Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages.

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

Tet methylcytosine dioxygenase 2 (TET2) deficiency elicits EGFR-TKI (tyrosine kinase inhibitors) resistance in non-small cell lung cancer.

Despite epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) have shown remarkable efficacy in patients with EGFR-mutant non-small cell lung cancer (NSCLC), acquired resistance inevitably develops, limiting clinical efficacy. We found that TET2 was poly-ubiquitinated by E3 ligase CUL7FBXW11 and degraded in EGFR-TKI resistant NSCLC cells. Genetic perturbation of TET2 rendered parental cells more tolerant to TKI treatment. TET2 was stabilized by MEK1 phosphorylation at Ser 1107, while MEK1 inactivation promoted its proteasome degradation by enhancing the recruitment of CUL7FBXW11. Loss of TET2 resulted in the upregulation of TNF/NF-κB signaling that confers the EGFR-TKI resistance. Genetic or pharmacological inhibition of NF-κB attenuate the TKI resistance both in vitro and in vivo. Our findings exemplified how a cell growth controlling kinase MEK1 leveraged the epigenetic homeostasis by regulating TET2, and demonstrated an alternative path of non-mutational acquired EGFR-TKI resistance modulated by TET2 deficiency. Therefore, combined strategy exploiting EGFR-TKI and inhibitors of TET2/NF-κB axis holds therapeutic potential for treating NSCLC patients who suffered from this resistance.

Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer.

The drug therapy for non-small cell lung cancer (NSCLC) have always been issues of poisonous side effect, acquired drug resistance and narrow applicable population. In this study, we built a novel network analysis method (difference- correlation- enrichment- causality- node), which was based on the difference analysis, Spearman correlation network analysis, biological function analysis and Bayesian causality network analysis to discover new therapeutic target of NSCLC in the sequencing data of BEAS-2B and 7 NSCLC cell lines. Our results showed that, as a proteasome subunit coding gene in the central of cell cycle network, PSMD2 was associated with prognosis and was an independent prognostic factor for NSCLC patients. Knockout of PSMD2 inhibited the proliferation of NSCLC cells by inducing cell cycle arrest, and exhibited marked increase of cell cycle blocking protein p21, p27 and decrease of cell cycle driven protein CDK4, CDK6, CCND1 and CCNE1. IPA and molecular docking suggested bortezomib has stronger affinity to PSMD2 compared with reported targets PSMB1 and PSMB5. In vitro and In vivo experiments demonstrated the inhibitory effect of bortezomib in NSCLC with different driven mutations or with tyrosine kinase inhibitors resistance. Taken together, bortezomib could target PSMD2, PSMB1 and PSMB5 to inhibit the proteasome degradation of cell cycle check points, to block cell proliferation of NSCLC, which was potential optional drug for NSCLC patients.

Metformin attenuates chronic lung allograft dysfunction: evidence in rat models.

BACKGROUND: Chronic lung allograft dysfunction (CLAD) directly causes an abysmal long-term prognosis after lung transplantation (LTx), but effective and safe drugs are not available. Metformin exhibits high therapeutic potential due to its antifibrotic and immunomodulatory effects; however, it is unclear whether metformin exerts a therapeutic effect in CLAD. We sought to investigate the effect of metformin on CLAD based on rat models. METHODS: Allogeneic LTx rats were treated with Cyclosporin A (CsA) in the first week, followed by metformin, CsA, or vehicle treatment. Syngeneic LTx rats received only vehicles. All rats were sacrificed on post-transplant week 4. Pathology of lung graft, spleen, and thymus, extent of lung fibrosis, activity of profibrotic cytokines and signaling pathway, adaptive immunity, and AMPK activity were then studied. RESULTS: Allogeneic recipients without maintenance CsA treatment manifested CLAD pathological characteristics, but these changes were not observed in rats treated with metformin. For the antifibrotic effect, metformin suppressed the fibrosis extent and profibrotic cytokine expression in lung grafts. Regarding immunomodulatory effect, metformin reduced T- and B-cell infiltration in lung grafts, spleen and thymus weights, the T- and B-cell zone areas in the spleen, and the thymic medullary area. In addition, metformin activated AMPK in lung allografts and in α-SMA+ cells and T cells in the lung grafts. CONCLUSIONS: Metformin attenuates CLAD in rat models, which could be attributed to the antifibrotic and immunomodulatory effects. AMPK activation suggests the potential molecular mechanism. Our study provides an experimental rationale for further clinical trials.

