Genome-wide identification of phasiRNAs in Arabidopsis thaliana, and insights into biogenesis, temperature sensitivity, and organ specificity.

The knowledge of biogenesis and target regulation of the phased small interfering RNAs (phasiRNAs) needs continuous update, since the phasiRNA loci are dynamically evolved in plants. Here, hundreds of phasiRNA loci of Arabidopsis thaliana were identified in distinct tissues and under different temperature. In flowers, most of the 24-nt loci are RNA-dependent RNA polymerase 2 (RDR2)-dependent, while the 21-nt loci are RDR6-dependent. Among the RDR-dependent loci, a significant portion is Dicer-like 1-dependent, indicating the involvement of microRNAs in their expression. Besides, two TAS candidates were discovered. Some interesting features of the phasiRNA loci were observed, such as the strong strand bias of phasiRNA generation, and the capacity of one locus for producing phasiRNAs by different increments. Both organ specificity and temperature sensitivity were observed for phasiRNA expression. In leaves, the TAS genes are highly activated under low temperature. Several trans-acting siRNA-target pairs are also temperature-sensitive. In many cases, the phasiRNA expression patterns correlate well with those of the processing signals. Analysis of the rRNA-depleted degradome uncovered several phasiRNA loci to be RNA polymerase II-independent. Our results should advance the understanding on phasiRNA biogenesis and regulation in plants.

Beneficial implications of adjuvant chemotherapy for stage IB lung adenocarcinoma exhibiting elevated SUVmax in FDG-PET/CT: a retrospective study from a single center.

Background: Controversy surrounds the efficacy of adjuvant chemotherapy (ACT) in the treatment of stage I lung adenocarcinoma (LUAD). The objective of this study was to examine the impact of the maximum standardized uptake value (SUVmax) as measured by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) on the efficacy of ACT in patients diagnosed with stage I LUAD. Methods: We scrutinized the medical records of 928 consecutive patients who underwent complete surgical resection for pathological stage I LUAD at our institution. The ideal cut-off value for primary tumor SUVmax in terms of disease-free survival (DFS) and overall survival (OS) was determined using the X-tile software. The Kaplan-Meier method and Cox regression analysis were used for survival analysis. Results: Based on the SUVmax algorithm, the ideal cutoff values were determined to be 4.9 for DFS and 5.0 for OS. We selected 5.0 as the threshold because OS is the more widely accepted predictive endpoint. In a multivariate Cox regression analysis, SUVmax ≥ 5.0, problematic IB stage, and sublobectomy were identified as independent risk factors for poor DFS and OS. It is noteworthy that patients who were administered ACT had significantly longer DFS and OS than what was observed in the subgroup of patients with pathological stage IB LUAD and SUVmax ≥ 5.0 (p < 0.035 and p ≤ 0.046, respectively). However, there was no observed survival advantage for patients in stages IA or IB who had an SUVmax < 5.0. Conclusion: The preoperative SUVmax of tumors served as an indicator of the impact of ACT in the context of completely resected pathological stage I LUAD. Notably, patients within the Stage IB category exhibiting elevated SUVmax levels emerged as a subgroup experiencing substantial benefits from postoperative ACT.

Quantitative MRI on MR-Linacs: Towards Biological Image-Guided Adaptive Radiotherapy.

Recognizing the potential of quantitative imaging biomarkers (QIBs) in radiotherapy, many studies have investigated the prognostic value of quantitative MRI (qMRI). With the introduction of MRI-guided radiotherapy systems, the practical challenges of repeated imaging have been substantially reduced. Since patients are treated inside an MRI scanner, acquisition of qMRI can be done during each fraction with limited or no prolongation of the fraction duration. In this review paper, we identify the steps that need been taken to move from MR as an imaging technique to a useful biomarker for MRI-guided radiotherapy (MRgRT).

Spatial transcriptomics delineates molecular features and cellular plasticity in lung adenocarcinoma progression.

