Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression

Proteomic profiling of tumor microenvironment and prognosis risk prediction in stage I lung adenocarcinoma

OBJECTIVES

With the increasing popularity of CT screening, more cases of early-stage lung cancer are being diagnosed. However, 24.5% of stage I non-small-cell lung cancer (NSCLC) patients still experience treatment failure post-surgery. Biomarkers to predict lung cancer patients at high risk of recurrence are needed.

MATERIALS AND METHODS

We collected protein mass spectrometry data from the Taiwan Lung Cancer Moonshot Project and performed bioinformatics analysis on proteins with differential expressions between tumor and adjacent normal tissues in 74 stage I lung adenocarcinoma (LUAD) cases, aiming to explore the tumor microenvironment related prognostic biomarkers. Findings were further validated in 6 external cohorts.

RESULTS

The analysis of differentially expressed proteins revealed that the most enriched categories of diseases and biological functions were cellular movement, immune cell trafficking, and cancer. Utilizing proteomic profiling of the tumor microenvironment, we identified five prognostic biomarkers (ADAM10, MIF, TEK, THBS2, MAOA). We then developed a risk score model, which independently predicted recurrence-free survival and overall survival in stage I LUAD. Patients with high risk scores experienced worse recurrence-free survival (adjusted hazard ratio = 8.28, p < 0.001) and overall survival (adjusted hazard ratio = 6.88, p = 0.013). Findings had been also validated in the external cohorts.

CONCLUSION

The risk score model derived from proteomic profiling of tumor microenvironment can be used to predict recurrence risk and prognosis of stage I LUAD.

PKIB, a Novel Target for Cancer Therapy

The serine-threonine kinase protein kinase A (PKA) is a cyclic AMP (cAMP)-dependent intracellular protein with multiple roles in cellular biology including metabolic and transcription regulation functions. The cAMP-dependent protein kinase inhibitor β (PKIB) is one of three known endogenous protein kinase inhibitors of PKA. The role of PKIB is not yet fully understood. Hormonal signaling is correlated with increased PKIB expression through genetic regulation, and increasing PKIB expression is associated with decreased cancer patient prognosis. Additionally, PKIB impacts cancer cell behavior through two mechanisms; the first is the nuclear modulation of transcriptional activation and the second is the regulation of oncogenic AKT signaling. The limited research into PKIB indicates the oncogenic potential of PKIB in various cancers. However, some studies suggest a role of PKIB in non-cancerous disease states. This review aims to summarize the current literature and background of PKIB regarding cancer and related issues. In particular, we will focus on cancer development and therapeutic possibilities, which are of paramount interest in PKIB oncology research.

Clonal gene signatures predict prognosis in mesothelioma and lung adenocarcinoma

Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.

A novel transcriptomic signature associated with lymphovascular invasion predicts clinical outcomes, tumor microenvironment, and therapeutic response in lung adenocarcinoma

PURPOSE

Since TNM staging has limitations for predicting post-operative outcomes and relapse, more effective prediction tools need to be researched and developed. Lymphovascular invasion, LVI, as a histopathological feature, has been widely shown to have a correlation with poor prognosis and early recurrence of lung adenocarcinoma (LUAD). However, LVI assessment is limited by subjective bias, and therefore its efficacy in practical clinical application needs further clarification. The aim of this study was to formulate a new signature based on LVI-related genes to predict prognosis and recurrence in patients with lung adenocarcinoma.

METHODS

Clinicopathological information, gene sequencing data and whole slide images (WSIs) of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA) databases. LVI statue were evaluated by professional pathologists, and then the differentially expressed genes (LVI DEGs) associated with LVI were screened. The least absolute shrinkage and selection operator (LASSO) and Step Cox regression models were used to construct LVI-associated risk scores (LVRS), including PAQR4, ARGHEF6, CKS1B, CFTR and SEC14L4. The validity of the LVRS score was evaluated on multiple external datasets and our JSSZL cohort dataset. Using LVRS scores and clinical information, nomogram were constructed for use by clinicians. In addition, we further explored the relationship between LVRS score and clinicopathological features, immune infiltration, tumor mutational load, and immunotherapy response, and confirmed the expression of key genes in LVRS score in lung adenocarcinoma tissues using qRT-PCR and immunohistochemistry (IHC) techniques.

RESULTS

Based on the LVRS, patients could be classified into high-LVRS and low-LVRS groups. It was found that OS and PFS were significantly worse in the high-LVRS group than in the low-LVRS group (p < 0.001). By ROC curve analysis, it could be found that the nomogram combining LVRS and clinical information could accurately predict the prognosis of LUAD patients with the area under the curve of 1,3,5-year survival rate could reach 0.754, 0.741 and 0.735. The results of univariate and multivariate analysis showed that LVRS was an independent prognostic factor. At the same time, there were significant differences in the mutation profiles and immune microenvironment between the high-LVRS and low-LVRS groups, with the high-LVRS group having a significantly higher mutation rate than the low-LVRS group and exhibiting immunological "cold" features. By the experimental results, higher expression levels of PAQR4 and CKS1B were found in LUAD tissues, while lower expression levels of ARGHEF6, CFTR and SEC14L4 were observed.

