Survival In Lung Adenocarcinoma Research Articles

SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data.

Simple SummaryRobust methods for modelling and estimation of cancer survival could be relevant in understanding and limiting the impact of cancer. This study was aimed at developing an efficient Machine learning (ML) pipeline that could model survival in Lung Adenocarcinoma (LUAD) patients. Image transformations of multi omics data were employed for training a machine vision-based model capable of segregating patients into high-risk and low-risk subgroups. The performance was evaluated using concordance index, Brier score, and other similar metrices. The proposed model was able to outperform similar methods with a high degree of confidence. Furthermore, critical modules in cell cycle and pathways were also identified.The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient’s survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Interferon Regulatory Factor 4 Correlated With Immune Cells Infiltration Could Predict Prognosis for Patients With Lung Adenocarcinoma.

BackgroundImmune related interferon regulatory factor 4 (IRF4) is a member of the IRF family, whereas the clinical significance and possible role of IRF4 in lung adenocarcinoma (LUAD) remains unclear. We aimed to investigate the role of IRF4 in predicting the prognosis of LUAD patients.MethodsUsing The Cancer Genome Atlas (TCGA) database and our immunohistochemical (IHC) cohort, we analyzed the correlation between IRF4 expression and clinical characteristics, and the prognostic value of IRF4 was also evaluated in LUAD. The potential biological functions of IRF4 in LUAD were analyzed by Gene Set Enrichment Analysis (GSEA). The relationship between IRF4 and immune cell infiltration were evaluated by TISIDB database and our own IHC cohort. In addition, an immune checkpoint inhibitor (ICI) treated cohort from Gene Expression Omnibus database was used to determine the role of IRF4 in LUAD patients with immunotherapy.ResultsWe found that either mRNA or protein expression level of IRF4 was significantly higher in LUAD than in normal tissues (P < 0.001). The elevate in IRF4 expression in LUAD was significantly associated with the earlier clinical stage (P = 0.002). Patients with LUAD and IRF4 high expression correlated with significant longer overall survival in both TCGA database (P < 0.05) and our IHC-cohort (P = 0.001). Our results also demonstrated that IRF4 could serve as an independent favorable prognostic factor in patients with LUAD. GSEA analysis indicated that high IRF4 expression group enriched with several immune-related pathways, such as B cell receptor signaling pathway, T cell receptor signaling pathway and cytokine-cytokine receptor interaction signaling pathway. In LUAD, IRF4 positively correlated with several different immune infiltrations including various B cells, CD8+ T cells and CD4+ T cells both in mRNA and protein levels. Additionally, we found that the expression of IRF4 was positively associated with PD-1 and PD-L1 mRNA expression levels, and IRF4 high expression predicted moderate better survival in LUAD with immunotherapy (P = 0.071).ConclusionsOur results suggested that IRF4 was associated with higher B cells and T cells infiltration levels and might be a favorable prognostic biomarker in LUAD patients, whereas the potential prognostic role of IRF4 in ICI-treated patients needed further exploration.

Clinicopathological, Radiological, and Molecular Features of Primary Lung Adenocarcinoma with Morule-Like Components.

Background Morule-like component (MLC) was a rare structure in primary lung adenocarcinoma. We aimed to reveal the clinicopathological, radiological, immunohistochemical, and molecular features of lung adenocarcinoma with MLCs. Methods Twenty lung adenocarcinomas with MLCs were collected, and computed tomographic and histological documents were reviewed. Immunohistochemistry, targeted next-generation sequencing, and Sanger sequencing for β-catenin gene were performed. Results There were 9 lepidic adenocarcinomas, 8 acinar adenocarcinomas, 2 papillary adenocarcinomas, and 1 minimally invasive adenocarcinoma. Most patients (16/17) were shown a pure solid nodule, and 1 patient was shown a partly solid nodule on chest computed tomography (CT). Nine cases were accompanied with micropapillary components, and 3 were with cribriform components in which 2 suffered a worse prognosis. No significant association was found between the MCLs and the overall survival of lung adenocarcinoma (P = 0.109). The MLCs were often arranged in whorled or streaming patterns. The cells in MLCs showed syncytial and mild appearance. The MLCs were positive for E-cadherin, CK7, TTF-1, napsin-A, vimentin, and β-catenin (membrane), and negative for CK5/6, p40, p63, Synaptophysin, chromogranin A, and Cdx-2. EGFR mutation, ALK-EML4 fusion, HER2 amplification, and PIK3CA mutation were detected in 16 cases, 2 cases, 1 case, and 1 case, respectively. EGFR mutation was more frequent in adenocarcinomas with MLCs than those without MLCs (P = 0.040). β-catenin gene mutation was not detected in any patients. Conclusions MLC is often observed in the background of acinar, lepidic, and papillary adenocarcinomas. Lung adenocarcinomas with MLCs tend to appear as a solid mass on CT and harbor EGFR gene mutations. The micropapillary components and cribriform components may cause poor prognosis of lung adenocarcinomas with MLCs. Vimentin is always positive in MLCs, and it is a useful marker for the identification of MLCs.

