[Current Status and Prospect of PD-1/PD-L1 Immune Checkpoint Inhibitor Therapy 
in Elderly Patients with Advanced NSCLC].

The incidence of cancer is closely correlated with age, as 75% of non-small cell lung cancer (NSCLC) patients are aged at least 65 years. The availability of immune checkpoint inhibitors (ICIs) has altered the available NSCLC therapeutic pattern. Limited studies on elderly patients have demonstrated that ICIs as monotherapy provide substantial benefits for patients aged 65-75 years, showing no significant difference compared to younger patients. This benefit is also observed in combination with immune-combined chemotherapy or radiotherapy. For individuals older than 75 years, the survival effect was not evident, though. Immune-related adverse events (irAEs) with ICIs alone were similar in incidence across age categories. Immune-combination chemotherapy resulted in a higher incidence of irAEs than chemotherapy alone, and patients ≥75 years of age were more likely to experience higher-grade irAEs. Besides the fact that immunosenescence in older patients influences the immune milieu in a multifaceted manner, which in turn impacts the effectiveness of immunotherapy, the prognosis is also influenced by the Eastern Cooperative Oncology Group performance status (ECOG PS) score, among other factors. For certain individuals aged ≥75 years or in poor physical health, immunotherapy combined with low-intensity chemotherapy has emerged as a viable treatment option. However, there are fewer related studies, so there should be a conscious effort to increase the number of elderly patients enrolled in the trial and a comprehensive assessment to explore individualized treatment options. To provide additional references and guidance for immunotherapy in elderly NSCLC patients and to propose new therapeutic perspectives in combination with their characteristics, this review aims to summarize and analyze the pertinent studies on the application of programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors in these patients.
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[Research Progress of Lung Cancer Vaccines].

With the development of medical technology, tumor vaccines as a novel precise immunotherapy approach have gradually received attention in clinical applications. Against the backdrop of the global corona virus disease 2019 (COVID-19) outbreak, vaccine technology has further advanced. Depending on the types of antigens, tumor vaccines can be divided into whole-cell vaccines, peptide vaccines, messenger ribonucleic acid (mRNA) vaccines, recombinant virus vaccines, etc. Although some tumor vaccines have been marketed and achieved certain therapeutic effects, the results of tumor vaccines in clinical trials have been unsatisfactory in the past period. With the maturation of next-generation sequencing (NGS) technology and the continuous development of bioinformatics, dynamic monitoring of the entire process of tumor subpopulation development has become a reality, which has laid a solid foundation for personalized, neoantigen-centered therapeutic tumor vaccines. This article reviews the recent developments of tumor vaccines of different types, starts with lung cancer and summarizes the achievements of tumor vaccines in clinical applications, and provides an outlook for the future development of antigen-centered tumor vaccines.
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[Eukaryotic expression of human Y-box binding protein 1 (YB-1) promotes the proliferation and migration of human HepG2 cells].

Objective To establish the eukaryotic expression vector of Y-box-binding protein 1 (YB-1) with FLAG-tagged and transfect it into hepatocellular carcinoma HepG2 cells to identify the effects of YB-1 on the proliferation and migration. Methods Human YB-1 gene was amplified from the human ovary library by PCR. YB-1 fraction was double enzyme digested and connected with pcDNA3.0-FLAG vector to construct eukaryotic expression vector pcDNA3.0-FlAG-YB-1, which was transfected into HepG2 cells. The expression of YB-1 was detected by Western blotting, and the effect of YB-1 on the proliferation of HepG2 cells was determined by CCK-8 assay and clone formation. The effect of YB-1 on the migration of HepG2 cells was analyzed by wound healing assays. Results The eukaryotic expression vector pcDNA3.0-FLAG-YB-1 was successfully established. YB-1 protein can be expressed in HepG2 cells, and YB-1 promoted the proliferation and migration of HepG2 cells. Conclusion YB-1 promotes the proliferation and migration of HepG2 cells.

Clinicopathological characteristics and prognostic significance of HDAC11 protein expression in non-small cell lung cancer: a retrospective study.