CONCLUSIONS

The LVRS established in this study serves as a valid tool to predict the prognosis and recurrence status of lung adenocarcinoma patients and has a predictive effect on the response to postoperative treatment. The establishment of LVRS may offer some theoretical support to clinical treatment strategies for patients with lung adenocarcinoma following surgical intervention.

A Novel Nucleic Acid Sensing-related Genes Signature for Predicting Immunotherapy Efficacy and Prognosis of Lung Adenocarcinoma

BACKGROUND

As a novel pillar for lung adenocarcinoma (LUAD) treatment, immunotherapy has limited efficiency in LUAD patients. The nucleic acid sensing (NAS) pathways are critical in the anti-tumor immune response, but their role in LUAD remains controversial.

OBJECTIVE

The study aims to develop a classification system to identify immune subtypes of LUAD based on nucleic acid sensing-related genes so that it can help screen patients who may respond to immunotherapy.

METHODS

We performed a comprehensive bioinformatics analysis of the NAS molecule expression profiles across multiple public datasets. Using qRT-PCR to verify the NAS genes in multiple lung cancer cell lines. Molecular docking was performed to screen drug candidates.

RESULTS

The NAS-activated subgroup and NAS-suppressed subgroup were validated based on the different patterns of gene expression and pathways enrichment. The NAS-activated subgroup displayed a stronger immune infiltration and better prognosis of patients. Moreover, we constructed a seven nucleic acid sensing-related risk score (NASRS) model for the convenience of clinical application. The predictive values of NASRS in prognosis and immunotherapy were subsequently fully validated in the lung adenocarcinoma dataset and the uroepithelial carcinoma dataset. Additionally, five potential drugs binding to the core target of the NAS signature were predicted through molecular docking.

CONCLUSION

We found a significant correlation between nucleic acid sensing function and the immune treatment efficiency in LUAD. The NASRS can be used as a robust biomarker for the predicting of prognosis and immunotherapy efficiency and may help in clinical decisions for LUAD patients.

Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes

BACKGROUND

Lung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes.

METHODS

The genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model.

RESULTS

Based on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients' prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD.

CONCLUSION

In our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

RATIONALE AND OBJECTIVES

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading.

MATERIALS AND METHODS

This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness.

RESULTS

Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

Epigenetic regulation of SMAD3 by histone methyltransferase SMYD2 promotes lung cancer metastasis

Epigenetic alterations, especially histone methylation, are key factors in cell migration and invasion in cancer metastasis. However, in lung cancer metastasis, the mechanism by which histone methylation regulates metastasis has not been fully elucidated. Here, we found that the histone methyltransferase SMYD2 is overexpressed in lung cancer and that knockdown of SMYD2 could reduce the rates of cell migration and invasion in lung cancer cell lines via direct downregulation of SMAD3 via SMYD2-mediated epigenetic regulation. Furthermore, using an in vitro epithelial-mesenchymal transition (EMT) system with a Transwell system, we generated highly invasive H1299 (In-H1299) cell lines and observed the suppression of metastatic features by SMYD2 knockdown. Finally, two types of in vivo studies revealed that the formation of metastatic tumors by shSMYD2 was significantly suppressed. Thus, we suggest that SMYD2 is a potential metastasis regulator and that the development of SMYD2-specific inhibitors may help to increase the efficacy of lung cancer treatment.

An intrinsic purine metabolite AICAR blocks lung tumour growth by targeting oncoprotein mucin 1

BACKGROUND

Lung cancer cells overexpress mucin 1 (MUC1) and active subunit MUC1-CT. Although a peptide blocks MUC1 signalling, metabolites targeting MUC1 are not well studied. AICAR is a purine biosynthesis intermediate.

METHODS

Cell viability and apoptosis were measured in AICAR-treated EGFR-mutant and wild-type lung cells. AICAR-binding proteins were evaluated by in silico and thermal stability assays. Protein-protein interactions were visualised by dual-immunofluorescence staining and proximity ligation assay. AICAR-induced whole transcriptomic profile was determined by RNA sequencing. EGFR-TL transgenic mice-derived lung tissues were analysed for MUC1 expression. Organoids and tumours from patients and transgenic mice were treated with AICAR alone or in combination with JAK and EGFR inhibitors to evaluate treatment effects.

RESULTS

AICAR reduced EGFR-mutant tumour cell growth by inducing DNA damage and apoptosis. MUC1 was one of the leading AICAR-binding and degrading proteins. AICAR negatively regulated JAK signalling and JAK1-MUC1-CT interaction. Activated EGFR upregulated MUC1-CT expression in EGFR-TL-induced lung tumour tissues. AICAR reduced EGFR-mutant cell line-derived tumour formation in vivo. Co-treating patient and transgenic mouse lung-tissue-derived tumour organoids with AICAR and JAK1 and EGFR inhibitors reduced their growth.