Targeting cancer-related inflammation with non-steroidal anti-inflammatory drugs: Perspectives in pharmacogenomics.

Inflammatory processes are essential for innate immunity and contribute to carcinogenesis in various malignancies, such as colorectal cancer, esophageal cancer and lung cancer. Pharmacotherapies targeting inflammation have the potential to reduce the risk of carcinogenesis and improve therapeutic efficacy of existing anti-cancer treatment. Non-steroidal anti-inflammatory drugs (NSAIDs), comprising a variety of structurally different chemicals that can inhibit cyclooxygenase (COX) enzymes and other COX-independent pathways, are originally used to treat inflammatory diseases, but their preventive and therapeutic potential for cancers have also attracted researchers' attention. Pharmacogenomic variability, including distinct genetic characteristics among different patients, can significantly affect pharmacokinetics and effectiveness of NSAIDs, which might determine the preventive or therapeutic success for cancer patients. Hence, a more comprehensive understanding in pharmacogenomic characteristics of NSAIDs and cancer-related inflammation would provide new insights into this appealing strategy. In this review, the up-to-date advances in clinical and experimental researches targeting cancer-related inflammation with NSAIDs are presented, and the potential of pharmacogenomics are discussed as well.

Circulating tumor DNA accurately predicts disease progression and genotype alterations in postoperative adjuvant EGFR-TKI resistance: a case report.

Background: Circulating tumor DNA (ctDNA) is receiving more and more attention for its role in tumor screening and disease surveillance in cancer patients. However, it is unclear whether ctDNA can be used to predict recurrence and metastasis in patients after radical resection due to the resulting lower tumor burden. The published literature on postoperative ctDNA levels is also currently limited. Case Description: In this article, we report a rare case in which ctDNA accurately predicted relapse, disease progression and mechanism of resistance to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) in adjuvant setting in an EGFR-mutated lung adenocarcinoma patient. The 49-year-old male patient was a current smoker and denied any family history. Chest computed tomography (CT) scans revealed a 5.7×4.3 mass in the left upper lobe. He received adjuvant gefitinib after surgery for a stage IIIB (pT3N2M0) pulmonary adenocarcinoma. The ctDNA detection showed that the EGFR exon 19 deletion (EGFR del19) gene mutation frequencies decreased gradually and even disappeared. However, 8 months after the operation, the EGFR del19 mutation re-emerged in the blood, accompanied by a newly emerged solitary nodule (2 mm) that was later confirmed to be metastatic. Soon afterward, ctDNA detection revealed the EGFR T790M mutation, and the mediastinal lymph nodes rapidly enlarged. The patient's treatment was switched to Osimertinib and the ctDNA detection results showed the EGFR T790M gene mutation frequencies steadily decreased to zero. During the treatment period, ctDNA detection accurately predicted each change in disease burden and revealed genotype alterations. The patient ultimately developed severe metastases in the liver after developing resistance to Osimertinib. Conclusions: This report suggests that ctDNA help monitor disease recurrence and identify genotypes in patients undergoing postoperative adjuvant EGFR-TKI therapy. More clinical researches are needed to support ctDNA is a promising tool for predicting disease progressive.

Targeting STT3A produces an anti-tumor effect in lung adenocarcinoma by blocking the MAPK and PI3K/AKT signaling pathway.

Background: Glycosylation is crucial for the stability and biological functions of proteins. The aberrant glycosylation of critical proteins plays an important role in multiple cancers, including lung adenocarcinoma (LUAD). STT3 oligosaccharyltransferase complex catalytic subunit A (STT3A) is a major isoform of N-linked glycosyltransferase that catalyzes the glycosylation of various proteins. However, the functions of STT3A in LUAD are still unclear. Methods: The expression profiles of STT3A were initially analyzed in public data sets and then validated by quantitative real-time polymerase chain reaction, Western blot and immunohistochemistry assays in clinical LUAD samples. The overall survival (OS) between patients with high and low STT3A expression was compared using a Kaplan-Meier curve with a log-rank analysis. STT3A was knocked-out using CRISPR/Cas9 and inhibited by NGI-1. Cell Counting Kit-8, colony formation assay, wound-healing, transwell assay, and flow cytometry were performed to assess the cellular functions of STT3A in vitro. A mice xenograft model was established to investigate the effects of STT3A on tumor growth in vivo. Further, the downstream signaling pathways of STT3A were screened by mass spectrometry with a bioinformatics analysis, and the activation of the target pathways were subsequently validated by Western blot. Results: The expression of STT3A was frequently upregulated in LUAD tissues than normal lung tissues. The high expression of STT3A was significantly associated with poor OS in LUAD patients. The knockout or inhibition of STT3A suppressed proliferation, migration, and invasion, and arrested the cell cycle of LUAD cell lines in vitro. Similarly, the knockout or inhibition of STT3A suppressed tumor growth in vivo. In terms of molecular mechanism, STT3A may promote LUAD progression by activating the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase and protein kinase B (PI3K/AKT) pathways and regulating the epithelial-mesenchymal transition. Conclusions: STT3A promotes LUAD progression via the MAPK and PI3K/AKT signaling pathways and could serve as a novel prognostic biomarker and potential therapeutic target for LUAD patients.