Background: Although the prognosis of non-small cell lung cancer (NSCLC) can be assessed based on pathological type, disease stage and inflammatory indicators, the prognostic scoring model of NSCLC still needs to improve. HDAC11 is associated with poor prognosis of partial tumors, but its prognostic relationship with NSCLC is poorly understood. In this study, the role of HDAC11 in NSCLC was studied to evaluate relationship with disease prognosis and potential therapeutic target. Methods: The clinicopathological and paracancerous tissues of patients with NSCLC primarily diagnosed in Tangdu Hospital from 2009 to 2013 were collected. Follow-up of patients were made every three months and the last follow-up period was December 2018. The expression of HDAC11 was assessed by immunohistochemistry (IHC). Then, weighted gene co-expression network analysis (WGCNA) was used to analyze the relationship between HDAC11 expression and the prognosis of lung adenocarcinoma (LUAD) patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Kaplan-Meier plotter database was used to verify the connection between hub genes and tumor stage and prognosis. We accessed the relationship between HDAC11 expression and clinicopathological features, and impact on the prognosis. Results: The study assessed 326 patients with NSCLC. Compared with adjacent tissues, HDAC11 expression was upregulated (HR =1.503, 95% CI: 1.172 to 1.927, P=0.001). Kaplan-Meier survival analyses showed that HDAC11 expression was closely related to OS of NSCLC patients (P=0.0011). Univariate and multivariate analyses showed that the independent risk factors of OS were clinical stage, HDAC11 expression, and HDAC11 differentiation (all P≤0.001). HDAC11 was significantly associated with prognosis in LUAD. A total of 1,174 differential genes and WGCNA were obtained to construct a co-expression network in LUAD. The GO and KEGG pathway enrichment analyses showed the relevance with staphylococcus aureus infection, NOD-like receptor signaling pathway, and others. The results of LUAD survival analysis showed that HDAC11-related genes NKX2-5 and FABP7 were significantly associated with LUAD prognosis. Conclusions: The high expression of HDAC11 is related to the poor prognosis of LUAD, and it is expected to become a therapeutic target and prognostic evaluation therapy for LUAD in the future. However, the relevant results need to be further studied and verified.

First-line PD-1/PD-L1 inhibitors plus chemotherapy versus bevacizumab plus chemotherapy for advanced non-squamous non-small cell lung cancer: A Bayesian network meta-analysis of randomized controlled trials.

Chemotherapy in combination with immune checkpoint inhibitor (ICI) or bevacizumab has demonstrated a superior effect for non-squamous non-small cell lung cancer (NS-NSCLC). There are still few randomized controlled trials (RCTs) investigating the differences between ICI plus chemotherapy (ICI-chemotherapy) and bevacizumab plus chemotherapy (Bev-chemotherapy) in first-line treatment of NS-NSCLC. We identified RCTs in databases and conference abstracts presented at international conferences by Sep 1, 2021. Bayesian network meta-analysis was performed using randomized effect consistency model to estimate hazard ratio (HR) and odds ratio (OR). The outcomes included overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and grade ≥ 3 treatment-related adverse events (TRAEs). Fifteen RCTs (17 articles) of 6561 advanced NS-NSCLC patients receiving ICI-chemotherapy, Bev-chemotherapy, or chemotherapy at first-line were eligible for analysis. NMA results showed that first-line ICI-chemotherapy prolonged OS (HR 0.79, 0.66-0.94) in patients with advanced NS-NSCLC compared with Bev-chemotherapy, while no differences were in PFS, ORR, and grade ≥ 3 TRAEs (p > 0.05). Ranking plots suggested that ICI-chemotherapy had the most probability to offer the best OS (probability 0.993), PFS (probability 0.658), and ORR (probability 0.565), and Bev-chemotherapy had the most risks of grade ≥ 3 TRAEs (probability 0.833). Therefore, our findings showed that first-line ICI-chemotherapy was associated with better OS than Bev-chemotherapy in patients with advanced NS-NSCLC, and more clinical trials are warranted to confirm these results.

USP18 reduces paclitaxol sensitivity of triple-negative breast cancer via autophagy.

