Non-Small-Cell Lung Cancer Signaling Pathways, Metabolism, and PD-1/PD-L1 Antibodies

Cancer immunotherapy by immune checkpoint blockade and its advanced application using bio-nanomaterials

Cancer is the second leading cause of death worldwide. Traditional approaches, such as surgery, chemotherapy, and radiotherapy have been the main cancer therapeutic modalities in recent years. Cancer immunotherapy is a novel therapeutic modality that potentiates the immune responses of patients against malignancy. Immune checkpoint proteins expressed on T cells or tumor cells serve as a target for inhibiting T cell overactivation, maintaining the balance between self-reactivity and autoimmunity. Tumors essentially hijack the immune checkpoint pathway in order to survive and spread. Immune checkpoint inhibitors (ICIs) are being developed as a result to reactivate the anti-tumor immune response. Recent advances in nanotechnology have contributed to the development of successful, safe, and efficient anticancer drug systems based on nanoparticles. Nanoparticle-based cancer immunotherapy overcomes numerous challenges and offers novel strategies for improving conventional immunotherapies. The fundamental and physiochemical properties of nanoparticles depend on various cancer therapeutic strategies, such as chemotherapeutics, nucleic acid-based treatments, photothermal therapy, and photodynamic agents. The review discusses the use of nanoparticles as carriers for delivering immune checkpoint inhibitors and their efficacy in cancer combination therapy.

Identification and Validation Prognostic Impact of MiRNA-30a-5p in Lung Adenocarcinoma

MiRNA-30a-5p is a microRNA found to be decreased in various human cancers, including lung adenocarcinoma (LUAD). However, the molecular mechanisms of miRNA-30a-5p involve in the progression of LUAD remains unclear. In this study, we found that miRNA-30a-5p expression was significantly decreased in LUAD cells lines, LUAD tissues, and peripheral blood serum. Besides, LUAD patients with decreased miRNA-30a-5p expression exhibit worse clinical outcomes compared to the patients with higher miRNA-30a-5p expression, decreased expression of miRNA-30a-5p was associated with advanced clinical outcomes. Receiver operating characteristic (ROC) curve analysis of miRNA-30a-5p showed an area under the curve (AUC) value of 0.902, indicating its prognostic value in LUAD. Moreover, immune infiltration and gene set enrichment analysis (GSEA) enrichment analyze demonstrated that miRNA-30a-5p expression was associated with immune cell infiltrated in LUAD. Finally, we found that miRNA-30a-5p inhibits cell proliferation, migration, and self-renewal abilities of LUAD in vitro. In summary, this is the first report that miRNA-30a-5p correlated with progression and immune infiltration, which shed some lights on potential prognostic and therapeutic biomarker for LUAD.

The Application of CRISPR/Cas9 Technology for Cancer Immunotherapy: Current Status and Problems

Cancer is one of the main causes of disease-related deaths in the world. Although cancer treatment strategies have been improved in recent years, the survival time of cancer patients is still far from satisfied. Cancer immunotherapy, such as Oncolytic virotherapy, Immune checkpoints inhibition, Chimeric antigen receptor T (CAR-T) cell therapy, Chimeric antigen receptor natural killer (CAR-NK) cell therapy and macrophages genomic modification, has emerged as an effective therapeutic strategy for different kinds of cancer. However, many patients do not respond to the cancer immunotherapy which warrants further investigation to optimize this strategy. The clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9), as a versatile genome engineering tool, has become popular in the biology research field and it was also applied to optimize tumor immunotherapy. Moreover, CRISPR-based high-throughput screening can be used in the study of immunomodulatory drug resistance mechanism. In this review, we summarized the development as well as the application of CRISPR/Cas9 technology in the cancer immunotherapy and discussed the potential problems that may be caused by this combination.

LKB1 expression and the prognosis of lung cancer: A meta-analysis

BACKGROUND

In the past few decades, many lines of evidence implicate the importance of liver kinase B1 (LKB1) as a tumor suppressor gene in the development and progression of solid tumours. However, the prognostic and clinicopathological value of LKB1 in patients with lung cancer are controversial. This article aimed to investigate the latest evidence on this question.

