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

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

Expression Pattern and Prognostic Analysis of Branched-Chain Amino Acid Catabolism-Related Genes in Non-Small Cell Lung Cancer.

BACKGROUND: The purpose of our study is to analyze the expression pattern and prognostic value of catabolism-related enzymes of branched-chain amino acids (BCAAs) in non-small cell lung cancer (NSCLC). METHODS: Differential expression analysis, mutation, copy number variation (CNV), methylation analysis, and survival analysis of BCAAs catabolism-related enzymes in NSCLC were performed using the Cancer Genome Atlas (TCGA) database. RESULTS: Six and seven differentially expressed genes were obtained in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. IL4I1 was located at the core regulatory nodes in the gene co-expression networks of both LUAD and LUSC. The AOX1 mutation rate was the highest in both LUAD and LUSC. For CNV, IL4I1 was up-regulated in both LUAD and LUSC with an increase in copy number, whereas AOX1 and ALDH2 were differentially regulated in the two subtypes of lung cancer. In patients with NSCLC, high expression of IL4I1 was associated with lower overall survival (OS), and low expression of ALDH2 predicted shorter disease-free survival (DFS). ALDH2 expression was related with LUSC survival. CONCLUSIONS: This study explored the biomarkers of BCAAs catabolism related to the prognosis of NSCLC, which provided a theoretical foundation to guide the clinical diagnosis and treatment of NSCLC.

Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation.

Background: The role of the tumor microenvironment (TME) in predicting prognosis and therapeutic efficacy has been demonstrated. Nonetheless, no systematic studies have focused on TME patterns or their function in the effectiveness of immunotherapy in triple-negative breast cancer. Methods: We comprehensively estimated the TME infiltration patterns of 491 TNBC patients from four independent cohorts, and three cohorts that received immunotherapy were used for validation. The TME subtypes were comprehensively evaluated based on immune cell infiltration levels in TNBC, and the TRG score was identified and systematically correlated with representative tumor characteristics. We sequenced 80 TNBC samples as an external validation cohort to make our conclusions more convincing. Results: Two TME subtypes were identified and were highly correlated with immune cell infiltration levels and immune-related pathways. More representative TME-related gene (TRG) scores calculated by machine learning could reflect the fundamental characteristics of TME subtypes and predict the efficacy of immunotherapy and the prognosis of TNBC patients. A low TRG score, characterized by activation of immunity and ferroptosis, indicated an activated TME phenotype and better prognosis. A low TRG score showed a better response to immunotherapy in TNBC by TIDE (Tumor Immune Dysfunction and Exclusion) analysis and sensitivity to multiple drugs in GDSC (Genomics of Drug Sensitivity in Cancer) analysis and a significant therapeutic advantage in patients in the three immunotherapy cohorts. Conclusion: TME subtypes played an essential role in assessing the diversity and complexity of the TME in TNBC. The TRG score could be used to evaluate the TME of an individual tumor to enhance our understanding of the TME and guide more effective immunotherapy strategies.

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

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

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

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

Tumor-Infiltrating PD-1hiCD8+-T-Cell Signature as an Effective Biomarker for Immune Checkpoint Inhibitor Therapy Response Across Multiple Cancers.