CONCLUSIONS

AICAR represses the MUC1 activity in EGFR-mutant lung cancer, disrupting protein-protein interactions between MUC1-CT and JAK1 and EGFR.

Loss of BLK expression as a potential predictor of poor prognosis and immune checkpoint blockade response in NSCLC and contribute to tumor progression

BACKGROUND

Immune checkpoint blockade (ICB) has been proved to have significant anti-tumor effect in the clinical treatment of non-small cell lung cancer (NSCLC). Therefore, biomarkers predicting ICB response can provide better treatment for patients with NSCLC.

METHODS

Differential expression genes (DEGs) were identified by ImmuCellAI database. Copy number alteration (CNA) was analyzed by cBioPortal. The predicted efficiency of 4 genes on cancer immunotherapy was assessed by ROC analysis. The survival value of BLK was analyzed by Kaplan-Meier plotter and Prognoscan analysis. Clinical significance of BLK IHC-TMA score in NSCLC was also explored. The CCK-8 assay, wound healing assay, western blot assay in vitro and subcutaneous xenograft experiments in vivo were used for investigating the functions of BLK. The RNA-sequencing were performed to screen BLK regulated genes and conducted for GO/KEGG enrichment analysis. The transcriptional regulatory factor of BLK promoter region was predicted by ChIP-seq analysis.

RESULTS

39 common DEGs between ICB Response (R) group and No Response (NR) group with NSCLC were identified, in which the CNA frequency of BLK deletion (> 6%) was found. The predicted efficiency of BLK on immunotherapy was performed best in NSCLC (AUC>0.7). Low expression of BLK was related to NSCLC with significantly poor prognosis. BLK overexpression can inhibit growth of NSCLC via activating apoptosis pathway, inhibiting the G2M checkpoint and Glycolysis pathway. The enrichment analysis indicated that BLK regulated genes related to oncogenic potential in NSCLC. Besides, BLK expression was inhibited via H3K27me3 modification in A549 and H1299 cells. BLK mRNA level was negatively correlated with methylation and positively correlated with the tumor purity in NSCLC.

CONCLUSION

Our study provides strong evidence that low expression of BLK may serve as a biomarker for poor prognosis in NSCLC, while response to ICB therapy and contributes to NSCLC tumor progression.

Prognostic value of interleukin-34 and interleukin-38 in patients with newly diagnosed atrial fibrillation

Background

Interleukin (IL)-34 and IL-38 are associated with cardiovascular disease (CVD). However, their involvement in atrial fibrillation (AF) and AF-associated adverse events remains uncertain. Therefore, we aimed to investigate their association with various AF prognostic factors in a cohort study and assessed their predictive value for the prognosis of patients with AF.

Methods

Patients with new-onset non-valvular AF were consecutively enrolled between 2013 and 2015 at the Department of Cardiovascular Medicine of the Southwest Hospital of the Army Medical University (Third Military Medical University) in Chongqing, China. The endpoints included stroke and all-cause mortality. The baseline levels of plasma IL-34, IL-38, NT-proBNP, high-sensitivity cardiac troponin T (hs-cTnT), and GDF-15 were measured and their correlation with AF-related adverse events were analyzed in a Cox proportional-hazards regression model. The C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the performance of the AF prognostic models. Decision curve analysis (DCA) was used to evaluate the clinical net benefit of the original and modified models.

Results

A total of 299 patients with new-onset AF were enrolled. During the median follow-up time of 28 (IQR: 27, 29) months, the higher levels of IL-34 were associated with a lower risk of stroke, and the higher levels of IL-38 were associated with an increased risk of all-cause death (all adjusted P < 0.05). In addition, elevated hs-cTnT and NT-proBNP concentrations were associated with a higher risk of stroke and all-cause mortality (all adjusted P < 0.05). Furthermore, the CHA₂DS₂-VASc score combined with IL-38 and NT-proBNP significantly improved the C-statistic, IDI, and NRI (all P < 0.01). There was no statistically significant difference (all P > 0.05) in the discrimination power between the preference models and the ABC (age, biomarkers, and clinical history) score for the two prognostic outcomes.

Conclusion

Our results suggested that IL-34 and IL-38 were independently associated with stroke and all-cause mortality in patients with AF. Moreover, adding IL-38 and NT-proBNP to the CHA₂DS₂-VASc score significantly improved its predictive ability of AF-related all-cause death. Finally, the preference model performed equally well as the ABC score in predicting AF prognosis.