METHODS

A systematic literature searched in the PubMed, Web of Science, Embase, Cochrane library, Scopus until September 20, 2020. The association between overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), clinicopathological features and LKB1 were analysed by meta-analysis.

RESULTS

Eleven studies including 1507 patients were included in this meta-analysis. The pooled results revealed that low LKB1 expression was significantly associated with poor overall survival (OS) (HR = 1.67, 95% CI: 1.07-2.60, P = .024) in lung cancer. However, no association was found between LKB1 expression and DFS/PFS (HR = 1.29, 95% CI: 0.70-2.39, P = .410). Pooled results showed that low LKB1 expression was associated with histological differentiation (poor vs moderate or well, OR = 4.135, 95% CI:2.524-6.774, P < .001), nodal metastasis (absent vs present, OR = 0.503, 95% CI: 0.303-0.835, P = .008) and smoking (yes vs no, OR = 1.765, 95% CI: 1.120-2.782, P = .014).

CONCLUSION

These results suggest that low expression of LKB1 can be considered as a unfavorable prognostic biomarker for human lung cancer, which should be further researched.

Molecular mechanisms and therapeutic relevance of gasdermin E in human diseases

Gasdermin E (GSDME) is one of the main members of the GSDM family and is originally involved in hereditary hearing loss. Recent studies have reported that GSDME expression is epigenetically silenced by methylation in several common tumours, thereby enhancing tumour proliferation and metastasis. GSDME is also downregulated in cancer tissues compared with normal tissues, which suggests that GSDME can be considered a tumour suppressor. Furthermore, GSDME is the effector protein of caspase-3 and granzyme B in pyroptosis, and it plays a significant role in innate immunity, tissue damage, cancer, and hearing loss, thus revealing potential novel therapeutic avenues. A great deal of evidence reveals that GSDME can be implemented as a biomarker in cancer diagnosis and monitoring, chemotherapy, immunotherapy, and chemoresistance. Based on the current knowledge of GSDME, this review is focussed on its mechanism of action and the most recent advances in its role in cancer and normal physiology.

High DSCC1 Level Predicts Poor Prognosis of Lung Adenocarcinoma

Purpose

To evaluate the role of DSCC1 in LUAD.

Patients and Methods

Based on TCGA and GTEx, the Wilcoxon rank-sum test was used to compare the expression differences of DSCC1 between the normal samples of GTEx combined TCGA and the unpaired tumor samples of TCGA, and to compare DSCC1 expression values between tumor tissues and paired normal LUAD tissues in the TCGA cohort. Kruskal–Wallis rank-sum test, Wilcoxon rank-sum test, and logistics regression were used to compare the relationship between the expression of DSCC1 and the clinicopathological parameters. The biological function of DSCC1 was annotated by GSEA and ssGSEA, while Kaplan–Meier and Cox regression analysis were used to evaluate the prognostic value of DSCC1. Furthermore, the time-dependent ROC curve was used to analyze the diagnostic efficacy of DSCC1 in LUAD.

Results

We downloaded the RNA-Seq data of 513 LUAD cases. The expression of DSCC1 was significantly correlated with T stage (OR = 1.04(1.02–1.07), P = 0.002), pathological stage (OR=1.03 (1.01–1.05), P = 0.008) and TP53 status (OR=1.10 (1.07–1.14), P < 0.001). The high expression of DSCC1 was significantly correlated with DSS (HR=1.56 (1.07–2.26), P = 0.021) and OS (HR=1.53 (1.14–2.05), P = 0.004). Moreover, ROC curve analysis (AUC=0.845, CI (0.820-0.870)) indicated DSCC1 as a potential diagnostic molecule for LUAD. In the group with high DSCC1 expression phenotype, down-regulation of EGFR signal, reduction of IL-6 deprivation, cell cycle, and p53 signal pathway were significantly abundant. Spearman correlation analysis showed that the expression of DSCC1 was positively correlated with the infiltration of Th2 cells, T Helper cells.

Conclusion

Our results suggest that DSCC1 may be an important biomarker for the treatment of LUAD.