Paclitaxol is a first-line treatment for triple-negative breast cancer (TNBC). The molecular mechanisms underlying paclitaxol resistance in TNBC remain largely unclear. In this study, differential expressed genes (DEGs) between TNBC cells and paclitaxol-resistant (taxol-R) TNBC cells were screened by bioinformatics analysis. Among these DEGs, USP18 mRNA expression was significantly increased in taxol-R TNBC cells. USP18 overexpression reduced paclitaxol sensitivity by decreasing paclitaxol-induced apoptosis and cell cycle arrest in TNBC cells. In contrast, USP18 knockdown increased paclitaxol mediated anticancer activity in taxol-R TNBC cells in vitro and in vivo. Mechanistically, USP18 induced autophagy, an important pathway in chemotherapy resistance. The autophagy inhibitor leupeptin could effectively reverse the effect of USP18 on paclitaxol resistance phenotype. These findings suggested that USP18 may be a promising target for overcoming paclitaxol resistance in TNBC.

Identification of a Ferroptosis-Related Long Noncoding RNA Prognostic Signature and Its Predictive Ability to Immunotherapy in Hepatocellular Carcinoma.

Background: Immune checkpoint blockers (ICBs) are increasingly being used to treat patients with advanced hepatocellular carcinoma (HCC), but only a third of these patients are sensitive to ICBs. Emerging evidence suggests that ferroptosis could be a novel target for antitumor treatment, and combined treatment with ferroptosis inducers might enhance sensitivity to immunotherapy. However, there is a lack of information on the crosstalk between ferroptosis-related lncRNAs and anti-tumor immunity. Therefore, we aim to explore prognostic value of ferroptosis-related lncRNAs and clarify potential role in ICBs of HCC. Methods: We obtained mRNA and lncRNA expression data from two independent cohorts (TCGA and GEO database). Univariate Cox, the least absolute shrinkage and selection operator (Lasso) algorithm and multivariate Cox analysis were used to construct a lncRNA signature, which was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan-Meier curves. Tumor-infiltrating cell (TIC) profiling and the tumor immune dysfunction and exclusion (TIDE) algorithm were used to validate the signature model and immunotherapy. Finally, we adopted RT-PCR assay to evaluate the differential expression of lncRNAs in HCC tissues in our hospital. Results: The ferroptosis-related lncRNA signature included five lncRNAs, most of which were positively correlated with clinical stage and grade. The signature could stratify patients into two risk groups, with the high-risk group associated with a shorter overall survival (OS, p < 0.05) in TCGA-LIHC and GSE76427. Besides, the AUCs of the 1-, 3-, and 5-years OS were 0.772, 0.707, and 0.666, respectively. Gene set enrichment analysis (GESA) of lncRNAs revealed enrichment of oncogenic and immune-related pathways. The TIC profiling indicated a close correlation between the signature and immune cells. Furthermore, the high-risk group had a better response to immunotherapy than low-risk group. RT-PCR demonstrated these five lncRNAs were upregulated in cancerous tissue than normal tissues. Conclusions: The ferroptosis-related lncRNA signature could accurately predict the OS of HCC patients and may serve as an independent clinical factor for patients' outcomes. Ferroptosis-related lncRNAs may remodel the tumor microenvironment (TME) and affect the anti-cancer ability of ICBs, and therefore, could potentially act as an indicator for the response to immunotherapy in HCC.

RHOV promotes lung adenocarcinoma cell growth and metastasis through JNK/c-Jun pathway.

Lung adenocarcinoma (LUAD) is a common type of lung cancer with high frequent metastasis and a high death rate. However, genes responsible for LUAD metastasis are still largely unknown. Here, we identify an important role of ras homolog family member V (RHOV) in LUAD metastasis using a combination of bioinformatic analysis and functional experiments. Bioinformatic analysis shows five hub LUAD metastasis driver genes (RHOV, ZIC5, CYP4B1, GPR18 and TCP10L2), among which RHOV is the most significant gene associated with LUAD metastasis. High RHOV expression predicted shorter overall survival in LUAD patients. RHOV overexpression promotes proliferation, migration, and invasion of LUAD cells, whereas RHOV knockdown inhibits these biological behaviors. Moreover, knockdown of RHOV suppresses LUAD tumor growth and metastasis in nude mice. Mechanistically, RHOV activates Jun N-terminal Kinase (JNK)/c-Jun signalling pathway, an important pathway in lung cancer development and progression, and regulates the expression of markers of epithelial-to-mesenchymal transition, a process involved in cancer cell migration, invasion and metastasis. RHOV-induced malignant biological behaviors are inhibited by pyrazolanthrone, a JNK inhibitor. Our findings indicate a critical role of RHOV in LUAD metastasis and may provide a biomarker for prognostic prediction and a target for LUAD therapy.