N6-Methyladenosine RNA Methylation Regulator-Related Alternative Splicing (AS) Gene Signature Predicts Non-Small Cell Lung Cancer Prognosis.

Aberrant N6-methyladenosine (m6A) RNA methylation regulatory genes and related gene alternative splicing (AS) could be used to predict the prognosis of non–small cell lung carcinoma. This study focused on 13 m6A regulatory genes (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5) and expression profiles in TCGA-LUAD (n = 504) and TCGA-LUSC (n = 479) datasets from the Cancer Genome Atlas database. The data were downloaded and bioinformatically and statistically analyzed, including the gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. There were 43,948 mRNA splicing events in lung adenocarcinoma (LUAD) and 46,020 in lung squamous cell carcinoma (LUSC), and the data suggested that m6A regulators could regulate mRNA splicing. Differential HNRNPC and RBM15 expression was associated with overall survival (OS) of LUAD and HNRNPC and METTL3 expression with the OS of LUSC patients. Furthermore, the non–small cell lung cancer prognosis-related AS events signature was constructed and divided patients into high- vs. low-risk groups using seven and 14 AS genes in LUAD and LUSC, respectively. The LUAD risk signature was associated with gender and T, N, and TNM stages, but the LUSC risk signature was not associated with any clinical features. In addition, the risk signature and TNM stage were independent prognostic predictors in LUAD and the risk signature and T stage were independent prognostic predictors in LUSC after the multivariate Cox regression and receiver operating characteristic analyses. In conclusion, this study revealed the AS prognostic signature in the prediction of LUAD and LUSC prognosis.

PRMT5 Selective Inhibitor Enhances Therapeutic Efficacy of Cisplatin in Lung Cancer Cells.

As a therapeutic approach, epigenetic modifiers have the potential to enhance the efficacy of chemotherapeutic agents. Protein arginine methyltransferase 5 (PRMT5), highly expressed in lung adenocarcinoma, was identified to be involved in tumorigenesis. In the current study, we examined the potential antineoplastic activity of PRMT5 inhibitor, arginine methyltransferase inhibitor 1 (AMI-1), and cisplatin on lung adenocarcinoma. Bioinformatic analyses identified apoptosis, DNA damage, and cell cycle progression as the main PRMT5-associated functional pathways, and survival analysis linked the increased PRMT5 gene expression to worse overall survival in lung adenocarcinoma. Combined AMI-1 and cisplatin treatment significantly reduced cell viability and induced apoptosis. Cell cycle arrest in A549 and DMS 53 cells was evident after AMI-1, and was reinforced after combination treatment. Western blot analysis showed a reduction in demethylation histone 4, a PRMT5- downstream target, after treatment with AMI-1 alone or in combination with cisplatin. While the combination approach tackled lung cancer cell survival, it exhibited cytoprotective abilities on HBEpC (normal epithelial cells). The survival of normal bronchial epithelial cells was not affected by using AMI-1. This study highlights evidence of novel selective antitumor activity of AMI-1 in combination with cisplatin in lung adenocarcinoma cells.

Bioinformatic analysis of PLOD family member expression and prognostic value in non-small cell lung cancer.