Histone Modification in NSCLC: Molecular Mechanisms and Therapeutic Targets

Lung cancer is the leading cause of cancer mortality in both genders, with non-small cell lung cancer (NSCLC) accounting for about 85% of all lung cancers. At the time of diagnosis, the tumour is usually locally advanced or metastatic, shaping a poor disease outcome. NSCLC includes adenocarcinoma, squamous cell carcinoma, and large cell lung carcinoma. Searching for novel therapeutic targets is mandated due to the modest effect of platinum-based therapy as well as the targeted therapies developed in the last decade. The latter is mainly due to the lack of mutation detection in around half of all NSCLC cases. New therapeutic modalities are also required to enhance the effect of immunotherapy in NSCLC. Identifying the molecular signature of NSCLC subtypes, including genetics and epigenetic variation, is crucial for selecting the appropriate therapy or combination of therapies. Epigenetic dysregulation has a key role in the tumourigenicity, tumour heterogeneity, and tumour resistance to conventional anti-cancer therapy. Epigenomic modulation is a potential therapeutic strategy in NSCLC that was suggested a long time ago and recently starting to attract further attention. Histone acetylation and deacetylation are the most frequently studied patterns of epigenetic modification. Several histone deacetylase (HDAC) inhibitors (HDIs), such as vorinostat and panobinostat, have shown promise in preclinical and clinical investigations on NSCLC. However, further research on HDIs in NSCLC is needed to assess their anti-tumour impact. Another modification, histone methylation, is one of the most well recognized patterns of histone modification. It can either promote or inhibit transcription at different gene loci, thus playing a rather complex role in lung cancer. Some histone methylation modifiers have demonstrated altered activities, suggesting their oncogenic or tumour-suppressive roles. In this review, patterns of histone modifications in NSCLC will be discussed, focusing on the molecular mechanisms of epigenetic modifications in tumour progression and metastasis, as well as in developing drug resistance. Then, we will explore the therapeutic targets emerging from studying the NSCLC epigenome, referring to the completed and ongoing clinical trials on those medications.

Comprehensive Analysis of Clinical Significance, Immune Infiltration and Biological Role of m6A Regulators in Early-Stage Lung Adenocarcinoma

Recent publications have revealed that N6-methyladenosine (m⁶A) modification is critically involved in tumorigenesis and metastasis. However, the correlation of m⁶A modification and immune infiltration in early-stage lung adenocarcinoma (LUAD) is still uncertain. We performed NMF clustering based on 23 m⁶A regulators and identify three distinct m⁶A clusters and three m⁶A related genes clusters (m⁶A cluster-R) in early-stage LUAD. The immune infiltrating levels were calculated using CIBERSORT, MCPcounter and ssGSEA algorithms. And we established the m⁶A-predictive score to quantify m⁶A modified phenotypes and predict immunotherapeutic responses. Based on the TME characteristics, different immune profiles were also identified among three m⁶A gene-related clusters. And the m⁶A-R-C2 was related to a favorable overall survival (OS), whereas m⁶A-R-C3 had unfavorable overall survival. The m⁶A-predictive score was built according to the expression levels of m⁶A-related genes, and patients could be stratified into subgroups with low/high scores. Patients with high scores had poor overall survival, enhanced immune infiltration, high tumor mutation burden and increased level of somatic mutation. Besides, patients with high scores had unfavorable overall survival in the anti-PD-1 cohort, whereas the overall survival of high-score patients was better in the adoptive T cell therapy cohort. Our work highlights that m⁶A modification is closely related to immune infiltration in early-stage LUAD, which also contributes to the development of more effective immunotherapy strategies.

Construction of a Prognostic Model in Lung Adenocarcinoma Based on Ferroptosis-Related Genes

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma. Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observed that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score. Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes. Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways.

Construction and validation of a novel immune and tumor mutation burden-based prognostic model in lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common type of cancer, is hard to diagnose and has an unfavorable prognosis. Tumor mutation burden (TMB) is a useful predictor and can also determine the efficacy of immunotherapy in various cancers. The present study focused on unraveling the association between immune infiltration and TMB and developing an immune- and TMB-related prognostic model to predict LUAD patients' prognosis. The results revealed that the immune-related prognostic model (IPM) based on TMB was capable of classifying LUAD patients in all cohorts into different risk groups. The IPM was useful and had a significant correlation with LUAD patients' overall survival (OS). Based on the multivariate Cox analysis results, the IPM was proved to be an independent predictive biomarker. Furthermore, the five hub genes and the immune-related model were related to different immune infiltrating cells. The IPM was related to immune checkpoints. At last, an effective nomogram was established to predict LUAD patients' prognosis. To conclude, our IPM is effective in predicting LUAD patients' prognosis and provides novel insights into immunotherapy for LUAD.