BACKGROUND: Stratification of patients who could benefit from immune checkpoint inhibitor (ICI) therapy is of much importance. PD-1hiCD8+ T cells represent a newly identified and effective biomarker for ICI therapy response biomarker in lung cancer. Accurately quantifying these T cells using commonly available RNA sequencing (RNA-seq) data may extend their applications to more cancer types. METHOD: We built a transcriptome signature of PD-1hiCD8+ T cells from bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data of tumor-infiltrating immune cells. The signature was validated by flow cytometry and in independent datasets. The clinical applications of the signature were explored in non-small-cell lung cancer, melanoma, gastric cancer, urothelial cancer, and a mouse model of breast cancer samples treated with ICI, and systematically evaluated across 21 cancer types in The Cancer Genome Atlas (TCGA). Its associations with other biomarkers were also determined. RESULTS: Signature scores could be used to identify the PD-1hiCD8+ T subset and were correlated with the fraction of PD-1hiCD8+ T cells in tumor tissue (Pearson correlation, R=0.76, p=0.0004). Furthermore, in the scRNA-seq dataset, we confirmed the capability of PD-1hiCD8+ T cells to secrete CXCL13, as well as their interactions with other immune cells. In 581 clinical samples and 204 mouse models treated with ICIs, high signature scores were associated with increased survival, and the signature achieved area under the receiver operating characteristic curve scores of 0.755 (ranging from 0.61 to 0.91) in predicting therapy response. In TCGA pan-cancer datasets, our signature scores were consistently correlated with therapy response (R=0.78, p<0.0001) and partially explained the diverse response rates among different cancer types. Finally, our signature generally outperformed other mRNA-based predictors and showed improved predictive performance when used in combination with tumor mutational burden (TMB). The signature score is available in the R package "PD1highCD8Tscore" (https://github.com/Liulab/PD1highCD8Tscore). CONCLUSION: Through estimating the fraction of the PD-1hiCD8+ T cell, our signature could predict response to ICI therapy across multiple cancers and could serve as a complementary biomarker to TMB.

A Multifeature Learning and Fusion Network for Facial Age Estimation.

Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation.

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

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

LncAS2Cancer: a comprehensive database for alternative splicing of lncRNAs across human cancers.

Accumulating studies demonstrated that the roles of lncRNAs for tumorigenesis were isoform-dependent and their aberrant splicing patterns in cancers contributed to function specificity. However, there is no existing database focusing on cancer-related alternative splicing of lncRNAs. Here, we developed a comprehensive database called LncAS2Cancer, which collected 5335 bulk RNA sequencing and 1826 single-cell RNA sequencing samples, covering over 30 cancer types. By applying six state-of-the-art splicing algorithms, 50 859 alternative splicing events for 8 splicing types were identified and deposited in the database. In addition, the database contained the following information: (i) splicing patterns of lncRNAs under seven different conditions, such as gene interference, which facilitated to infer potential regulators; (ii) annotation information derived from eight sources and manual curation, to understand the functional impact of affected sequences; (iii) survival analysis to explore potential biomarkers; as well as (iv) a suite of tools to browse, search, visualize and download interesting information. LncAS2Cancer could not only confirm the known cancer-associated lncRNA isoforms but also indicate novel ones. Using the data deposited in LncAS2Cancer, we compared gene model and transcript overlap between lncRNAs and protein-coding genes and discusses how these factors, along with sequencing depth, affected the interpretation of splicing signals. Based on recurrent signals and potential confounders, we proposed a reliable score to prioritize splicing events for further elucidation. Together, with the broad collection of lncRNA splicing patterns and annotation, LncAS2Cancer will provide important new insights into the diverse functional roles of lncRNA isoforms in human cancers. LncAS2Cancer is freely available at https://lncrna2as.cd120.com/.

Clinical significance of tumour mutation burden in immunotherapy across multiple cancer types: an individual meta-analysis.

BACKGROUND: Biomarkers for stratifying patients that could benefit from immune checkpoint inhibitors are necessary. Tumour mutation burden has recently become a promising biomarker in cancer, but the associations between tumour mutation burden and outcomes of immune checkpoint inhibitors treatment were not well-documented in present studies. METHODS: We searched PubMed, Web of Science and EMBASE databases up to 1 October 2019. Studies evaluated the association between tumour mutation burden and clinical outcomes were included. Hazard ratios and odds ratios were applied to estimate the association of tumour mutation burden score with overall survival, progression-free survival and response rate, respectively. The best cut-off value was chosen by best discriminated overall survival using Contal and O'Quigley method. RESULTS: Twenty-two studies involving 6171 patients in diverse cancers were included. The individual participant data meta-analysis demonstrated that high tumour mutation burden was associated with better overall survival (HR = 0.57, 95% CI = 0.50-0.64) and progression-free survival (HR = 0.50, 95% CI = 0.40-0.63) and higher response rate. The best cut-off values in each cancer type were 17.7/MB in non-small cell lung cancer, 7.9/MB in bladder cancer, 6.1/MB in melanoma, 12.3/MB in colorectal cancer, 6.9/MB in esophagogastric cancer, 10.5/MB in head and neck cancer. The pooled meta-analysis showed the prognosis value was robust and the sensitivity, specificity and area under the receiver operating characteristic curves in predicting response rates were 0.63, 0.71 and 0.73, respectively. CONCLUSIONS: The present meta-analysis indicates tumour mutation burden is a promising predictor of immune checkpoint inhibitors therapy but the cut-off value differs in different cancers.