BackgroundProcollagen-lysine, 2-oxoglutarate 5-dioxygenases (PLODs) are a group of enzymes that can mediate the hydroxylation of lysyl to hydroxylysine and participate in the formation of stabilized collagen. Evidence has demonstrated that PLODs are involved in the steps of tumor progression, including proliferation, invasion, and metastasis. However, limited information is available on the function of PLOD1/2/3 in lung cancer. In this study, we investigated the expression patterns and prognostic values of PLODs in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).MethodsThe Oncomine database and UALCAN were used to analyze the mRNA expression levels of PLOD family members in non-small cell lung cancer (NSCLC). The prognostic values of PLODs were investigated by the Kaplan–Meier Plotter database. We collected 33 patients with lung cancer to further verify the expression profiles and prognostic values of PLODs. The Kaplan-Meier method was used to perform survival curves, and the log-rank test was performed to evaluate the differences in survival. According to the GSE31210 databset, univariate and multivariate analyses were performed to identify whether PLODs were independent prognostic indicators for survival. Meanwhile, we investigated the mutations, potential biological functions and immune relevance of PLODs on the basis of the cBioPortal, Metascape and TIMER databases respectively.ResultsWe found that the mRNA and protein expression levels of PLODs in NSCLC tissues were higher than those in normal lung tissues. High PLOD1/2/3 expression had significant relevance to poor survival in LUAD but not in LUSC. In addition, the GSE31210 dataset showed that PLOD1 and PLOD3 were independent risk factors for relapse-free survival and overall survival (OS) in LUAD. We observed a high alteration rate of PLODs in LUSC patients, and the genetic alterations of PLODs had significant relevance to favorable OS. Furthermore, we observed that PLODs were significantly associated with tumor immunity in lung cancer. The enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway showed that the functions of the PLODs focused on cell cycle, DNA replication, and glycolysis/gluconeogenesis in LUAD.ConclusionsThese results indicated that PLODs were highly expressed in lung cancer and may be suitable prognostic markers.

Zc3h12d, a Novel of Hypomethylated and Immune-Related for Prognostic Marker of Lung Adenocarcinoma.

BackgroundZc3h12d is a negative regulator which plays a crucial role in immune modulation. However, the role of zc3h12d in lung adenocarcinoma (LUAD) remains unclear. We aim to explore the prognostic of zc3h12d and investigate the relationship between zc3h12d expression and immune infiltration in LUAD.MethodsTIMER site was used to analyze the expression of zc3h12d in LUAD. The zc3h12d protein levels in patient tissue samples were detected by immunohistochemistry staining assays. Meanwhile, based on UALCAN database and samples’ data from our cohort, we explored the relationship of clinicopathological features and zc3h12d expression to determine the clinical effect of zc3h12d in LUAD. Several databases including GEPIA, Kaplan–Meier plotter and our samples’ data were used to explore the prognostic value of zc3h12d in LUAD. Cox regression analysis was established to further evaluate the prognostic value of zc3h12d in LUAD. In addition, zc3h12d promoter methylation was analyzed by UALCAN database. Genetic alteration analysis was observed in the cBioPortal web. GO and KEGG analyses were conducted to elucidate the underlying mechanisms. Finally, the correlation between zc3h12d and tumor-infiltrating immune cells in LUAD was investigated by TIMER database. The B cells level was investigated by flow cytometry analysis of peripheral blood from our LUAD cohort.ResultsZc3h12d expression was significantly higher in LUAD, compared with adjacent normal tissues. The clinical data from the UALCAN database demonstrated that zc3h12d expression was closely related with cancer stage and nodal metastasis. However, patient sample detection revealed that zc3h12d expression was closely related to pathological N (p = 0.0431) and grade (p = 0.004). Moreover, low zc3h12d expression was associated with poorer overall survival in LUAD. We analyzed the methylation level of zc3h12d in LUAD and found that the methylation levels of zc3h12d promoter in LUAD were significantly reduced. In addition, zc3h12d genetic alterations, including deep deletion, could be found in LUAD. GO and KEGG pathway analysis results indicated that zc3h12d has a certain value in immune infiltration. We investigated the expression of zc3h12d in tumor-immune interactions. It was found that zc3h12d might be associated with the immune infiltration and markers of infiltrating immune cells of LUAD. The results of patient sample detection confirmed that B cells level was significantly lower in the patients with low zc3h12d expression than those in the patients with high zc3h12d expression.Conclusionzc3h12d might be considered as a potential biomarker for determining prognosis and immune-related therapeutic target in LUAD.