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Upregulation of nuclear division cycle 80 contributes to therapeutic resistance via the promotion of autophagy-related protein-7-dependent autophagy in lung cancer

Lung cancer remains the leading cause of malignant mortality worldwide. Hence, the discovery of novel targets that can improve therapeutic effects in lung cancer patients is an urgent need. In this study, we screened differentially expressed genes using isobaric tags for relative and absolute quantitation (iTRAQ) analysis and datasets from the cancer genome atlas database, and found that nuclear division cycle 80 (NDC80) might act as a novel prognostic indicator of lung cancer. The expression of NDC80 was significantly increased in lung cancer tissues, as compared to normal tissues, and high expression levels of NDC80 were correlated with unfavorable survival rates. Furthermore, an in vitro analysis showed that the stable knockdown of NDC80 decreased the cell viability and increased therapeutic sensitivity in two lung cancer cell lines, A549-IRR and H1246-IRR. Moreover, gene set enrichment analysis results showed that NDC80 was enriched in autophagy-related pathways. The downregulation of NDC80 inhibited the formation of autophagosomes, and reduced the expression of autophagy-related proteins such as LC3II, Beclin-1, and p62 in lung cancer cells. To further clarify the role of NDC80 as a downstream regulator of autophagy, we validated autophagic mediators through iTRAQ analysis and real-time polymerase chain reaction arrays. Autophagy-related protein7 (ATG7) was observed to be downregulated after the knockdown of NDC80 in lung cancer cells. Immunohistochemistry assay results revealed that both NDC80 and ATG7 were upregulated in an array of lung adenocarcinoma samples, compared to normal tissues, and the expression of NDC80 was identified to be positively associated with the levels of ATG7. Our findings suggest that NDC80 promotes the development of lung cancer by regulating autophagy, and might serve as a potential target for increasing the therapeutic sensitivity of lung cancer.

CCT6A and CHCHD2 Are Coamplified with EGFR and Associated with the Unfavorable Clinical Outcomes of Lung Adenocarcinoma

Chaperonin containing TCP1 subunit 6A (CCT6A) and coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2) are located at the chromosome 7p11 region proximal to epidermal growth factor receptor (EGFR). However, the amplifications, expressions, and the prognostic effects of CCT6A and CHCDH2 in lung adenocarcinoma (LUAD) are unclear. Here, using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, we found that CCT6A was coamplified and coexpressed with EGFR in LUAD patients. CCT6A amplification was correlated with the unfavorable outcomes of LUAD. Moreover, CCT6A was upregulated in LUAD tissues, and CCT6A overexpression was correlated with the unfavorable relapse free survival or overall survival of LUAD. On the contrary, CCT6A was hypomethylated in LUAD, and CCT6A hypermethylation was correlated with the favorable overall survival of LUAD. Similar expression and methylation profiling of CCT6A were obtained in 479 lung normal tissues and 544 LUAD tissues collected from 11 independent datasets. In 1,462 LUAD patients from eight independent cohorts, CCT6A was also correlated with LUAD relapse-free survival or overall survival. Furthermore, CCT6A overexpression promoted the cell growth and invasion of LUAD. Identification of genes differentially expressed in CCT6A highly expressed LUAD patients revealed that CHCHD2 was the most correlated with CCT6A expression. CHCHD2 was coamplified with CCT6A. CHCHD2 was upregulated in LUAD tissues, and overexpression of CHCHD2 was correlated with the shorted relapse-free survival or overall survival of LUAD. Overall, our results revealed that CCT6A and CHCHD2 were coamplifying and coexpressing with EGFR and were correlated with the unfavorable clinical outcomes of LUAD.

Identification of metastasis-related long non-coding RNAs in lung cancer through a novel tumor mesenchymal score

Long non-coding RNAs (lncRNAs) have been proven to play critical roles in epithelial-mesenchymal transition (EMT) and metastasis of lung cancer. However, the biological functions and related mechanisms of lncRNAs are unclear. In addition, the EMT-based prognosis prediction in lung cancer still lacks investigation. Here, we established the methodology of identifying critical metastasis-related lncRNAs using comprehensive datasets of cancer transcriptome, genome and epigenome, and also provided tools for prognosis prediction in lung cancer. Initially, important mesenchymal marker genes were identified to compose the tumor mesenchymal score, which predicted patient prognosis in lung cancer, especially lung adenocarcinoma (LUAD). The score was also correlated with several crucial biological and physiological processes, such as tumor immune and hypoxia. Based on the score, lung cancer patients was classified into epithelial and mesenchymal subtypes, and lncRNAs which exhibited expressional dysregulation, promotor methylation alteration and copy number variation between the two subtypes in LUAD were identified and underwent further prognostic analyses. Finally, we identified 14 lncRNAs as EMT-related and significant biomarkers in prognosis prediction of LUAD. As validation, lncRNA RBPMS-AS1 was proven to be co-expressed with epithelial biomarkers, suppressive for A549 cell migration, invasion and EMT, and also significantly associated with better outcomes of LUAD patients, suggesting the potential of RBPMS-AS1 to serve as a lncRNA epithelial biomarker in metastasis of LUAD. Based on the identified lncRNAs, an EMT-linked lncRNA prognostic signature was further established. Taken together, our study provides robust predictive tools, potential lncRNA targets and feasible screening strategies for future study of lung cancer metastasis.

Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics

Lung cancer persists to contribute to one-quarter of cancer-associated deaths. Among the different histologies, non-small cell lung cancer (NSCLC) alone accounts for 85% of the cases. The development of therapies involving immune checkpoint inhibitors and angiogenesis inhibitors has increased patients' survival probability and reduced mortality rates. Developing targeted therapies against essential genetic alterations also translates to better treatment strategies. But the benefits still seem farfetched due to the development of drug resistance and refractory tumors. In this review, we have highlighted the interplay of different tumor microenvironment components, essentially discussing the chemokine families (CC, CXC, C, and CX3C) that regulate the tumor biology in NSCLC and promote tumor growth, metastasis, and associated heterogeneity. The development of therapeutics and prognostic markers is a complex and multipronged approach. However, some essential chemokines can act as critical players for being considered potential prognostic markers and therapeutic targets.

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

PURPOSE

Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN).

METHODS

This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence.

RESULTS

The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86.

CONCLUSION

We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.

Hypoxia Selectively Increases a SMAD3 Signaling Axis to Promote Cancer Cell Invasion

Transforming growth factor β (TGFβ) plays a paradoxical role in cancer, first inhibiting then promoting its progression, a duality that poses a real challenge for the development of effective TGFβ-targeted therapies. The major TGFβ downstream effectors, SMAD2 and SMAD3, display both distinct and overlapping functions and accumulating evidence suggests that their activation ratio may contribute to the dual effect of TGFβ. However, the mechanisms responsible for their selective activation remain poorly understood. Here, we provide experimental evidence that hypoxia induces the pro-invasive arm of TGFβ signaling through a selective increase in SMAD3 interaction with SMAD-Anchor for Receptor Activation (SARA). This event relies on HDAC6-dependent SMAD3 bioavailability, as well as increased SARA recruitment to EEA1+ endosomes. A motility gene expression study indicated that SMAD3 selectively increased the expression of ITGB2 and VIM, two genes that were found to be implicated in hypoxia-induced cell invasion and associated with tumor progression and metastasis in cohorts of cancer patients. Furthermore, CAM xenograft assays show the significant benefit of selective inhibition of the SMAD3 signaling pathway as opposed to global TGFβ inhibition in preventing tumor progression. Overall, these results suggest that fine-tuning of the pro-invasive HDAC6-SARA-SMAD3 axis could be a better strategy towards effective cancer treatments.

Comprehensive analysis of the prognostic value and immune implications of the TTK gene in lung adenocarcinoma: a meta-analysis and bioinformatics analysis

Background

High expression levels of the TTK gene are closely related to tumor occurrence and poor prognosis, as confirmed by some studies. Our study explored the prognosis of lung adenocarcinoma (LUAD) patients with different TTK levels and the possible pathological mechanism of TTK in LUAD.

Methods

We extensively searched literature databases and high-throughput sequencing databases and included relevant literature or datasets in the meta-analysis according to the inclusion and exclusion criteria. Hazard ratios (HRs) and 95% confidence intervals (CIs) related to TTK expression were calculated, publication bias was assessed, and sensitivity tests were performed. We also compared the relationship between cancer immune infiltrating cells and tumor mutation burden (TMB) in patients with different TTK expression levels via bioinformatics analysis. The half maximal inhibitory concentration (IC50) of some chemotherapeutic and targeted therapy drugs were calculated. The potential biological functions or pathways associated with different TTK expression levels were determined by gene set enrichment analysis (GSEA).

Results

The meta-analysis revealed that higher TTK expression level was significantly associated with poor prognosis in LUAD patients, both in overall survival (OS) and progression-free survival (PFS). The expression level of TTK was significantly correlated with presence of some immune cells and TMB. Tumors with higher TTK expression levels were mostly enriched for the cell cycle, DNA replication and homologous recombination pathways. In addition, patients with different TTK expression levels were differently sensitive to some antitumor drugs.

Conclusion

TTK may be a promising prognostic biomarker for LUAD and is worthy of further investigation.

Molecular Radiobiology in Non-Small Cell Lung Cancer: Prognostic and Predictive Response Factors

Simple Summary

The identification of prognostic and predictive gene signatures of response to cancer treatment (radiotherapy) could help in making therapeutic decisions in patients affected by NSCLC. There are multiple proposals for gene signatures that attempt to predict survival or predict response to treatment (not radiotherapy), but they mainly focus on early stages or metastasis at diagnosis. In contrast, there have been few studies that raise these predictive and/or prognostic elements in nonmetastatic locally advanced stages, where treatment with ionizing radiation plays an important role. In this work, we review in depth previous works discovering the prognostic and predictive response factors in non-small cell lung cancer, specially focused on non-deeply studied radiation-based therapy.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, generating huge economic and social impacts that have not slowed in recent years. Oncological treatment for this neoplasm usually includes surgery, chemotherapy, treatments on molecular targets and ionizing radiation. The prognosis in terms of overall survival (OS) and the different therapeutic responses between patients can be explained, to a large extent, by the existence of widely heterogeneous molecular profiles. The identification of prognostic and predictive gene signatures of response to cancer treatment, could help in making therapeutic decisions in patients affected by NSCLC. Given the published scientific evidence, we believe that the search for prognostic and/or predictive gene signatures of response to radiotherapy treatment can significantly help clinical decision-making. These signatures may condition the fractions, the total dose to be administered and/or the combination of systemic treatments in conjunction with radiation. The ultimate goal is to achieve better clinical results, minimizing the adverse effects associated with current cancer therapies.