Identification of GPX4 as a therapeutic target for lung adenocarcinoma after EGFR-TKI resistance.

Background: Tyrosine kinase inhibitor (TKI) treatment has significantly improved the prognosis of oncogenic-driven lung adenocarcinoma (LUAD). However, drug resistance limits the long-term benefits of patients. Therefore, there is a pressing need to explore the mechanism of TKI resistance and identify new therapeutic targets. It is possible to overcome TKI resistance by inducing tumor cell death through a new process called ferroptosis. Aberrations in ferroptosis, which is a kind of regulated cell death (RCD), has been confirmed to be involved in the development and progression of multiple tumors, and is closely related to patient survival. At present, the role of ferroptosis in TKI resistance remains unclear. Methods: Ferroptosis-related factors were isolated by expression characteristics analysis based on the multi-omics data of LUADs and normal lung tissues from The Cancer Genome Atlas (TCGA) database. Next, expression of selected ferroptosis-related factors and prognosis were analyzed. Subsequently, the differences in the expression of selected ferroptosis-related factors before and after TKI resistance on a variety of LUAD cell lines were analyzed to identify the factors that were involved in TKI resistance. Finally, the therapeutic effects were confirmed in vitro by targeting the selected ferroptosis-related factors with small molecule compounds. Results: Glutathione Peroxidase 4 (GPX4), a ferroptosis-related factor, was up-regulated in tumor tissue of LUADs, and correlated with the prognosis of patients. By detecting the expression change of GPX4 before and after TKI resistance in a variety of LUAD cell lines, we confirmed that the inhibition of GPX4 could overcome epidermal growth factor receptor (EGFR)-TKI resistance by inducing ferroptosis. Conclusions: GPX4 could serve as a novel therapeutic target for EGFR-TKI resistance in LUAD.

Artificial intelligence in cancer target identification and drug discovery.

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

A Novel Immune-Prognosis Index Predicts the Benefit of Lung Adenocarcinoma Patients.

Background: Constructed an immune-prognosis index (IPI) and divided lung adenocarcinoma (LUAD) patients into different subgroups according to IPI score, describe the molecular and immune characteristics of patients between different IPI subgroups, and explore their response to immune checkpoint blockade (ICB) treatment. Methods: Based on the transcriptome profile of LUAD patients in TCGA and immune gene sets from ImmPort and InnateDB, 15 hub immune genes were identified through correlation and Bayesian causal network analysis. Then, IPI was constructed with 5 immune genes by using COX regression analysis and verified with external datasets (GSE30219, GSE37745, GSE68465, GSE126044 and GSE135222). Finally, the characteristics and the response to ICB treatment of LUAD patients between two different IPI subgroups were analyzed. Results: IPI was constructed based on the expression of 5 genes, including A2M, ADRB1, ADRB2, VIPR1 and PTH1R. IPI-high LUAD patients have a better overall survival than IPI-low LUAD patients, consistent with the results in the GEO cohorts. The comprehensive results showed that patients in the IPI-high subgroup were exhibited characters as metabolism-related signaling pathways activation, lower TP53 and TTN mutation rate, more infiltrations of CD8 T cells, dendritic cells and macrophages M1, especially earned more benefit from ICB treatment. In contrast, patients in the IPI-low subgroup were exhibited characters as p53 signaling pathways activation, higher TP53 and TTN mutation rate, more infiltrations of resting memory CD4 T cells, macrophages M2, immune-suppressive response and less benefit from ICB treatment. Conclusion: IPI is a potentially valuable prognostic evaluation method for LUAD, which works well in the benefit predicting of LUAD patients within ICB treatment.