Analysis of Colorectal Cancer-Associated Alternative Splicing Based on Transcriptome.

Colorectal cancer (CRC) is a severe risk to public health, and there is growing evidence that alternative splicing (AS) plays a crucial role in cancer. However, the AS biomarkers in CRC are seldom reported. In this study, we perform transcriptome analysis of colorectal samples for cancer-specific AS and transcriptomic alterations. We identify 1577 splice events in 885 genes, enriched in CRC-associated pathways and functions. In parallel, we find 10 splicing factors (SFs) with transcriptome variation or significant differential expression. Based on co-expression and binding motif, we construct an SF-AS regulatory network, revealing the association between cancer-specific AS and aberrant SF. Integrating The Cancer Genome Atlas and published sources, we observed that some recurrent AS is an indicator of poor prognosis. Through further experimental verification, we found that the AS of six genes showed significant differences between the tumor sample and the normal sample, and AS of TCF7, COL12A1, GK, and UBA1 can be used as new potential biomarkers in CRC. Our study provides an important analysis of CRC-associated AS, which could act as a starting point for future functional explorations, the development of biomarkers and AS-based target therapy.

Correction: Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0104282.].

Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability.

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

Decreased expression of EFCC1 and its prognostic value in lung adenocarcinoma.

BACKGROUND: So far, there is a lack of reliable prognostic biomarkers for lung adenocarcinoma (ADC). Initially, we found that EF-hand and coiled-coil domain containing 1 (EFCC1) was a novel gene which was downregulated consistently with the progression of lung ADC in The Cancer Genome Atlas (TCGA) data through bioinformatics analysis. In this study, we aimed to evaluate the prognostic significance of EFCC1 in lung ADC in both TCGA data and clinical samples. METHODS: Firstly, the expression level and prognostic significance of EFCC1 in lung ADC were investigated in TCGA data. Then, the expression level of EFCC1 was validated by qPCR, Western blot, and immunohistochemistry (IHC) in five clinical lung ADC and matched adjacent non-tumor tissues. Finally, the association of EFCC1 expression with clinicopathological characteristics and overall survival (OS) in lung ADC patients was further evaluated in 130 clinical lung ADC samples with tissue microarray (TMA). RESULTS: In TCGA data, we found that decreased mRNA expression (P<0.001), elevated DNA methylation (P<0.001) of EFCC1 in lung ADC samples compared with normal lung samples, and low EFCC1 mRNA expression was associated with poor OS in lung ADC patients (HR =0.856, 95% CI: 0.754-0.970, P=0.015). In five clinical lung ADC and matched adjacent non-tumor tissues, both mRNA and protein levels of EFCC1 were lower in all lung ADC tissues than in their adjacent non-tumor counterparts. In 130 clinical lung ADC samples with TMA, EFCC1 expression was correlated with tumor-node-metastasis (TNM) stages (P=0.040) and lymph node metastasis status (P=0.001). The Kaplan-Meier survival curve revealed that low EFCC1 expression was significantly associated with poor OS in lung ADC patients (P=0.001) and multivariate Cox regression hazard model demonstrated that EFCC1 expression level was an independent prognostic factor for lung ADC patients (HR =0.557, 95% CI: 0.351-0.883, P=0.013). CONCLUSIONS: Our findings suggested that decreased expression of EFCC1 was significantly associated with progression of lung ADC and could serve as a novel prognostic biomarker for lung ADC patients.

Systematic identification of lincRNA-based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma.