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

ObjectivesThe subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images. MethodsA dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms. ResultsThis study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set. ConclusionsThe automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.

Immune subtypes in LUAD identify novel tumor microenvironment profiles with prognostic and therapeutic implications.

e20512 Background: The incidence of lung adenocarcinoma (LUAD) is increasing year by year worldwide. Thorsson et al. identified six immune subtypes (ISs) (C1 - C6) in 33 diverse cancer types, including LUAD. The tumor microenvironment and lung cancer cells of 6 ISs have complex and different interplay, and the consequence of the interplay determines the growth of tumor cells and the prognosis of patients. Methods: Clinical and molecular information of TCGA LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) (N = 457). Kaplan-Meier survival analysis and Cox proportional-hazard analyses were performed in 4 independent GEO LUAD data sets (GSE31210, GSE37745, GSE50081, and GSE68465) and, where five immune subtypes C1-C4, C6 were stratified according to Thorsson et al.’s immune classification method. Results: Only 5 ISs were identified in the TCGA NSCLC cohort; the proportion of the C3 inflammatory IS (39.2%) was the most common IS observed in the TCGA LUAD cohort, whereas C1 was particularly dominant in LUSC (57.1%). The proportion of C3 was also the most common IS observed in GEO LUAD cohorts (GSE37745, GSE50081, GSE68465), except for one stage I-II lung adenocarcinomas cohort (GSE31210). The prognostic relevance of the C3 IS was associated with significantly longer overall survival and progression-free interval time of LUAD in the TCGA cohort. In contrast, the association was not significant in the TCGA LUSC cohort. C3 was also associated with significantly longer overall survival (p &lt; 0.001) and disease-free interval time (p &lt; 0.001) in the 4 GEO LUAD cohorts by multivariate analyses. To further evaluate prediction accuracies of C3, we compared the C3 model with the other 3 reported immune-related signatures. For all comparisons in these 4 GEO cohorts, C3 outperformed the other 3 signatures with superior overall survival and DFS predictive performances according to the time-dependent Concordance index. C3 presented higher levels of CD8 T, follicular helper T, M1 macrophage, CD4 memory resting, myeloid dendritic, neutrophils, and endothelial cells by using CIBERSORT and MCP counter. Low levels of M2 macrophages and cancer-associated fibroblast cells were also found in the C3 subtype. GSEA pathway analysis showed that the C3 subtype had a heightened activation in the antigen process and presenting pathway, interferon-gamma response pathway, T cell receptor signaling pathway, and neural killer cell-mediated cytotoxicity pathway than C1/C2. In contrast, the C1/C2 presented greater activation of pathways related to the cell cycles, MMR repair DNA repair, and p53 signaling pathways than the C3 subtype. Conclusions: The robust prognostic relevance of the C3 IS allows a more precise categorization of patients with relevant clinical and biological implications, which may be valuable tools to improve tailored therapeutic interventions in LUAD patients.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools.

BackgroundIndividual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.MethodsMultivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.ResultsThere were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.ConclusionThe current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Neoantigen driven B cell and CD4+ T follicular helper cell collaboration promotes robust anti-tumor CD8+ T cell responses