Immunotherapeutic Advances for NSCLC

Immunotherapy with antibodies against PD-1 or PD-L1, either alone or in combination with chemotherapy, has revolutionized treatment paradigms of non-small cell lung cancer (NSCLC) patients without oncogenic driver alterations. These agents, namely immune checkpoint inhibitors (ICIs), have also widely demonstrated a remarkable efficacy in locally advanced as well as in early-stage NSCLC. Assessment of tumor PD-L1 expression by immunohistochemistry has entered into routine clinical practice to select patients for immunotherapy, even though its predictive role has long been debated. Despite improved survival outcomes over standard chemotherapy, treatment with ICIs is associated with initial low response rate, with a significant proportion of patients not responding to these agents. Hence, novel appealing predictive biomarkers, such as those related to tumor cell signaling pathways, metabolism or the tumor microenvironment, have emerged as potentially useful to select those patients most likely to benefit from immunotherapy. Moreover, most patients ultimately develop acquired resistance to ICI treatment over time and novel therapeutic strategies are urgently needed to overcome or delay resistance. Herein, we provide an overview on recent advances in immunotherapy in NSCLC, focusing on updated results from studies on ICIs in different disease settings and at different lines of treatment. We further describe currently emerging predictive biomarkers, beyond PD-L1, to optimize patient selection and novel strategies to improve clinical outcomes.

Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Aims

Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment.

Methods

Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC).

Results

According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy.

Conclusion

We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

A novel ferroptosis-related genes model for prognosis prediction of lung adenocarcinoma

Background

Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).

Methods

RNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well.

Results

High-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.

Conclusions

This study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01588-2.

Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

Objective

To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice.

Methods

For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model.

Results

Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles.

Conclusions

We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

Prognostic value of ferroptosis-related genes in patients with lung adenocarcinoma

BACKGROUND

The prevalence of lung adenocarcinomas (LUADs) has dramatically increased in recent decades. Ferroptosis is a process of iron-dependent regulatory cell death. It is still unclear whether the expression of ferroptosis-related genes (FRGs) is involved in the pathogenesis and survival of patients with LUAD.

METHODS

We retrieved LUAD data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and used LASSO Cox regression analysis to select the gene signature suitable for modeling. The risk score was calculated according to the model, and the patients were divided into high- and low-risk groups according to the median risk score. Functional enrichment analysis was carried out by this group, and a model for predicting clinical prognosis was established by combining this group with clinical factors.

RESULTS

Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) analysis showed that there were several immune-related pathways and immune infiltration differences between high- and low-risk groups. A prognostic model integrating 10 ferroptosis-related genes (FR-DEGs), and clinical factors were constructed and validated in an external cohort.

CONCLUSIONS

The FR-DEGs signature was related to immune infiltration, and a model based on FR-DEGs and clinical factors was established to predict the prognosis of patients with LUAD.

KPNB1 Inhibitor Importazole Reduces Ionizing Radiation-Increased Cell Surface PD-L1 Expression by Modulating Expression and Nuclear Import of IRF1

Programmed death-ligand 1 (PD-L1) is an immune checkpoint molecule that negatively regulates anti-tumor immunity. Recent reports indicate that anti-cancer treatments, such as radiation therapy, increase PD-L1 expression on the surface of tumor cells. We previously reported that the nuclear transport receptor karyopherin-β1 (KPNB1) is involved in radiation-increased PD-L1 expression on head-and-neck squamous cell carcinoma cells. However, the mechanisms underlying KPNB1-mediated, radiation-increased PD-L1 expression remain unknown. Thus, the mechanisms of radiation-increased, KPNB1-mediated PD-L1 expression were investigated by focusing on the transcription factor interferon regulatory factor 1 (IRF1), which is reported to regulate PD-L1 expression. Western blot analysis showed that radiation increased IRF1 expression. In addition, flow cytometry showed that IRF1 knockdown decreased cell surface PD-L1 expression of irradiated cells but had a limited effect on non-irradiated cells. These findings suggest that the upregulation of IRF1 after irradiation is required for radiation-increased PD-L1 expression. Notably, immunofluorescence and western blot analyses revealed that KPNB1 inhibitor importazole not only diffused nuclear localization of IRF1 but also decreased IRF1 upregulation by irradiation, which attenuated radiation-increased PD-L1 expression. Taken together, these findings suggest that KPNB1 mediates radiation-increased cell surface PD-L1 expression through both upregulation and nuclear import of IRF1.