A Six-Gene Prognostic and Predictive Radiotherapy-Based Signature for Early and Locally Advanced Stages in Non-Small-Cell Lung Cancer

Simple Summary

The search for prognostic and/or predictive gene signatures of the response to radiotherapy treatment can significantly aid clinical decision making. These signatures can condition the fractionation, the total dose to be administered, and/or the combination of systemic treatments and radiation. The ultimate goal is to achieve better clinical results, as well as to minimize the adverse effects associated with current cancer therapies. To this end, we analyzed the intrinsic radiosensitivity of 15 NSCLC lines and found the differences in gene expression levels between radiosensitive and radioresistant lines, resulting in a potentially applicable six-gene signature in NSCLC patients. The six-gene signature had the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, generating an enormous economic and social impact that has not stopped growing in recent years. Cancer treatment for this neoplasm usually includes surgery, chemotherapy, molecular targeted treatments, and ionizing radiation. The prognosis in terms of overall survival (OS) and the disparate therapeutic responses among patients can be explained, to a great extent, by the existence of widely heterogeneous molecular profiles. The main objective of this study was to identify prognostic and predictive gene signatures of response to cancer treatment involving radiotherapy, which could help in making therapeutic decisions in patients with NSCLC. To achieve this, we took as a reference the differential gene expression pattern among commercial cell lines, differentiated by their response profile to ionizing radiation (radiosensitive versus radioresistant lines), and extrapolated these results to a cohort of 107 patients with NSCLC who had received radiotherapy (among other therapies). We obtained a six-gene signature (APOBEC3B, GOLM1, FAM117A, KCNQ1OT1, PCDHB2, and USP43) with the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received. Patients who had an unfavorable prognostic signature had a median OS of 24.13 months versus 71.47 months for those with a favorable signature, and the median PFS was 12.65 months versus 47.11 months, respectively. We also carried out a univariate analysis of multiple clinical and pathological variables and a bivariate analysis by Cox regression without any factors that substantially modified the HR value of the proposed gene signature.

In Silico Bioinformatics Followed by Molecular Validation Using Archival FFPE Tissue Biopsies Identifies a Panel of Transcripts Associated with Severe Asthma and Lung Cancer

Simple Summary

The present study identified a panel of transcripts involved in the pathogenesis of both severe asthma and lung cancer. The genes identified using publicly available transcriptomics data were validated on cell lines, plasma samples, and archival tissue biopsies from asthmatic and lung cancer patients. The functional roles of the identified markers in both the diseases were ascertained from the literature. These molecular markers might be useful for diagnosing lung cancer at early stages.

Abstract

Severe asthma and lung cancer are both heterogeneous pathological diseases affecting the lung tissue. Whilst there are a few studies that suggest an association between asthma and lung cancer, to the best of our knowledge, this is the first study to identify common genes involved in both severe asthma and lung cancer. Publicly available transcriptomic data for 23 epithelial brushings from severe asthmatics and 55 samples of formalin-fixed paraffin-embedded (FFPE) lung cancer tissue at relatively early stages were analyzed by absolute gene set enrichment analysis (GSEA) in comparison to 37 healthy bronchial tissue samples. The key pathways enriched in asthmatic patients included adhesion, extracellular matrix, and epithelial cell proliferation, which contribute to tissue remodeling. In the lung cancer dataset, the main pathways identified were receptor tyrosine kinase signaling, wound healing, and growth factor response, representing the early cancer pathways. Analysis of the enriched genes derived from the pathway analysis identified seven genes expressed in both the asthma and lung cancer sets: BCL3, POSTN, PPARD, STAT1, MYC, CD44, and FOSB. The differential expression of these genes was validated in vitro in the cell lines retrieved from different lung cancer and severe asthma patients using real-time PCR. The effect of the expression of the seven genes identified in the study on the overall survival of lung cancer patients (n = 1925) was assessed using a Kaplan–Meier plot. In vivo validation performed in the archival biopsies obtained from patients diagnosed with both the disease conditions provided interesting insights into the pathogenesis of severe asthma and lung cancer, as indicated by the differential expression pattern of the seven transcripts in the mixed group as compared to the asthmatics and lung cancer samples alone.

Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer

Background: Lung cancer is the second most common cancer and the main leading cause of cancer-associated death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer diagnoses and more than 50% of all lung cancer cases are diagnosed at an advanced stage; hence have poor prognosis. Therefore, it is important to diagnose NSCLC patients reliably and as early as possible in order to reduce the risk of mortality.