Abstract CD4+ TFH cells provide help to B cells, which is critical for germinal center (GC) formation. Although the presence of these cells is correlated with favorable clinical outcomes in various cancer types, little is known about how TFH and B cells function in anti-tumor immunity. Through the analyses of survival and bulk expression data in The Cancer Genome Atlas (TCGA) and single cell RNA sequencing data, we found that TFH cells correlated with GC B cells and with prolonged survival of lung adenocarcinoma (LUAD) patients. To further investigate if and how TFH-B cell interactions evoke protective anti-tumor responses, we developed the KP-HELLO murine lung adenocarcinoma model, in which tumor cells expressed B-cell- and T-cell-recognized neoantigens (HEL, LCMV GP33–43/FLAG/GP61–80, and codon-Optimized mScarlet). KP-HELLO tumors triggered tumor-specific TFH and GC B cell responses, which were necessary for tumor control, as were effector CD8+ T cell responses. The latter were reduced in the absence of T cell-B cell interactions or the IL-21 receptor. IL-21 was produced primarily by TFH cells, development of which required B cells. Moreover, development of tumor-specific TFH cell-responses was also reliant upon tumors that expressed B-cell-recognized neoantigens, which suggested that tumor-neoantigens themselves can control the fate decisions of tumor-specific CD4+ T cells by facilitating interactions with tumor-specific B cells. Our data highlight the importance of B cell-TFH cell collaborations in driving anti-tumor CD8+ T cell responses via IL-21 and indicated that therapeutics targeting B cell-TFH cell-IL-21 axis may be beneficial in patients with lung and other cancers.

Ubiquitin Conjugating Enzyme E2 H (UBE2H) Is Linked to Poor Outcomes and Metastasis in Lung Adenocarcinoma.

Simple SummaryThe development of novel treatments for metastatic lung adenocarcinoma is an important issue because some patients do not respond to current standard therapies. Our study aimed to investigate the gene expression profiles in non-tumor tissue, primary tumor tissue, and the metastatic lung tumor tissue in the pleura. After RNA sequencing and bioinformatic analysis from a patient with lung adenocarcinoma (LUAD), ubiquitin conjugating enzyme E2 H (UBE2H) was identified. Compared with normal tissue, a higher expression of UBE2H was observed in the tumor tissue. The high UBE2H expression was significantly associated with poor survival. Suppressing UBE2H in cell lines of lung adenocarcinoma inhibited metastatic capacity and reversed epithelial–mesenchymal transition signaling pathway. Five microRNAs, including miR-101, miR-30a, miR-30b, miR-328, and miR-497, predicted to target UBE2H might be potential prognostic biomarkers for survival in lung adenocarcinoma. The copy number variation may be involved in the regulation of the UBE2H expression. Our observations show that UBE2H is a novel regulatory molecule of metastasis, and may be a prognostic biomarker and a therapeutic target in lung adenocarcinoma.The prognosis of patients with metastatic lung adenocarcinoma (LUAD) is poor. Although novel lung cancer treatments have been developed for metastatic LUAD, not all patients are fit to receive these treatments. The present study aimed to identify the novel regulatory genes in metastatic LUAD. Because the pleural cavity is a frequent metastasis site of LUAD, the adjacent non-tumor tissue, primary tumor tissue, and metastatic lung tumor tissue in the pleura of a single patient with LUAD were collected. The gene expression profiles of the collected samples were further analyzed via RNA sequencing and bioinformatic analysis. A high expression level of ubiquitin conjugating enzyme E2 H (UBE2H), a hypoxia-mediated gene, was identified in the metastatic malignant pleural tumor. After accessing the survival data in patients with lung adenocarcinoma through online databases, a high UBE2H expression was associated with poor survival for LUAD. UBE2H knockdown in two lung adenocarcinoma cell lines suppressed the cell migration capacity and reversed the epithelial–mesenchymal transition (EMT) signaling pathway. A high expression of UBE2H-targeting microRNAs, including miR-101, miR-30a, miR-30b, miR-328, and miR-497, were associated with a favorable prognosis. Moreover, the UBE2H expression revealed a significant correlation with the copy number variation. Taken together, the presence of UBE2H regulated the EMT program and metastasis in LUAD.