Indolent (lepidic) and aggressive (micropapillary, solid, and poorly differentiated acinar) histologic subtypes often coexist within a tumor tissue of lung adenocarcinoma (LUAD), but the molecular features associated with different subtypes and their transitions remain elusive. Here, we combine spatial transcriptomics and multiplex immunohistochemistry to elucidate molecular characteristics and cellular plasticity of distinct histologic subtypes of LUAD. We delineate transcriptional reprogramming and dynamic cell signaling that determine subtype progression, especially hypoxia-induced regulatory network. Different histologic subtypes exhibit heterogeneity in dedifferentiation states. Additionally, our results show that macrophages are the most abundant cell type in LUAD, and identify different tumor-associated macrophage subpopulations that are unique to each histologic subtype, which might contribute to an immunosuppressive microenvironment. Our results provide a systematic landscape of molecular profiles that drive LUAD subtype progression, and demonstrate potentially novel therapeutic strategies and targets for invasive lung adenocarcinoma.

An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans.

INTRODUCTION: It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time. MATERIALS AND METHODS: We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters. RESULTS: The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals. CONCLUSION: This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.

Synthesis, cytotoxicity, antioxidant activity and molecular modeling of new NSAIDs-EBS derivatives.

Two series of NSAIDs-EBS derivatives (5a-j and 9a-i) based on the hybridization of nonsteroidal anti-inflammatory drugs (NSAIDs) skeleton and Ebselen moiety were synthesized. Their cytotoxicity was evaluated against five types of human cancer cell lines, BGC-823 (human gastric cancer cell line), SW480 (human colon adenocarcinoma cells), MCF-7 (human breast adenocarcinoma cells), HeLa (human cervical cancer cells), A549 (human lung carcinoma cells). Moreover, the most active compound 5j showed IC50 values below 3 µM in all cancer cell lines and with remarkable anticancer activity against MCF-7 (1.5 µM) and HeLa (1.7 µM). The redox properties of the NSAIDs-EBS derivatives prepared herein were conducted by 2, 2-didiphenyl-1-picrylhydrazyl (DPPH), bleomycin dependent DNA damage and glutathione peroxidase (GPx)-like assays. Finally, TrxR1 inhibition activity assay and molecular docking study revealed NSAIDs-EBS derivatives could serve as potential TrxR1 inhibitor.

Association of early immune-related adverse events with treatment efficacy of neoadjuvant Toripalimab in resectable advanced non-small cell lung cancer.

Background: Neoadjuvant immunotherapy with anti-PD-1 was proved promising in resectable non-small cell lung cancer (NSCLC). Immune-related adverse events (irAEs) have been preliminarily implicated their association with treatment efficacy. Here we elucidated the early onset of irAEs associated with better clinical outcomes in a prospective study (Renaissance study). Methods: We conducted the prospective study of NSCLC patients treated by neoadjuvant Toripalimab (240mg, every 3 weeks) plus double platinum-based chemotherapy from December 2020 to March 2022 at Peking University Cancer Hospital. Patients were enrolled if they have resectable IIB-IIIB NSCLC without EGFR/ALK mutation. Data were analyzed to explore the relationship between clinical outcome and irAEs after neoadjuvant treatment. A multidisciplinary team including physicians, surgeons, and radiologists, confirmed the irAEs according to the clinical manifestation. The relationship between irAEs and pathological outcomes was analyzed. The Renaissance study was approved by the Peking University Ethic board (2020YJZ58) and registered at https://clinicaltrials.gov/ as NCT04606303. Results: Fifty-five consecutive patients were enrolled with a male-to-female ratio of 10:1, the median age was 62 years old (IQR: 45-76), of which 44 patients (80%) were diagnosed with squamous cell carcinoma. Forty-eight of 55 patients finally received thoracic surgery with a median preoperative waiting time of 67 days (IQR 39-113 days). Pathological results demonstrated that 31 (64.6%) patients achieved major pathological response (MPR) and 24 (50.0%) achieved complete pathological response (pCR). Among 48 patients who received R0 resection, immunotherapy-related thyroid dysfunction, rash/pruritus and enteritis occurred in 11 patients (22.9%), 7 patients (14.6%), and 1 patient (2.1%), respectively. Six patients (54.5%) with thyroid dysfunction achieved MPR with 5 (45.5%) achieved pCR, and a median time to onset was 45 days (IQR 21-91 days). Six patients (85.7%) with rash or pruritus achieved MPR and 5 patients (71.4%) achieved pCR, with median time to onset being 8 days (IQR 6-29 days). Furthermore, irAEs had no significant influence on operation time (170.6 min vs 165.7 min, P=0.775), intraoperative blood loss (67.4 mL vs 64.3 mL, P=0.831) and preoperative waiting time (93 days vs 97 days, P=0.630) when comparing with patients without irAEs (Figure 1).Figure 1Comparison of operation time (A), intraoperative blood loss (B), and preoperative waiting time (C) between "with irAEs" and "without irAEs". Conclusion: The immunotherapy-related rash is potentially associated with pathological outcomes in NSCLC patients after neoadjuvant chemo-immunotherapy, suggesting easy-to-find irAEs, such as rash, can be used as indicators to predict response to neoadjuvant chemo-immunotherapy.Clinical trial registration: clinicaltrials.gov/, identifier NCT04606303.