Tumor-endogenous PD-1 promotes cell proliferation and predicts poor survival in non-small cell lung cancer.

Background: Immunotherapy has emerged as a promising treatment strategy in malignant tumors. Inhibitors and neutralizing antibodies targeted PD-1 or PD-L1 show improved clinical outcomes in advanced non-small cell lung cancers (NSCLCs). Previous studies concluded that PD-1 was mainly expressed on activated T cells, B cells and other immune cells. Recently, two studies suggested that PD-1 could be detected in the tumor cells of melanoma and hepatocellular carcinoma. Tumor-endogenous PD-1 in NSCLCs has not been reported at present. Thus, we intended to explore the expression and prognostic relevance of tumor-endogenous PD-1 in NSCLCs. Methods: We detected the PD-1 expression in fresh tumor samples and tumor slices by flow cytometer and immunohistochemical analysis respectively. Overall survival (OS) and dis-ease-free survival (DFS) were analyzed with Kaplan-Meier survival curve. We explored the PD-1 expression in lung cancer cell lines and investigated its effect on cell proliferation. Results: Flow cytometry showed that tumor cells with endogenous PD-1 constituted 2.08%, 2.42%, 1.79%, and 1.21% in 4 clinical NSCLC samples respectively. Thirty-four patients (34%) in the cohort were positive for tumor-endogenous PD-1 in IHC analysis. Patients with tumor-endogenous PD-1 had significantly worse 5-year overall survival (OS) and disease-free survival (DFS). Multivariate analysis identified tumor-endogenous PD-1 as an independent risk factor (HR =3.807, 95% CI: 2.031-7.135, P<0.05). PD-1 could be observed in 4 kinds of lung cancer cell lines (A549, H1975, H1299 and HCC827). PD-1 overexpression could enhance proliferation and clone formation of these cell lines. Conclusions: PD-1 was endogenously expressed on the tumor cells of NSCLCs, and it could serve as an independent prognostic risk factor. The molecular mechanism of inferior survival of tumor-endogenous PD-1 may attribute to its role in promoting cell proliferation and clone formation. Our results demonstrated the non-immune role of PD-1 in tumor microenvironment and may provide new thinking for the therapy of NSCLCs.

Identifies Immune Feature Genes for Prediction of Chemotherapy Benefit in Cancer.

Chemotherapy is still the most fundamental treatment for advanced cancers so far. Previous studies have indicated that immune cell infiltration (ICI) index could serve as a biomarker to predict chemotherapy benefit in breast cancer and colorectal cancer. However, due to different responses of tumor infiltrating immune cells (TIICs) to chemotherapy, the prediction efficiency of ICI index is not fully confirmed by now. In our study, we first extended this conclusion in 7 cancers that high ICI index could certainly indicate chemotherapy benefit (P<0.05). But we also found the fraction of different TIICs and the interaction of TIICs were varies greatly from cancer to cancer. Therefore, we executed correlation and causal network analysis to identify chemotherapy associated immune feature genes, and fortunately identified six co-owned immune feature genes (CD48, GPR65, C3AR1, CD2, CD3E and ARHGAP9) in 10 cancers (BLCA, BRCA, COAD, LUAD, LUSC, OV, PAAD, SKCM, STAD and UCEC). Base on this, we developed a chemotherapy benefit prediction model within six co-owned immune feature genes through random forest classifying (AUC =0.83) in cancers mentioned above, and validated its efficiency in external datasets. In short, our work offers a novel model with a shrinking panel which has the potential to guide optimal chemotherapy in cancer.

Perioperative ctDNA-Based Molecular Residual Disease Detection for Non-Small Cell Lung Cancer: A Prospective Multicenter Cohort Study (LUNGCA-1).