Copy number alterations (CNAs) of lincRNAs act as one of important mechanisms in disrupting lincRNA expression which may play critical roles during tumorigenesis in lung adenocarcinoma (LUAD). The copy number alterations of lincRNAs can mark the spectrum of cancer progression and may serve as biomarkers for prognosis in LUAD, however it is rarely studied. We analyzed RNASeq data for 488 LUAD patients from TCGA portal and 58 healthy subjects to identify prognostic lincRNAs predictive of patient survival. Computational analysis entailing integration of expression and copy number alteration data revealed five prognostic lincRNAs: RBPMS-AS1, TDRKH-AS1, LINC00578, RP11-470 M17.2 and LINC00941. The copy number alterations in the LINC00578 and RP11-470 M17.2 genes were positively associated with the longer overall survival of LUAD patients. The CNA in LINC00941 was negatively associated with the longer overall survival. Copy number amplification significantly correlated with increased expression of TDRKH-AS1, which regulates telomere organization and EZH2-mediated epigenetic silencing of CDKN1A, CDKN1B and IL24. Decreased survival of LUAD patients was associated with high LINC00941 expression. The LINC00941 regulates the PI3K-AKT signaling pathway, focal adhesion by influencing potential targets, such as KRAS proto-oncogene GTPase and VEGFC. These lincRNA-based prognostic biomarkers may destroy important cancer-related biological processes contributing to LUAD prognosis. In summary, we demonstrate the prognostic potential of four differentially expressed lincRNAs with copy number alterations (RBPMS-AS1, TDRKH-AS1, LINC00578 and RP11-470 M17.2) that are positively associated with longer overall survival of LUAD patients. One differentially expressed lincRNA LINC00941 with copy number alterations was negatively associated with longer overall survival of LUAD patients.

Long non-coding RNA AFAP1-AS1 plays an oncogenic role in promoting cell migration in non-small cell lung cancer.

Long non-coding RNA (lncRNA) plays an important role in tumor progression and metastasis. Emerging evidence indicates that lncRNA actin filament-associated protein 1-antisense RNA 1 (AFAP1-AS1) is dysregulated in certain tumors. However, the function of AFAP1-AS1 in non-small cell lung cancer (NSCLC) remains elusive. In this study, we conducted global lncRNA profiling and identified that AFAP1-AS1 is significantly upregulated in NSCLC, suggesting that AFAP1-AS1 may be important for lung cancer development. For the first time, the transcription initiation and termination sites of AFAP1-AS1 were identified by rapid amplification of cDNA ends technology, and the sequencing data indicated that AFAP1-AS1 in lung cancer cells is a novel transcript variant. Through gain- and loss-of-function studies, AFAP1-AS1 was demonstrated to promote cell migration and invasion. Mechanistically, AFAP1-AS1 functions through positively regulating the expression of AFAP1 protein. On the other hand, the expression of lncRNA AFAP1-AS1 negatively correlates with CpG methylation status of its gene promoter, identified in both lung cancer cells and patient tissues, and treatment with DNA methyltransferase inhibitor decitabine significantly activates AFAP1-AS1 expression, strongly supporting that AFAP1-AS1 expression is tightly regulated by DNA methylation. Taken together, this study demonstrates that AFAP1-AS1 acts as an oncogene in NSCLC to promote cell migration partly by upregulating AFAP1 expression, while its own expression is controlled by DNA methylation, and highlights its diagnostic and therapeutic values for NSCLC patients.

A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs.

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non-coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well-known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan-cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in 'Tissue Invasion and Metastasis' in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA-based target therapy.

[Preparation of MIL-53(Fe)@polydopamine@Fe3O4 magnetic composite for magnetic solid-phase extraction of sulfonylurea herbicides in environmental water].