A Multi-modal Heterogeneous Graph Forest to Predict Lymph Node Metastasis of Non-small Cell Lung Cancer.

Lymph node metastasis (LNM) is critical for treatment decision-making for cancer patients, but it is difficult to diagnose accurately before surgery. Machine learning can learn nontrivial knowledge from multi-modal data to support accurate diagnosis. In this paper, we proposed a Multi-modal Heterogeneous Graph Forest (MHGF) approach to extract the deep representations of LNM from multi-modal data. Specifically, we first extracted the deep image features from CT images to represent the pathological anatomic extent of the primary tumor (pathological T stage) using a ResNet-Trans network. And then, a heterogeneous graph with six vertices and seven bi-directional relations was defined by medical experts to describe the possible relations between the clinical and image features. After that, we proposed a graph forest approach to construct the sub-graphs by removing each vertex in the complete graph iteratively. Finally, we used graph neural networks to learn the representations of each sub-graph in the forest to predict LNM and averaged all the prediction results as final results. We conducted experiments on 681 patients' multi-modal data. The proposed MHGF achieves the best performances with a 0.806 AUC value and 0.513 AP value compared with state-of-art machine learning and deep learning methods. The results indicate that the graph method can explore the relations between different types of features to learn effective deep representations for LNM prediction. Moreover, we found that the deep image features about the pathological anatomic extent of the primary tumor are useful for LNM prediction. And the graph forest approach can further improve the generalization ability and stability of the LNM prediction model.

Osimertinib as neoadjuvant therapy in patients with EGFR-mutant resectable stage II-IIIB lung adenocarcinoma (NEOS): A multicenter, single-arm, open-label phase 2b trial.

OBJECTIVES: Osimertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), has been approved for EGFR-mutant non-small-cell lung cancer (NSCLC). We aimed to evaluate the efficacy and safety of neoadjuvant osimertinib in patients with EGFR-mutant resectable locally advanced NSCLC. MATERIALS AND METHODS: This single-arm, phase 2b trial (ChiCTR1800016948) was conducted at six centers in mainland China. Patients with a measurable stage IIA-IIIB (T3-4 N2) lung adenocarcinoma and EGFR exon 19 and/or 21 mutations were enrolled. The patients were treated with osimertinib 80 mg orally once per day for six weeks, followed by surgical resection. The primary endpoint was the objective response rate (ORR) assessed according to the Response Evaluation Criteria In Solid Tumors version 1.1. RESULTS: Between October 17, 2018, and June 08, 2021, 88 patients were screened for eligibility. Forty patients were enrolled and treated with neoadjuvant osimertinib therapy. The ORR was 71.1 % (27/38) (95 % confidence interval: 55.2-83.0) in 38 patients who completed the 6-week osimertinib treatment. Thirty-two patients underwent surgery, and 30 (93.8 %) underwent successful R0 resection. Thirty (75.0 %) of 40 patients had treatment-related adverse events during neoadjuvant treatment, and three (7.5 %) had treatment-related adverse events of grade 3. The most common treatment-related adverse events were rash (n = 20 [50 %]), diarrhea (n = 12 [30 %]), and oral ulceration (n = 12 [30 %]). CONCLUSIONS: The third-generation EGFR TKI osimertinib, with satisfying efficacy and acceptable safety profile, could be a promising neoadjuvant therapy in patients with resectable EGFR-mutant NSCLC.

Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study.