PURPOSE: We assessed whether perioperative circulating tumor DNA (ctDNA) could be a biomarker for early detection of molecular residual disease (MRD) and prediction of postoperative relapse in resected non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: Based on our prospective, multicenter cohort on dynamic monitoring of ctDNA in lung cancer surgery patients (LUNGCA), we enrolled 950 plasma samples obtained at three perioperative time points (before surgery, 3 days and 1 month after surgery) of 330 stage I-III NSCLC patients (LUNGCA-1), as a part of the LUNGCA cohort. Using a customized 769-gene panel, somatic mutations in tumor tissues and plasma samples were identified with next-generation sequencing and utilized for ctDNA-based MRD analysis. RESULTS: Preoperative ctDNA positivity was associated with lower recurrence-free survival (RFS; HR = 4.2; P < 0.001). The presence of MRD (ctDNA positivity at postoperative 3 days and/or 1 month) was a strong predictor for disease relapse (HR = 11.1; P < 0.001). ctDNA-based MRD had a higher relative contribution to RFS prediction than all clinicopathologic variables such as the TNM stage. Furthermore, MRD-positive patients who received adjuvant therapies had improved RFS over those not receiving adjuvant therapy (HR = 0.3; P = 0.008), whereas MRD-negative patients receiving adjuvant therapies had lower RFS than their counterparts without adjuvant therapy (HR = 3.1; P < 0.001). After adjusting for clinicopathologic variables, whether receiving adjuvant therapies remained an independent factor for RFS in the MRD-positive population (P = 0.002) but not in the MRD-negative population (P = 0.283). CONCLUSIONS: Perioperative ctDNA analysis is effective in early detection of MRD and relapse risk stratification of NSCLC, and hence could benefit NSCLC patient management.

Impact of thymosin α1 as an immunomodulatory therapy on long-term survival of non-small cell lung cancer patients after R0 resection: a propensity score-matched analysis.

BACKGROUND: There is limited information about thymosin α1 (Tα1) as adjuvant immunomodulatory therapy, either used alone or combined with other treatments, in patients with non-small cell lung cancer (NSCLC). This study aimed to evaluate the effect of adjuvant Tα1 treatment on long-term survival in margin-free (R0)-resected stage IA-IIIA NSCLC patients. METHODS: A total of 5746 patients with pathologic stage IA-IIIA NSCLC who underwent R0 resection were included. The patients were divided into the Tα1 group and the control group according to whether they received Tα1 or not. A propensity score matching (PSM) analysis was performed to reduce bias, resulting in 1027 pairs of patients. RESULTS: After PSM, the baseline clinicopathological characteristics were similar between the two groups. The 5-year disease-free survival (DFS) and overall survival (OS) rates were significantly higher in the Tα1 group compared with the control group. The multivariable analysis showed that Tα1 treatment was independently associated with an improved prognosis. A longer duration of Tα1 treatment was associated with improved OS and DFS. The subgroup analyses showed that Tα1 therapy could improve the DFS and/or OS in all subgroups of age, sex, Charlson Comorbidity Index (CCI), smoking status, and pathological tumor-node-metastasis (TNM) stage, especially for patients with non-squamous cell NSCLC and without targeted therapy. CONCLUSION: Tα1 as adjuvant immunomodulatory therapy can significantly improve DFS and OS in patients with NSCLC after R0 resection, except for patients with squamous cell carcinoma and those receiving targeted therapy. The duration of Tα1 treatment is recommended to be >24 months.

Ectopic bronchogenic cyst arising from the diaphragm: a rare case report and literature review.

BACKGROUND: Bronchogenic cysts can be caused by errors in the growth of the ventral foregut. Localization of the bronchogenic cyst (BC) varies depending on the level of the abnormal budding. They are usually located in the lungs and mediastinum. BCs of the diaphragm are a rare form of this abnormality. CASE PRESENTATION: A 66-year-old woman coughs and expectorates. CT scan evaluation revealed a soft tissue shadow of 6 × 5 cm in the left lung. Under thoracoscopic surgery, we found that the mass originated from the diaphragm away from the lung tissue, we completely removed the mass and the pathological result was diagnosed as BC. CONCLUSIONS: The prognosis of ectopic BC is usually optimistic for benign tumors, as long as the tumor is completely removed.

Predictive value of pretreatment PD-L1 expression in EGFR-mutant non-small cell lung cancer: a meta-analysis.