[Research Progress of Circular RNA in Lung Cancer].

Lung cancer is the most common malignant tumor with the highest morbidity and mortality worldwide, and its imposes an insupportable burden on patients due to its poor prognosis. The diagnosis and treatment of lung cancer is under great pressure. Therefore, it is urgent to explore effective therapeutic targets and molecular markers. Circular RNA (circRNA) is a kind of covalently closed non-coding RNAs, which has attracted much attention due to its conservation, stability and tissue specificity. Many studies have found that circRNA participates in the regulation of lung cancer through various mechanisms such as sponging miRNA and plays a part vital role in the early diagnosis, treatment and prognosis evaluation. In recent years, there have been numerous studies on circRNA in lung cancer. This paper summarizes the current progress of circRNA in the diagnosis, treatment and prognosis of lung cancer.
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Pretreatment hemoglobin level as a predictor to evaluate the efficacy of immune checkpoint inhibitors in patients with advanced non-small cell lung cancer.

BACKGROUND: Targeting immune checkpoints represents an immense breakthrough in cancer therapeutics. The prognostic value of hemoglobin (Hb) has been investigated in many malignancies including non-small cell lung cancer (NSCLC). However, the prognostic impact of pretreatment Hb count for immune checkpoint inhibitors (ICIs) in advanced NSCLC patients remains unclear. METHODS: A total of 310 late-stage NSCLC patients who received ICI therapies between January 2015 and March 2019 were prospectively enrolled. We used a propensity score-matched cohort analysis for this study. Patients' clinicopathological characteristics and pretreatment Hb concentration were assessed against the progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier method and Cox proportional hazards regression. RESULTS: A propensity score (PS)-matched cohort analysis was applied to adjust for potential bias and to create two comparable groups according to patients' clinicopathological characteristics. The patients with normal baseline Hb levels (⩾110 g/L) had significantly longer PFS [median: 10.0 versus 4.0 months, hazard ratio (HR): 0.63, 95% confidence interval (CI): 0.46-0.86; p = 0.001] and OS [median: 17.6 versus 10.5 months, HR (95% CI): 0.56 (0.40-0.79); p < 0.001] than those with decreased Hb count (<110 g/L) in a PS-matched cohort (n = 255). For patients with normal pretreatment Hb levels, ICI combination therapy was significantly associated with better PFS [median: 11.1 versus 8.0 months, HR (95% CI): 0.74 (0.50-1.06); p = 0.09] and OS [median: 26.0 versus 12.9 months, HR (95% CI): 0.56 (0.37-0.86); p = 0.008] than monotherapy, but there was no such trend for patients with decreased baseline Hb levels. CONCLUSION: Our findings showed that normal pretreatment Hb count served as a favorable prognostic marker in advanced NSCLC patients treated with ICIs, representing an economical biomarker with readily measuring performance among all reported ones.

Dynamics of Serum Tumor Markers Can Serve as a Prognostic Biomarker for Chinese Advanced Non-small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors.