BACKGROUND: Anthropomorphic phantoms are used in surgical planning and intervention. Ideal accuracy and high efficiency are prerequisites for its clinical application. We aimed to develop a fully automated artificial intelligence-based three-dimensional (3D) reconstruction system (AI system) to assist thoracic surgery and to determine its accuracy, efficiency, and safety for clinical use. METHODS: This AI system was developed based on a 3D convolutional neural network (CNN) and optimized by gradient descent after training with 500 cases, achieving a Dice coefficient of 89.2%. Accuracy was verified by comparing virtual structures predicted by the AI system with anatomical structures of patients in retrospective (n = 113) and prospective cohorts (n = 139) who underwent lobectomy or segmentectomy at the Peking University Cancer Hospital. Operation time and blood loss were compared between the retrospective cohort (without AI assistance) and prospective cohort (with AI assistance) for safety evaluation. The time consumption for reconstruction and the quality score were compared between the AI system and manual reconstruction software (Mimics®) for efficiency validation. This study was registered at https://www.chictr.org.cn as ChiCTR2100050985. FINDINGS: The AI system reconstructed 13,608 pulmonary segmental branches from retrospective and prospective cohorts, and 1573 branches of interest corresponding to phantoms were detectable during the operation for verification, achieving 100% and 97% accuracy for segmental bronchi, 97.2% and 99.1% for segmental arteries, and 93.2% and 98.8% for segmental veins, respectively. With the assistance of the AI system, the operation time was shortened by 24.5 min for lobectomy (p < 0.001) and 20 min for segmentectomy (p = 0.007). Compared to Mimics®, the AI system reduced the model reconstruction time by 14.2 min (p < 0.001), and it also outperformed Mimics® in model quality scores (p < 0.001). INTERPRETATION: The AI system can accurately predict thoracic anatomical structures with higher efficiency than manual reconstruction software. Constant optimization and larger population validation are required. FUNDING: This study was funded by the Beijing Natural Science Foundation (No. L222020) and other sources.

Single-cell RNA sequencing reveals the cellular and molecular changes that contribute to the progression of lung adenocarcinoma.

Pure ground glass nodules (GGNs) and solid nodules (SNs) represent early and relatively late stages of lung adenocarcinoma (LUAD) in radiology, respectively. The cellular and molecular characteristics of pure GGNs and SNs have not been comprehensively elucidated. Additionally, the mechanism driving the progression of lung adenocarcinoma from pure GGN to SN in radiology is also elusive. In this study, by analyzing the single-cell transcriptomic profiles of 76,762 cells from four pure GGNs, four SNs, and four normal tissues, we found that anti-tumor immunity mediated by NK and CD8+T cells gradually weakened with the progression of LUAD and humoral immunity mediated by plasma B cells was more active in SNs. Additionally, the proliferation ability of some special epithelial cell increased during the progression process from pure GGN to SN. Furthermore, stromal cells and M2 macrophages could assist the progression of LUAD. Through comprehensive analyses, we revealed dynamic changes in cellular components and intercellular interactions during the progression of LUAD. These findings could facilitate our understanding of LUAD and discovery of novel therapeutic targets.

An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. Prognostic prediction plays a vital role in the decision-making process for postoperative non-small cell lung cancer (NSCLC) patients. However, the high imbalance ratio of prognostic data limits the development of effective prognostic prediction models. METHODS: In this study, we present a novel approach, namely ensemble learning with active sampling (ELAS), to tackle the imbalanced data problem in NSCLC prognostic prediction. ELAS first applies an active sampling mechanism to query the most informative samples to update the base classifier to give it a new perspective. This training process is repeated until no enough samples are queried. Next, an internal validation set is employed to evaluate the base classifiers, and the ones with the best performances are integrated as the ensemble model. Besides, we set up multiple initial training data seeds and internal validation sets to ensure the stability and generalization of the model. RESULTS: We verified the effectiveness of the ELAS on a real clinical dataset containing 1848 postoperative NSCLC patients. Experimental results showed that the ELAS achieved the best averaged 0.736 AUROC value and 0.453 AUPRC value for 6 prognostic tasks and obtained significant improvements in comparison with the SVM, AdaBoost, Bagging, SMOTE and TomekLinks. CONCLUSIONS: We conclude that the ELAS can effectively alleviate the imbalanced data problem in NSCLC prognostic prediction and demonstrates good potential for future postoperative NSCLC prognostic prediction.