A collagen glucosyltransferase drives lung adenocarcinoma progression in mice

Cancer cells are a major source of enzymes that modify collagen to create a stiff, fibrotic tumor stroma. High collagen lysyl hydroxylase 2 (LH2) expression promotes metastasis and is correlated with shorter survival in lung adenocarcinoma (LUAD) and other tumor types. LH2 hydroxylates lysine (Lys) residues on fibrillar collagen’s amino- and carboxy-terminal telopeptides to create stable collagen cross-links. Here, we show that electrostatic interactions between the LH domain active site and collagen determine the unique telopeptidyl lysyl hydroxylase (tLH) activity of LH2. However, CRISPR/Cas-9-mediated inactivation of tLH activity does not fully recapitulate the inhibitory effect of LH2 knock out on LUAD growth and metastasis in mice, suggesting that LH2 drives LUAD progression, in part, through a tLH-independent mechanism. Protein homology modeling and biochemical studies identify an LH2 isoform (LH2b) that has previously undetected collagen galactosylhydroxylysyl glucosyltransferase (GGT) activity determined by a loop that enhances UDP-glucose-binding in the GLT active site and is encoded by alternatively spliced exon 13 A. CRISPR/Cas-9-mediated deletion of exon 13 A sharply reduces the growth and metastasis of LH2b-expressing LUADs in mice. These findings identify a previously unrecognized collagen GGT activity that drives LUAD progression.

TYK2 is a prognostic biomarker and associated with immune infiltration in the lung adenocarcinoma microenvironment

Lung adenocarcinoma (LUAD) remains a major disease with high morbidity and mortality. The Janus kinases (JAKs) play a significant part in cellular biological process, inflammation and immunity. The role of JAK family in LUAD is still ambiguous. Various bioinformatics web portals were applied to explore the prognostic value of JAK family and their correlation with immune infiltration in LUAD. JAK1/2 was downregulated, whereas JAK3/TYK2 was upregulated in patients with LUAD compared with the healthy controls in subgroup analyses based on gender, age, smoking habits, cancer stage, TP53 mutation status, and nodal metastasis status. Drug sensitivity indicated that low expression of JAK3 and TYK2 were resistant to most of the small molecules or drugs. High TYK2 expression was associated with favorable overall survival and relapse free survival in LUAD. Moreover, univariate and multivariate analysis revealed that clinical stage, lymphatic node metastasis and TYK2 expression were the independent factors affecting the prognosis of LUAD patients. TYK2 expression in LUAD patients was positively associated with the abundance of immune cells (B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells) and immune biomarker sets. Moreover, TYK2 was mainly involved in RNA binding, transcriptional mis-regulation in cancer and cell cycles. We also identified several TYK2-associated miRNA or transcription factor targets in LUAD. Our results indicated that TYK2 was a biomarker and associated with prognosis and immune infiltration in LUAD, laying a foundation for further study about the role of TYK2 in the carcinogenesis and progression of LUAD.

Algorithm-Based Meta-Analysis Reveals the Mechanistic Interaction of the Tumor Suppressor LIMD1 With Non-Small-Cell Lung Carcinoma.

Non-small-cell lung carcinoma (NSCLC) is the major type of lung cancer, which is among the leading causes of cancer-related deaths worldwide. LIMD1 was previously identified as a tumor suppressor in lung cancer, but their detailed interaction in this setting remains unclear. In this study, we have carried out multiple genome-wide bioinformatic analyses for a comprehensive understanding of LIMD1 in NSCLC, using various online algorithm platforms that have been built for mega databases derived from both clinical and cell line samples. Our results indicate that LIMD1 expression level is significantly downregulated at both mRNA and protein levels in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), with a considerable contribution from its promoter methylation rather than its gene mutations. The Limd1 gene undergoes mutation only at a low rate in NSCLC (0.712%). We have further identified LIMD1-associated molecular signatures in NSCLC, including its natural antisense long non-coding RNA LIMD1-AS1 and a pool of membrane trafficking regulators. We have also identified a subgroup of tumor-infiltrating lymphocytes, especially neutrophils, whose tumor infiltration levels significantly correlate with LIMD1 level in both LUAD and LUSC. However, a significant correlation of LIMD1 with a subset of immune regulatory molecules, such as IL6R and TAP1, was only found in LUAD. Regarding the clinical outcomes, LIMD1 expression level only significantly correlates with the survival of LUAD (p<0.01) but not with that of LUSC (p>0.1) patients. These findings indicate that LIMD1 plays a survival role in LUAD patients at least by acting as an immune regulatory protein. To further understand the mechanisms underlying the tumor-suppressing function of LIMD1 in NSCLC, we show that LIMD1 downregulation remarkably correlates with the deregulation of multiple pathways that play decisive roles in the oncogenesis of NSCLC, especially those mediated by EGFR, KRAS, PIK3CA, Keap1, and p63, in both LUAD and LUSC, and those mediated by p53 and CDKN2A only in LUAD. This study has disclosed that LIMD1 can serve as a survival prognostic marker for LUAD patients and provides mechanistic insights into the interaction of LIMD1 with NSCLC, which provide valuable information for clinical applications.