Background: Serum tumor markers carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cytokeratin 19 fragment (CYFRA21-1) and squamous-cell carcinoma-related antigen (SCC-Ag) are routinely used for monitoring the response to chemotherapy or targeted therapy in advanced-stage non-small cell lung cancer (NSCLC), however their role in immunotherapy remains unclear. The aim of this study was to investigate whether dynamics of these serum markers were associated with the efficacy and prognosis of Chinese late-stage NSCLC patients treated with programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) inhibitors. Methods: We initiated a longitudinal prospective study on advanced NSCLC patients treated with PD-1/PD-L1 inhibitors in Chinese PLA general hospital (Beijing, China). Blood samples of baseline and after 6 weeks' treatment were collected. CT scan were used by all patients to evaluate treatment efficacy according to RECIST 1.1. Serum tumor markers levels were measured with an electrochemical luminescence for SCC-Ag and with a chemiluminescent microparticle immunoassay for serum CEA, CA125, and CYFRA21-1. At least 20% decreases of the biomarkers from baseline were considered as meaningful improvements after 6 weeks of treatment with immune checkpoint inhibitors (ICIs). Optimization-based method was used to balance baseline covariates between different groups. Associations between serum tumor biomarker improvements and objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were analyzed. Results: A total of 308 Chinese patients with advanced NSCLC were enrolled in the study. After balancing baseline covariates, patients with meaningful improvements in <2 out of 4 biomarkers (CEA, CA125, CYFRA21-1, and SCC-Ag) was ended up with lower ORR (0.08 vs. 0.35, p < 0.001), shorten PFS (median: 5.4 vs. 12.5 months, p < 0.001), and OS (median: 11.7 vs. 25.6 months, p < 0.001) in the total population. Subgroup analysis of patients with adenocarcinoma revealed that patients with meaningful improvements in <2 out of 4 biomarkers had significant lower ORR (0.06 vs. 0.36, p < 0.001), shorten PFS (median: 4.1 vs. 11.9 months, p < 0.001), and OS (median: 11.9 vs. 24.2 months, p < 0.001). So as in patients with squamous cell carcinoma, meaningful improvements in at least 2 out of 4 biomarkers were linked to better ORR (0.42 vs. 0.08, p = 0.014), longer PFS (median: 13.1 vs. 5.6 months, p = 0.001), and OS (median: 25.6 vs. 10.9 months, p = 0.06). Conclusions: The dynamic change of CEA, CA125, CYFRA21-1, and SCC-Ag from baseline have prognostic value for late-stage NSCLC patients treated with PD-1/PD-L1 inhibitors. Decrease of associated biomarkers serum levels were associated with favorable clinical outcomes.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Immunotherapy beyond progression in patients with advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) represent a great breakthrough in the treatment of advanced non-small cell lung cancer (aNSCLC). However, whether immunotherapy beyond progression (IBP) is effective for aNSCLC has yet to be established. Therefore, a retrospective clinical study was conducted to investigate the efficacy of IBP in patients with aNSCLC under real-world conditions. METHODS: A total of 125 Chinese patients with aNSCLC who experienced progressive disease (PD) after receiving monotherapy or combination therapy (combined with chemotherapy or/and antiangiogenic therapy) with programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) inhibitors between January 2015 and March 2019 were enrolled. Patients who were treated with ICIs for more than 6 weeks after PD were defined as IBP (n=39), while those who received ICI treatment for less than 6 weeks or discontinued it due to PD were defined as non-IBP (n=86). Patient clinical characteristics were evaluated. An optimization-based method was applied to balance the clinical baseline characteristics between the two groups. RESULTS: In total population, the IBP group had longer overall survival (median OS, 26.6 vs. 9.5 months; HR, 0.40; 95% CI: 0.23-0.69; P<0.001) and progression-free survival (median PFS, 8.9 vs. 4.1 months; HR, 0.41; 95% CI: 0.26-0.65; P<0.001), compared with the non-IBP group. Despite no significant difference in objective response rate (ORR, 15.4% vs. 11.6%, P=0.560), disease control rate (DCR) was significantly higher in the IBP group (89.7% vs. 61.6%, P<0.001). After balancing baseline covariates, the IBP group also had longer OS (median: 26.6 vs. 10.7 months; HR, 0.40; 95% CI: 0.19-0.84; P=0.015) and PFS (median: 9.7 vs. 4.3 months; HR, 0.28; 95% CI: 0.15-0.51; P<0.001), with a benefit in either of patients previously treated with ICI monotherapy or in combination therapy and with non-response to the previously ICI. CONCLUSIONS: IBP is associated with longer OS and PFS in patients with aNSCLC. Our findings may suggest new therapeutic options for patients with aNSCLC who experienced disease progression after initial immunotherapy.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning.

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split. Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model. Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.