New Organoselenium (NSAIDs-Selenourea and Isoselenocyanate) Derivatives as Potential Antiproliferative Agents: Synthesis, Biological Evaluation and in Silico Calculations.

In this study, we report on the synthesis of new organoselenium derivatives, including nonsteroidal anti-inflammatory drugs (NSAIDs) scaffolds and Se functionalities (isoselenocyanate and selenourea), which were evaluated against four types of cancer cell line: SW480 (human colon adenocarcinoma cells), HeLa (human cervical cancer cells), A549 (human lung carcinoma cells), MCF-7 (human breast adenocarcinoma cells). Among these compounds, most of the investigated compounds reduced the viability of different cancer cell lines. The most promising compound 6b showed IC50 values under 10 µM against the four cancer cell lines, particularly to HeLa and MCF-7, with IC50 values of 2.3 and 2.5 µM, respectively. Furthermore, two compounds, 6b and 6f, were selected to investigate their ability to induce apoptosis in MCF-7 cells via modulation of the expression of anti-apoptotic Bcl-2 protein, pro-inflammatory cytokines (IL-2) and proapoptotic caspase-3 protein. The redox properties of the NSAIDs-Se derivatives were conducted by 2, 2-didiphenyl-1-picrylhydrazyl (DPPH), bleomycin-dependent DNA damage and glutathione peroxidase (GPx)-like assays. Finally, a molecular docking study revealed that an interaction with the active site of thioredoxin reductase 1 (TrxR1) predicted the antiproliferative activity of the synthesized candidates. Overall, these results could serve as a promising launch point for further designs of NSAIDs-Se derivatives as potential antiproliferative agents.

Comparison of the somatic mutations between circulating tumor DNA and tissue DNA in Chinese patients with non-small cell lung cancer.

BACKGROUND: Non-invasive liquid biopsies of circulating tumor DNA (ctDNA) is a rapidly growing field in the research of non-small cell lung cancer (NSCLC). In this study, factors affecting the concordance of mutations in paired plasma and tissue and the detection rate of ctDNA in real-world Chinese patients with NSCLC were identified. METHODS: Peripheral blood and paired formalin-fixed paraffin-embedded tumor tissue samples from 125 NSCLC patients were collected and analyzed by sequencing 15 genes. Serological biomarkers were tested by immunoassay. RESULTS: The overall concordance between tumor and plasma samples and the detection rate of somatic mutations in ctDNA was 69.2% and 78.4%, respectively. The concordance and detection rate raised with clinical stage were stage I: 14.3%, 14.3%; stage II: 53.3%, 60.0%; stage III: 71.4%, 78.1%; stage IV: 74.1%, 85.2%. With increased tumor diameter, the concordance and detection rate raised from 33.33% to 71.64% and 33.33% to 80.8%, respectively. For patients with partial response, stable disease, progressive disease, and who were treatment-naïve, the concordance and detection rates were 0.0%, 62.7%, 75.2, 73.6%, and 16.7%, 61.9%, 83.3%, 86.5%, respectively. Serological markers: CEA, CA125, NSE, and CYFRA21-1 were significantly higher for patients with detectable somatic alterations in ctDNA than in those who were ctDNA negative (17.08 ng/mL vs. 3.95 ng/mL, 21.63 U/mL vs. 18.27 U/mL, 17.68 U/mL vs. 14.14 U/mL, and 6.55 U/mL vs. 3.81 U/mL, respectively). CONCLUSION: Advanced-stage, treatment naïve or poor therapy outcome, and large tumor size were associated with a high concordance and detection rate. Patients with detectable mutations in ctDNA had a higher level of carcinoembryonic antigen, CA125, NSE, and CYFRA21-1.

Hsa_circ_0007312 Promotes Third-Generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor Resistance through Pyroptosis and Apoptosis via the MiR-764/MAPK1 Axis in Lung Adenocarcinoma Cells.