KIF11 Serves as an Independent Prognostic Factor and Therapeutic Target for Patients With Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is challenging in clinical practice due to the poor understanding of molecular mechanisms and limited therapeutic targets. Herein, the work aimed to use bioinformatics to identify a promising molecular target for LUAD therapy.

Methods

Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) dataset were used for a weighted gene co-expression network analysis (WGCNA) to screen the hub gene. After a prognostic estimation with meta-analysis and COX regression analysis, we performed a function analysis on the corresponding gene. The ESTIMATE and CIBERSORT methods were adopted to analyze the association of the hub gene with the tumor microenvironment (TME). A cohort of functional assays was conducted to establish the functional roles of the hub gene in A549 and PC-9 cells.

Results

Our screen identified KIF11 as a prognostic factor, which indicated the poor overall survival and the worse progression-free survival in LUAD patients. Additionally, KIF11 was primarily involved in cell cycle, TME alteration and tumor-infiltrating immune cells proportions. KIF11 knockdown exerted inhibitory effects on cell proliferation, migration, and invasion. Results of the flow cytometry analysis revealed that KIF11 knockdown induced a G2/M phase arrest and improved apoptosis in LUAD cells.

Conclusions

KIF11 is essential for LUAD cell proliferation and metastasis, and it may serve as an independent prognostic factor as well as a promising therapeutic target for LUAD patients.

Perioperative safety and feasibility outcomes of stage IIIA-N2 non-small cell lung cancer following neoadjuvant immunotherapy or neoadjuvant chemotherapy: a retrospective study

Background

We sought to determine the perioperative safety and feasibility outcomes of stage IIIA (N2) non-small cell lung cancer (NSCLC) following neoadjuvant immunotherapy or neoadjuvant chemotherapy.

Methods

The clinical details of patients who attended the Affiliated Hospital of Qingdao University between January 2019 and December 2020 were retrospectively evaluated. Eligible patients had pathologically proven stage IIIA (N2) NSCLC and were randomly prescribed neoadjuvant therapy. Those in the neoadjuvant immunotherapy group received two cycles of nivolumab (3 mg/kg) and those in the control group received neoadjuvant chemotherapy (1,000 mg/m² gemcitabine and 80 mg/m² cisplatin). All patients were scheduled to undergo surgery. The primary endpoint was the risk of major complications within 30 days of surgery and the secondary endpoints were interval to surgery and 30-day mortality.

Results

A total of 107 eligible patients were evaluated of whom 25 were allocated to the neoadjuvant immunotherapy group and 82 to the neoadjuvant chemotherapy group. The median interval to surgery was similar in the two groups at 29.2 days [95% confidence interval (CI), 27.1 to 31.4 days] in the immunotherapy group and 28.7 days (95% CI, 27.6 to 29.8 days) in the chemotherapy group (P=0.656). While treatment-related adverse events were reported in most patients, all 25 patients completed two cycles of neoadjuvant immunotherapy and 80 of 82 patients completed two cycles of neoadjuvant chemotherapy, although one patient in the latter group died within 30 days of surgery. There was no statistically significant difference between the groups in the probability of grade 3 or higher postoperative complications within 30 days after surgery (P=0.757).

Conclusions

Most patients achieved the primary and secondary endpoints of the study. However, the major pathological response (MPR) showed statistically significant differences between the neoadjuvant immunotherapy and neoadjuvant chemotherapy groups.