Polydopamine (PDA) and MIL-53(Fe) modified Fe3O4 particles (MIL-53(Fe)@PDA@Fe3O4 magnetic composite) were prepared by simple one-pot solvothermal method. The obtained composite was introduced to rapidly extract four kinds of sulfonylurea herbicides (SUHs) from environmental water samples by magnetic solid-phase extraction (MSPE). Then the herbicides were analyzed by a high performance liquid chromatographic system equipped with a photodiode array detector. The mobile phase was a mixture of acetonitrile and water containing 0.01% (v/v) trifluoroacetic acid, and the detection wavelength was 233 nm. Significant extraction parameters were optimized to improve the extraction efficiency. Under the optimum conditions (5 mL desorption solvent of acetone, 4.5 min extraction time, 60 mg adsorbent dosage, 0.5 g NaCl and the pH 3 of the solution), the developed method showed good linearities with correlation coefficients (r) no less than 0.9980. The limits of detection (LODs, S/N=3) of the four SUHs were 0.28-0.77 µg/L. The method was successfully used to determine four SUHs in three kinds of environmental water samples with satisfactory recoveries ranging from 78.8% to 109.7%. Therefore, the MIL-53(Fe)@PDA@Fe3O4 magnetic composite is efficient and has good potential for the extraction of SUHs.

Modification of polydopamine-coated Fe3O4 nanoparticles with multi-walled carbon nanotubes for magnetic-µ-dispersive solid-phase extraction of antiepileptic drugs in biological matrices.

In this study, multi-walled carbon nanotubes were coated on the surface of magnetic nanoparticles modified by polydopamine. The synthesized composite was characterized and applied to magnetic-µ-dispersive solid-phase extraction of oxcarbazepine (OXC), phenytoin (PHT), and carbamazepine (CBZ) from human plasma, urine, and cerebrospinal fluid samples prior to analysis by a high-performance liquid chromatography-photodiode array detector. The extraction parameters were investigated and the optimum condition was obtained when the variables were set to the following: sorbent type, Fe3O4@polyDA-MWCNTs (length < 2 µm); sample pH, 6; amount of sorbent, 15 mg; sorption time, 1.5 min at room temperature; type and volume of the eluent, 2.5 mL methanol; and salt content, none added. Under the optimized conditions, the calibration curves are linear in the concentration range 2-2000 ng/mL, the limits of detection are in the range 0.4-3.1 ng/mL, and the relative standard deviations and relative recoveries of plasma (spiked at 200 ng/mL) and CSF (spiked at 50 ng/mL) are in the ranges 1.4-8.2% and 92.8-96.5%, respectively. The applicability of the method was successfully confirmed by extraction and determination of OXC, PHT, and CBZ in biological matrices. Graphical abstract Magnetic multi-walled carbon nanotube core-shell composites were applied as magnetic-µ-dispersive solid-phase extraction adsorbents for determination of antiepileptic drugs in biological matrices.

Targeting Pin1 by inhibitor API-1 regulates microRNA biogenesis and suppresses hepatocellular carcinoma development.

Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide, but there are few effective treatments. Aberrant microRNA (miRNA) biogenesis is correlated with HCC development. We previously demonstrated that peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) participates in miRNA biogenesis and is a potential HCC treatment target. However, how Pin1 modulates miRNA biogenesis remains obscure. Here, we present in vivo evidence that Pin1 overexpression is directly linked to the development of HCC. Administration with the Pin1 inhibitor (API-1), a specific small molecule targeting Pin1 peptidyl-prolyl isomerase domain and inhibiting Pin1 cis-trans isomerizing activity, suppresses in vitro cell proliferation and migration of HCC cells. But API-1-induced Pin1 inhibition is insensitive to HCC cells with low Pin1 expression and/or low exportin-5 (XPO5) phosphorylation. Mechanistically, Pin1 recognizes and isomerizes the phosphorylated serine-proline motif of phosphorylated XPO5 and passivates phosphorylated XPO5. Pin1 inhibition by API-1 maintains the active conformation of phosphorylated XPO5 and restores XPO5-driven precursor miRNA nuclear-to-cytoplasm export, activating anticancer miRNA biogenesis and leading to both in vitro HCC suppression and HCC suppression in xenograft mice. CONCLUSION: Experimental evidence suggests that Pin1 inhibition by API-1 up-regulates miRNA biogenesis by retaining active XPO5 conformation and suppresses HCC development, revealing the mechanism of Pin1-mediated miRNA biogenesis and unequivocally supporting API-1 as a drug candidate for HCC therapy, especially for Pin1-overexpressing, extracellular signal-regulated kinase-activated HCC. (Hepatology 2018).