Purposes: Osimertinib is a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) used for patients with gefitinib (first-generation EGFR-TKI) resistance, but osimertinib resistance inevitably occurs. Therefore, it is necessary to explore the mechanisms of osimertinib resistance. Materials and Methods: We performed quantitative real-time polymerase chain reaction to detect hsa_circ_0007312 (circ7312), miR-764, and MAPK1 expressions in tissues and cells. Western blotting was used to detect protein levels in cells. Cell Counting Kit-8, apoptotic, and Transwell assays were used to explore biological functions. Luciferase assays were used to identify the interactions between circ7312 and miR-764, MAPK1 and miR-764. A xenograft experiment was performed to clarify the role of circ7312 in vivo. Public datasets were used to identify the relation between circ7312 expression and the cell half maximal inhibitory concentration value of osimertinib in 41 lung adenocarcinoma cell lines. The Student t-test, Kaplan-Meier analysis, and Pearson correlation analysis were used in data analysis. Results: We found that circ7312 knockdown increased miR-764 expression and decreased MAPK1 expression, and circ7312 regulated MAPK1 by sponging miR-764. In addition, high circ7312 expression has significant positive correlation with osimertinib IC50 values, circ7312 knockdown decreased the cell half maximal inhibitory concentration value of osimertinib and increased pyroptosis and apoptosis by sponging the miR-764/MAPK1 axis. We also found that circ7312 and MAPK1 were highly expressed in tumor tissues and related to poor prognosis. Xenograft experiments revealed that circ7312 knockdown decreased osimertinib resistance in vivo. Conclusion: We demonstrated that the inhibition of circ7312 decreased osimertinib resistance by promoting pyroptosis and apoptosis via the miR-764/MAPK1 axis, providing a novel target for osimertinib resistance therapy.

Comprehensive genomic analysis of primary malignant melanoma of the esophagus reveals similar genetic patterns compared with epithelium-associated melanomas.

Primary malignant melanoma of the esophagus (PMME) is an exceedingly rare disease with a poor prognosis. The etiology of PMME remains largely unknown and genetic characteristics are yet to be clarified, essential for identifying potential therapeutic targets and defining treatment guidelines. Here, we performed whole-exome sequencing on 47 formalin-fixed paraffin-embedded specimens from 18 patients with PMME, including 23 tumor samples, 6 metastatic lymph nodes, and 18 tumor-adjacent normal tissues. The genomic features of PMME were comprehensively characterized, and comparative genomic analysis was further performed between these specimens and 398 skin cutaneous melanomas (SKCM), 67 non-esophagus mucosal melanomas (NEMM), and 79 uveal melanomas (UVM). In the PMME cohort, recurrently mutated driver genes, such as MUC16, RANBP2, NRAS, TP53, PTPRT, NF1, MUC4, KMT2C, and BRAF, were identified. All RANBP2 mutations were putatively deleterious, and most affected samples had multipoint mutations. Furthermore, RANBP2 showed parallel evolution by multiregional analysis. Whole-genome doubling was an early truncal event that occurred before most driver mutations, except for in TP53. An ultraviolet radiation-related mutational signature, SBS38, was identified as specific to epithelial melanomas and could predict inferior survival outcomes in both PMME and SKCM patients. Comparing the mutational and copy number landscapes between PMME and other subtypes of melanoma revealed that PMME has a similar genomic pattern and biological characteristics to SKCM. In summary, we comprehensively defined the key genomic aberrations and mutational processes driving PMME and suggested for the first time that PMME may share similar genomic patterns with SKCM; therefore, patients with rare melanomas, such as PMME, may benefit from the current treatment used for common cutaneous melanoma.

CRISPR-mediated MECOM depletion retards tumor growth by reducing cancer stem cell properties in lung squamous cell carcinoma.

Targeted therapy for lung squamous cell carcinoma (LUSC) remains a challenge due to the lack of robust targets. Here, we identified MECOM as a candidate of therapeutic target for LUSC by screening 38 genes that were commonly amplified in three pairs of primary tumors and patient-derived xenografts (PDXs) using a clustered regularly interspaced short palindromic repeats (CRISPR)-mediated approach. High MECOM expression levels were associated with poor prognosis. Forced expression of MECOM in LUSC cell lines promoted cancer stem cell (CSC) properties, and its knockout inhibited CSC phenotypes. Furthermore, systemic delivery of CRISPR-mediated MECOM depletion cassette using adenovirus with an adaptor, which is composed of a single-chain fragment variable (scFv) against epithelial cell adhesion molecules (EpCAM) fused to the ectodomain of coxsackievirus and adenovirus receptor, and a protector, which consists of the scFv connected to the hexon symmetry of the adenovirus, could specifically target subcutaneous and orthotopic LUSC and retard tumor growth. This study could provide a novel therapeutic strategy for LUSC with high efficacy and specificity.

