in non-small cell lung cancer was evaluated by Oncomine, TIMER, GEO, and TCGA database. And the prognosis analysis of NUF2 based on Kaplan-Meier was performed. R language was used to analyze the differential expression genes, functional annotation and protein-protein interaction (PPI). GSEA analysis of differential expression genes was also carried out. Mechanism analysis about exploring the characteristic of NUF2, multi-omics, and correlation analysis was carried out using UALCAN, cBioportal, GEPIA, TIMER, and TISIDB, respectively.

Results

The expression of NUF2 in NSCLC, both lung adenocarcinoma (LUAD) and squamous lung cancer (LUSC), was significantly higher than that in normal tissues. The analysis of UALCAN database samples proved that NUF2 expression was connected with stage and smoking habits. Meanwhile, the overall survival curve also validated that high expression of NUF2 has a poorer prognosis in NSCLC. GO, KEGG, GSEA, subcellular location from COMPARTMENTS indicated that NUF2 may regulate the cell cycle. Correlation analysis also showed that NUF2 was mainly positively associated with cell cycle and tumor-related genes. NUF2 altered group had a poorer prognosis than unaltered group in NSCLC. Immune infiltration analysis showed that the NUF2 expression mainly have negatively correlation with immune cells and immune subtypes in LUAD and LUSC. Furthermore, quantitative PCR was used to validate the expression difference of NUF2 in LUAD and LUSC.

Conclusion

Our findings elucidated that NUF2 may play an important role in cell cycle, and significantly associated with tumor-related gene in NSCLC; we consider that NUF2 may be a prognostic biomarkers in NSCLC.

Hepcidin Upregulation in Lung Cancer: A Potential Therapeutic Target Associated With Immune Infiltration

Lung cancer has the highest death rate among cancers globally. Hepcidin is a fascinating regulator of iron metabolism; however, the prognostic value of hepcidin and its correlation with immune cell infiltration in lung cancer remain unclear. Here, we comprehensively clarified the prognostic value and potential function of hepcidin in lung cancer. Hepcidin expression was significantly increased in lung cancer. High hepcidin expression was associated with sex, age, metastasis, and pathological stage and significantly predicted an unfavorable prognosis in lung cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) results suggested that hepcidin is involved in the immune response. Furthermore, hepcidin expression was positively correlated with the infiltration levels of immune cells and the expression of diverse immune cell marker sets. Importantly, hepcidin may affect prognosis partially by regulating immune infiltration in lung cancer patients. Hepcidin may serve as a candidate prognostic biomarker for determining prognosis associated with immune infiltration in lung cancer.

Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

PURPOSE

Most lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.

MATERIALS AND METHODS

We downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.

RESULTS

Six DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.

CONCLUSION

We identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.