OBJECTIVE: To investigate the predictive value of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). METHODS: We conducted a systemic search of PubMed, EMBASE, and the Cochrane Library from 1 January 2000 to 30 August 2020, to identify related studies. We combined the hazard ratio (HR) and 95% confidence interval (CI) to assess the correlation of PD-L1 expression with progression-free survival (PFS) and overall survival (OS). We assessed the quality of the included studies by the Newcastle-Ottawa Scale (NOS). We performed subgroup analyses based on immunohistochemistry (IHC) scoring system, IHC antibodies, sample size, countries, and survival analysis mode. Sensitivity analysis and evaluation of publication bias were also performed. RESULTS: Twelve studies including 991 patients met the criteria. The mean NOS score was 7.42 ± 1.19. Patients with high PD-L1 expression was associated with poorer PFS (HR = 1.90; 95% CI = 1.16-3.10; P = 0.011), while there was no association between PD-L1 expression and OS (HR = 1.19; 95% CI = 0.99-1.43; P = 0.070). Subgroup analysis prompted IHC scoring systems, IHC antibodies, and sample size have important effects on heterogeneity. The pooled results were robust according to the sensitivity analysis. CONCLUSIONS: The result of this meta-analysis suggested that PD-L1 expression might be a predictive biomarker for EGFR-mutant non-small cell lung cancer treated with EGFR-TKIs.

Identifying the prognostic significance of B3GNT3 with PD-L1 expression in lung adenocarcinoma.

BACKGROUND: As a novel treatment, programmed cell death protein 1 (PD-1) inhibitor appears to be less effective in tumors of lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) mutation. Beta-1,3-N-acetylglucosaminyltransferase 3 (B3GNT3) has reported to be associated with programmed death ligand 1 (PD-L1)/PD-1 interaction. However, the relationship between B3GNT3 and PD-L1 and its prognostic significance in. EGFR: mutant status are still unknown. METHODS: B3GNT3 was identified through transcriptome sequencing and The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) database. Flow cytometry and real-time polymerase chain reaction were performed to investigate the association between B3GNT3, PD-L1, and EGFR. Then, B3GNT3 and PD-L1 expression were evaluated by immunohistochemical analysis in 145 surgically resected primary lung adenocarcinomas. The relationships between survival and B3GNT3, PD-L1, and EGFR status were assessed, and the potential prognostic factors in patients with B3GNT3 expression were identified. RESULTS: We found that EGFR activation induced PD-L1 expression, and EGFR tyrosine kinase inhibitor (TKI) could reduce PD-L1 protein in EGFR-TKI-sensitive HCC827 and PC9 cell lines. Subsequent analysis showed that EGFR inhibitor could also lead to both decreased PD-L1 and B3GNT3 mRNA expression. A total of 145 lung adenocarcinoma patients were included. PD-L1 >1% and B3GNT3-positive expression in patients might contribute to worse prognosis in both overall survival (OS) [hazard ratio (HR), 2.63; 95% confidence interval (CI), 0.98-7.06; P=0.048] and disease-free survival (DFS) (HR, 3.04; 95% CI, 1.13-8.14; P=0.019), especially in the PD-L1 ≥50% group. However, when patients were negative for B3GNT3, PD-L1, and EGFR (or "triple negative"), there were significant decreases in OS (HR, 5.44; 95% CI, 0.99-29.83; P=0.029) and DFS (HR, 7.24; 95% CI, 1.32-39.73; P=0.008). Positive B3GNT3 expression was a significant risk factor associated with lower DFS (HR, 3.30; P=0.043). CONCLUSIONS: Our results indicate that the B3GNT3 expression is tightly correlated with PD-L1 expression and EGFR mutation status. B3GNT3 is associated with poor prognosis in lung adenocarcinoma patients. Collectively, these findings may offer new insight into enhancing immune therapy efficacy for lung adenocarcinoma patients.

Effect of a genetically engineered interferon-alpha versus traditional interferon-alpha in the treatment of moderate-to-severe COVID-19: a randomised clinical trial.