Methods: We identified blood-based gene markers for early NSCLC by performing a multi-omics approach utilizing integrated analysis of global gene expression and copy number alterations of NSCLC patients using array-based techniques. We also validated the diagnostic and the prognostic potential of the gene signature using independent datasets with detailed clinical information.

Results: We identified 12 genes that are significantly expressed in NSCLC patients’ blood, at the earliest stages of the disease, and associated with a poor disease outcome. We then validated 12-gene signature’s diagnostic and prognostic value using independent datasets of gene expression profiling of over 1000 NSCLC patients. Indeed, 12-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis (HR = 2.64, 95% CI = 1.72–4.07; p = 1.3 × 10−8). Significantly altered functions, pathways, and gene networks revealed alterations in several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5.

Conclusion: Our findings suggest that integrated genomic and network analyses may provide a reliable approach to identify genes that are associated with NSCLC, and lead to improved diagnosis detecting the disease in early stages in patients’ blood instead of using invasive techniques and also have prognostic potential for discriminating high-risk patients from the low-risk ones.

A lepidic gene signature predicts patient prognosis and sensitivity to immunotherapy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care.

METHODS

We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets.

RESULTS

We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy.

CONCLUSIONS

Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

MicroRNA-21 guide and passenger strand regulation of adenylosuccinate lyase-mediated purine metabolism promotes transition to an EGFR-TKI-tolerant persister state

In EGFR-mutant lung cancer, drug-tolerant persister cells (DTPCs) show prolonged survival when receiving EGFR tyrosine kinase inhibitor (TKI) treatments. They are a likely source of drug resistance, but little is known about how these cells tolerate drugs. Ribonucleic acids (RNAs) molecules control cell growth and stress responses. Nucleic acid metabolism provides metabolites, such as purines, supporting RNA synthesis and downstream functions. Recently, noncoding RNAs (ncRNAs), such as microRNAs (miRNAs), have received attention due to their capacity to repress gene expression via inhibitory binding to downstream messenger RNAs (mRNAs). Here, our study links miRNA expression to purine metabolism and drug tolerance. MiR-21-5p (guide strand) is a commonly upregulated miRNA in disease states, including cancer and drug resistance. However, the expression and function of miR-21-3p (passenger strand) are not well understood. We found that upregulation of miR-21-5p and miR-21-3p tune purine metabolism leading to increased drug tolerance. Metabolomics data demonstrated that purine metabolism was the top pathway in the DTPCs compared with the parental cells. The changes in purine metabolites in the DTPCs were partially rescued by targeting miR-21. Analysis of protein levels in the DTPCs showed that reduced expression of adenylosuccinate lyase (ADSL) was reversed after the miR-21 knockdown. ADSL is an essential enzyme in the de novo purine biosynthesis pathway by converting succino-5-aminoimidazole-4-carboxamide riboside (succino-AICAR or SAICAR) to AICAR (or acadesine) as well as adenylosuccinate to adenosine monophosphate (AMP). In the DTPCs, miR-21-5p and miR-21-3p repress ADSL expression. The levels of top decreased metabolite in the DTPCs, AICAR was reversed when miR-21 was blocked. AICAR induced oxidative stress, evidenced by increased reactive oxygen species (ROS) and reduced expression of nuclear factor erythroid-2-related factor 2 (NRF2). Concurrently, miR-21 knockdown induced ROS generation. Therapeutically, a combination of AICAR and osimertinib increased ROS levels and decreased osimertinib-induced NRF2 expression. In a MIR21 knockout mouse model, MIR21 loss-of-function led to increased purine metabolites but reduced ROS scavenging capacity in lung tissues in physiological conditions. Our data has established a link between ncRNAs, purine metabolism, and the redox imbalance pathway. This discovery will increase knowledge of the complexity of the regulatory RNA network and potentially enable novel therapeutic options for drug-resistant patients.

The Prognostic Significance of PD1 and PDL1 Gene Expression in Lung Cancer: A Meta-Analysis

Background

Immune checkpoint blockade therapy represents an extraordinary advance in lung cancer treatment. It is important to determine the expression of immune checkpoint genes, such as programmed cell death 1 (PD1) and programmed cell death-ligand 1 (PDL1), to develop immunotherapeutic strategies. The aim of this study was to explore the association between PD1 and PDL1 gene expression and prognoses and outcomes in lung cancer.

Methods

This meta-analysis analyzed 1,251 patients from eight different microarray gene expression datasets and were evaluated for their prognostic implications and verified using another independent research.

Results

The mean expression levels of PDL1 in adenocarcinoma (AD) and squamous cell carcinoma (SC) were significantly higher in patients who died than in patients who did not. There was a trend toward incremental increases in PD1 and PDL1 expression significantly decreasing the risk of relapse and death among AD patients (HR = 0.69; 95% CI = 0.53 ~ 0.91; HR = 0.68; 95% CI = 0.54 ~ 0.84, respectively) and SC patients (HR = 0.53; 95% CI = 0.32 ~ 0.89; HR = 0.78; 95% CI = 0.57 ~ 1.00 respectively), as early-stage patients in this study were more likely to have high expression of both PD1 and PDL1 than late-stage patients (P-trend < 0.05). In contrast, late-stage SC patients expressing one or more of the genes at a high level had a significantly elevated risk of relapse (HR = 1.51; 95% CI = 1.07 ~ 2.11) and death (HR = 1.41; 95% CI = 1.08 ~ 1.84). This result was consistent with the validation data set.