Identification of CXCL13/CXCR5 axis's crucial and complex effect in human lung adenocarcinoma

Immune escape and low response to immunotherapy are crucial challenges in present lung cancer treatment. In this study, we constructed a new immune-related classifier based on CXCL13/CXCR5, an important tumor microenvironment component and strongly related with the formation of tertiary lymphoid structures (TLSs) in tumor microenvironment. With the classifier, we divided patients into two main clusters and each cluster was further divided into subcluster (A1, A2, B1, B2, B3). In the later analysis, we noticed that patients in subcluster B3 had a distinct advantage over patients in A1 in survival time and immune infiltration, suggesting a more favorable response to immunotherapy. Moreover, we demonstrated the genetic and epigenetic regulation related to the subclusters and recovered four key differentially expressed genes (ERBB4, GRIN2A, IL2RA, CCND2). With several experiments, we verified the unique role of CCND2 in tumor metastasis and T cell apoptosis. Overexpressing CCND2 could significantly impair cancer cell abilities of migration and invasion and downregulate PD-1/PD-L1 signaling, which may be the cause of T cell apoptosis reduction. In the end, we constructed a regression risk model that could successfully predict ICI response. To sum up, our study established new stratification models that can successfully predict patient survival and response to ICI. And using integrative analysis of multi-omics data, four key DEGs were noticed, and CCND2, one of the four genes, was identified as a potential treatment target because of its effect in tumor metastasis and T cell apoptosis.

A narrative review of MET inhibitors in non-small cell lung cancer with MET exon 14 skipping mutations

Treatment of advanced non-small cell lung cancer (NSCLC) has radically improved in the last years due to development and clinical approval of highly effective agents including immune checkpoint inhibitors (ICIs) and oncogene-directed therapies. Molecular profiling of lung cancer samples for activated oncogenes, including epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), c-ros oncogene 1 (ROS1) and BRAF, is routinely performed to select the most appropriate up-front treatment. However, the identification of new therapeutic targets remains a high priority. Recently, MET exon 14 skipping mutations have emerged as novel actionable oncogenic alterations in NSCLC, sensitive to MET inhibition. In this review we discuss: (I) MET gene and MET receptor structure and signaling pathway; (II) MET exon 14 alterations; (III) current data on MET inhibitors, mainly focusing on selective MET tyrosine kinase inhibitors (TKIs), in the treatment of NSCLC with MET exon 14 skipping mutations. We identified the references for this review through a literature search of papers about MET, MET exon 14 skipping mutations, and MET inhibitors, published up to September 2020, by using PubMed, Scopus and Web of Science databases. We also searched on websites of main international cancer congresses (ASCO, ESMO, IASLC) for ongoing studies presented as abstracts. MET exon 14 skipping mutations have been associated with clinical activity of selective MET inhibitors, including capmatinib, that has recently received approval by FDA for clinical use in this subgroup of NSCLC patients. A large number of trials are testing MET inhibitors, also in combinatorial therapeutic strategies, in MET exon 14-altered NSCLC. Results from these trials are eagerly awaited to definitively establish the role and setting for use of these agents in NSCLC patients.

Uncovering the Anti-Lung-Cancer Mechanisms of the Herbal Drug FDY2004 by Network Pharmacology

With growing evidence on the therapeutic efficacy and safety of herbal drugs, there has been a substantial increase in their application in the lung cancer treatment. Meanwhile, their action mechanisms at the system level have not been comprehensively uncovered. To this end, we employed a network pharmacology methodology to elucidate the systematic action mechanisms of FDY2004, an anticancer herbal drug composed of Moutan Radicis Cortex, Persicae Semen, and Rhei Radix et Rhizoma, in lung cancer treatment. By evaluating the pharmacokinetic properties of the chemical compounds present in FDY2004 using herbal medicine-associated databases, we identified its 29 active chemical components interacting with 141 lung cancer-associated therapeutic targets in humans. The functional enrichment analysis of the lung cancer-related targets of FDY2004 revealed the enriched Gene Ontology terms, involving the regulation of cell proliferation and growth, cell survival and death, and oxidative stress responses. Moreover, we identified key FDY2004-targeted oncogenic and tumor-suppressive pathways associated with lung cancer, including the phosphatidylinositol 3-kinase-Akt, mitogen-activated protein kinase, tumor necrosis factor, Ras, focal adhesion, and hypoxia-inducible factor-1 signaling pathways. Overall, our study provides novel evidence and basis for research on the comprehensive anticancer mechanisms of herbal medicines in lung cancer treatment.

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside

Background

Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).

Methods

The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram.

Results

In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873).

Conclusions

We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.