BACKGROUND: There are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. To compare the effectiveness of a novel genetically engineered recombinant super-compound interferon (rSIFN-co) with traditional interferon-alpha added to baseline antiviral agents (lopinavir-ritonavir or umifenovir) for the treatment of moderate-to-severe COVID-19. METHOD: In this multicenter randomized (1:1) trial, patients hospitalized with moderate-to-severe COVID-19 received either rSIFN-co nebulization or interferon-alpha nebulization added to baseline antiviral agents for no more than 28 days. The primary endpoint was the time to clinical improvement. Secondary endpoints included the overall rate of clinical improvement assessed on day 28, the time to radiological improvement and virus nucleic acid negative conversion. RESULTS: A total of 94 patients were included in the safety set (46 patients assigned to rSIFN-co group, 48 to interferon-alpha group). The time to clinical improvement was 11.5 days versus 14.0 days (95% CI 1.10 to 2.81, p = .019); the overall rate of clinical improvement on day 28 was 93.5% versus 77.1% (difference, 16.4%; 95% CI 3% to 30%); the time to radiological improvement was 8.0 days versus 10.0 days (p = .002), the time to virus nucleic acid negative conversion was 7.0 days versus 10.0 days (p = .018) in the rSIFN-co and interferon alpha arms, respectively. Adverse events were balanced with no deaths among groups. CONCLUSIONS AND RELEVANCE: rSIFN-co was associated with a shorter time of clinical improvement than traditional interferon-alpha in the treatment of moderate-to-severe COVID-19 when combined with baseline antiviral agents. rSIFN-co therapy alone or combined with other antiviral therapy is worth to be further studied.Key messagesThere are few effective therapies for coronavirus disease 2019 (COVID-19) upon the outbreak of the pandemic. Interferon alphas, by inducing both innate and adaptive immune responses, have shown clinical efficacy in treating severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus.In this multicenter, head-to-head, randomized, clinical trial which included 94 participants with moderate-to-severe COVID-19, the rSIFN-co plus antiviral agents (lopinavir-ritonavir or umifenovir) was associated with a shorter time of clinical improvement than interferon-alpha plus antiviral agents.

A six-long noncoding RNA model predicts prognosis in lung adenocarcinoma.

BACKGROUND: The incidence and mortality of lung cancer rank first among various malignant tumors. The lack of clear molecular classification and effective individualized treatment greatly limits the treatment benefits of patients. Long non-coding RNAs (lncRNAs) have been demonstrated widely involve in tumor progressing, and been proved easy to detect for occupying majority in transcriptome. However, less work focuses on studying the potency of lncRNAs as molecular typing and prognostic indicator in lung cancer. METHODS: Based on the 448 lung adenocarcinoma (LUAD) samples and the expression of 14,127 lncRNAs from the Cancer Genome Atlas (TCGA) database, we constructed a co-expression network using weighted gene co-expression network analysis. Then based on the feature module and the overall survival of patients, we constructed a risk score model through Cox proportional hazards regression and verified it with a validation cohort. Finally, according to the median of risk score, the function of this model was enriched. RESULTS: We identified a module containing 123 lncRNAs that is related with the prognosis of LUAD. Using univariate and multivariate Cox proportional hazards regression with lasso regression, six lncRNAs were identified to construct a risk score model. The calculation formula shown as follows: risk score = (-0.3057 × EXPVIM-AS1) + (0.9678 × EXPAC092811.1) + (1.0829 × EXPNFIA-AS1) + (-0.3505 × EXPAL035701.1) + (3.9378 × EXPAC079336.4) + (-0.2810 × EXPAL121790.2). Six-lncRNA model can be used as an independent prognostic indicator in LUAD (P<0.001) and the area under the 5-year receiver operating characteristic (ROC) curve is 0.715. CONCLUSIONS: We developed a six-lncRNA model, which could be used for predicting prognosis and guiding medical treatment in LUAD patients.

Targeting EZH2 for glioma therapy with a novel nanoparticle-siRNA complex.

BACKGROUND: For the past few years, gene-therapy has recently shown considerable clinical benefit in cancer therapy, and the applications of gene therapies in cancer treatments continue to increase perennially. EZH2, an ideal candidate for tumor gene therapy, plays an important role in the tumorigenesis. METHODS: In this study, we developed a novel gene delivery system with a self-assembly method by Methoxy polyethylene glycol-polycaprolactone (MPEG-PCL) and DOTAP(DMC). And EZH2si-DMC was used to research anti-glioma both in vitro and in vivo. RESULTS: DMC with zeta-potential value of 36.7 mV and size of 35.6 nm showed good performance in the delivery siRNA to glioma cell in vitro with high 98% transfection efficiency. EZH2si-DMC showed good anti-glioma effect in vitro through inducing cell apoptosis and inhibiting cell growth. What's more, treatment of tumor-bearing mice with DMC-EZH2si complex had significantly inhibited tumor growth at the subcutaneous model in vivo by inhibiting EZH2 protein expression, promoting apoptosis and reducing proliferation. CONCLUSION: The EZH2 siRNA and DMC complex may be used to treat the glioma in clinical as a new drug.