Characterization of Tumor Microenvironment in Lung Adenocarcinoma Identifies Immune Signatures to Predict Clinical Outcomes and Therapeutic Responses

Background and ObjectiveIncreasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.MethodsIn this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.ResultsThree TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).ConclusionIn conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

Decreased HLF Expression Predicts Poor Survival in Lung Adenocarcinoma.

BackgroundLung adenocarcinoma (LUAD) is a type of non-small cell carcinoma. Its pathogenesis is being explored and there is no cure for the disease.Material/MethodsThe Gene Expression Omnibus (GEO) was searched to obtain data on expression of messenger RNA. GEO2R, an interactive web tool, was used to calculate the differentially expressed genes (DEGs) in LUAD. All the DEGs from different datasets were imported into VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to identify the intersection of the DEGs. An online analysis tool, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), was used to help understand the biological meaning of DEG enrichment in LUAD. Cytoscape 3.7.2 was used to perform centrality analysis and visualize hub genes and related networks. Furthermore, the prognostic value of the hub genes was evaluated with the Kaplan-Meier plotter survival analysis tool.ResultsThe GEO database was used to obtain RNA sequencing information for LUAD and normal tissue from the GSE118370, GSE136043, and GSE140797 datasets. A total of 376 DEGs were identified from GSE118370, 248 were identified from GSE136403, and 718 DEGs were identified from GSE140797. The 10 genes with the highest degrees of expression – the hub genes – were CAV1, TEK, SLIT2, RHOJ, DGSX, HLF, MEIS1, PTPRD, FOXF1, and ADRB2. In addition, Kaplan-Meier survival evaluation showed that CAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 were associated with favorable outcomes for LUAD.ConclusionsCAV1, TEK, SLIT2, HLF, MEIS1, PTPRD, FOXF1, and ADRB2 are hub genes in the DEG interaction network for LUAD and are involved in the development of and prognosis for the disease. The mechanisms underlying these genes should be the subject of further studies.

Pentraxin 3 acts as a functional effector of Akt/NF-κB signaling to modulate the progression and cisplatin-resistance in non-small cell lung cancer

Pentraxin 3 (PTX3) has been documented to be involved in the development of chemoresistance, however, the mechanisms by which it regulates cisplatin (DDP) resistance in non-small cell lung cancer (NSCLC) have never been elucidated. Quantitative reverse transcriptase polymerase chain reaction and Western blot were carried to determine the expression of PTX3, ATP-binding cassette sub-family B member 1 (ABCB1)/P-glycoprotein 1 (p-gp), protein kinase B (Akt), phosphorylated Akt and nuclear factor-kappa B (NF-кB) p65. The biological roles of PTX3 in NSCLC progression and NSCLC cell resistance to DDP were evaluated using enzyme-linked immunosorbent assay, cell count kit-8, colony formation assay, flow cytometry, as well as xenograft tumor assay. The expression of PTX3 was increased in the serum of NSCLC patients as well as in NSCLC cell lines. Lower PTX3 level was associated with longer overall survival in lung adenocarcinoma and lung squamous cell carcinoma patients. Furthermore, PTX3 expression was greatly higher in DDP-resistant NSCLC cells than that in NSCLC cells. Silencing of PTX3 restrained the proliferation and promoted the apoptosis of NSCLC cells, as well as sensitized DDP-resistant NSCLC cells to DDP. Additionally, knockdown of PTX3 inhibited the growth of NSCLC tumors in vivo. Upregulation of PTX3 expression was dependent on the activation of Akt/NF-κB signaling. The induction of apoptosis by PTX3 knockdown was enhanced by MK-2206 or JSH-23. In conclusion, knockdown of PTX3 restrained the progression of NSCLC and sensitized NSCLC cells towards DDP, which provides a potential target to restore DDP chemoresponse.

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma.

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.