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study.

BACKGROUND: Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non-small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use. OBJECTIVE: This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms. METHODS: We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician's evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction. RESULTS: Experimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician's evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features. CONCLUSIONS: The LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician's evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models.

LINC00365 inhibited lung adenocarcinoma progression and glycolysis via sponging miR-429/KCTD12 axis.

This study researched the function of long non-coding RNA LINC00365 in lung adenocarcinoma (LAD) progression. LINC00365, miR-429, and KCTD12 expression in the LAD clinical tissues and cells were detcetd by qRT-PCR and Western blot. LINC00365, miR-429, and KCTD12 effects on H1975 cells malignant phenotype were detected by cell counting kit-8 assay, clone formation experiment, Transwell experiment, and glycolysis. Dual luciferase reporter gene assay and RNA pull-down assay were implemented. LINC00365 effect on H1975 cells in vivo growth was detected. LINC00365 was low expressed in the LAD patients and cells, associating with poor outcome. LINC00365 up-regulation attenuated H1975 cells proliferation, migration, invasion, glycolysis and in vivo growth. LINC00365 inhibited KCTD12 expression by sponging miR-429. miR-429 up-regulation and KCTD12 down-regulation partial reversed LINC00365 inhibition on H1975 cells malignant phenotype. Thus, LINC00365 inhibited LAD progression and glycolysis via targeting miR-429/KCTD12 axis. LINC00365 might be a potential candidate for LAD target treatment clinically.

circ_0000567/miR-421/TMEM100 Axis Promotes the Migration and Invasion of Lung Adenocarcinoma and Is Associated with Prognosis.

Purpose: Due to the high metastatic ability and poor prognosis of lung adenocarcinoma (LUAD), we identified novel non-coding RNAs, which constitute approximately 60% of human transcripts, as prognostic biomarkers and potential therapeutic targets for LUAD. Methods: In this study, we downloaded and analyzed microRNA (miRNA) datasets from The Cancer Genome Atlas (TCGA) to identify dysregulated miRNAs correlating with the overall survival (OS) of LUAD patients. miR-421, circ_0000567, and TMEM100 expression levels were examined by quantitative real-time polymerase chain reaction (qRT-PCR) in NSCLC tissues from 73 patients and adjacent normal tissues. Cell migration and invasion were assayed using wound healing and transwell assays. miR-421 target predictions were conducted using starBase, CircInteractome, circBank, TargetScan, miRanda, MirDB, miRpath, and Gene Expression Omnibus (GEO) databases. The circular structure and stability of circ_0000567 were verified by RNase R digestion and qRT-PCR using oligo(dT) and random primers. A luciferase reporter assay was used to evaluate the relationship between miR-421, circ_0000567, and TMEM100. Results: The miRNA panel associated with OS in patients with LUAD was screened according to the hazard ratio (HR) of miRNAs from high to low. Based on the correlation between these miRNAs and OS, as well as miRNA expression levels, miR-421 was selected for further outcome analysis. High miR-421 expression was an independent risk factor for shorter OS in 73 patients collected from our department. Bioinformatic analyses, luciferase reporter assays, and functional assays showed that circ_0000567 could act as a sponge for miR-421 and prevent it from directly targeting the 3'-untranslated region of TMEM100 mRNA and further degrading it in LUAD. miR-421 expression was significantly upregulated, while circ_0000567 and TMEM100 were downregulated in tumor tissues of LUAD, compared to their counterparts in normal tissues. Gain- and loss-of-function assays showed that miR-421 promoted LUAD cell migration and invasion. Overexpression of circ_0000567 inhibited migration and invasion, whereas co-transfection of circ_0000567 and miR-421 mimics partly counteracted this effect. TMEM100 was upregulated by enhanced circ_0000567 in LUAD cells, and the expression of TMEM100 was inversely proportional to miR-421, whereas it was directly proportional to circ_0000567 in 73 LUAD specimens, which confirmed the competitive endogenous RNA (ceRNA) network. Conclusion: Our findings suggest that miR-421 promotes the migration and invasion of lung adenocarcinoma via circ_0000567/miR-421/TMEM100 signaling and could be a prognostic biomarker for LUAD.