Conclusion

These findings indicate that high expression of PD1 and PDL1 is associated with superior outcome in early-stage lung cancer but an adverse outcome in late-stage lung cancer. The expression levels of PD1 and PDL1 individually or jointly are potential prognostic factors for predicting patient outcomes in lung cancer.

HSD17B6 downregulation predicts poor prognosis and drives tumor progression via activating Akt signaling pathway in lung adenocarcinoma

Lung adenocarcinoma is one of the most frequent tumor subtypes, involving changes in a variety of oncogenes and tumor suppressor genes. Hydroxysteroid 17-Beta Dehydrogenase 6 (HSD17B6) could synthetize dihydrotestosterone, abnormal levels of which are associated with progression of multiple tumors. Previously, we showed that HSD17B6 inhibits malignant progression of hepatocellular carcinoma. However, the mechanisms underlying inhibiting tumor development by HSD17B6 are not clear. Moreover, its role in lung adenocarcinoma (LUAD) is yet unknown. Here, we investigated its expression profile and biological functions in LUAD. Analysis of data from the LUAD datasets of TCGA, CPTAC, Oncomine, and GEO revealed that HSD17B6 mRNA and protein expression was frequently lower in LUAD than in non-neoplastic lung tissues, and its low expression correlated significantly with advanced tumor stage, large tumor size, poor tumor differentiation, high tumor grade, smoking, and poor prognosis in LUAD. In addition, its expression was negatively regulated by miR-31-5p in LUAD. HSD17B6 suppressed LUAD cell proliferation, migration, invasion, epithelial-mesenchymal transition (EMT), and radioresistance. Furthermore, HSD17B6 overexpression in LUAD cell lines enhanced PTEN expression and inhibited AKT phosphorylation, inactivating downstream oncogenes like GSK3β, β-catenin, and Cyclin-D independent of dihydrotestosterone, revealing an underlying antitumor mechanism of HSD17B6 in LUAD. Our findings indicate that HSD17B6 may function as a tumor suppressor in LUAD and could be a promising prognostic indicator for LUAD patients, especially for those receiving radiotherapy.

Nucleoporin TPR promotes tRNA nuclear export and protein synthesis in lung cancer cells

The robust proliferation of cancer cells requires vastly elevated levels of protein synthesis, which relies on a steady supply of aminoacylated tRNAs. Delivery of tRNAs to the cytoplasm is a highly regulated process, but the machinery for tRNA nuclear export is not fully elucidated. In this study, using a live cell imaging strategy that visualizes nascent transcripts from a specific tRNA gene in yeast, we identified the nuclear basket proteins Mlp1 and Mlp2, two homologs of the human TPR protein, as regulators of tRNA export. TPR expression is significantly increased in lung cancer tissues and correlated with poor prognosis. Consistently, knockdown of TPR inhibits tRNA nuclear export, protein synthesis and cell growth in lung cancer cell lines. We further show that NXF1, a well-known mRNA nuclear export factor, associates with tRNAs and mediates their transport through nuclear pores. Collectively, our findings uncover a conserved mechanism that regulates nuclear export of tRNAs, which is a limiting step in protein synthesis in eukaryotes.

Regulation of EGFR signalling by palmitoylation and its role in tumorigenesis

The epidermal growth factor receptor (EGFR) is an essential driver of oncogenic signalling, and EGFR inhibitors are some of the earliest examples of successful targeted therapies in multiple types of cancer. The tractability of EGFR as a therapeutic target is overshadowed by the inevitable drug resistance that develops. Overcoming resistance mechanisms requires a deeper understanding of EGFR regulation in cancer cells. In this review, we discuss our recent discovery that the palmitoyltransferase DHHC20 palmitoylates EGFR on the C-terminal domain and plays a critical role in signal regulation during oncogenesis. Inhibiting DHHC20 expression or mutating the palmitoylation site on EGFR alters the EGF-induced signalling kinetics from a transient signal to a sustained signal. The change in signalling is accompanied by a decrease in cell proliferation in multiple human cancer cell lines. Our in vivo studies demonstrate that ablating the gene Zdhhc20 by CRISPR/Cas9-mediated inhibition in a mouse model of oncogenic Kras-driven lung adenocarcinoma potently inhibits tumorigenesis. The negative effect on tumorigenesis is mediated by EGFR since the expression of a palmitoylation-resistant mutant form of EGFR also inhibits Kras-driven lung adenocarcinoma. Finally, reducing EGFR palmitoylation increases the sensitivity of multiple cancer cell lines to existing inhibitors of EGFR and downstream signalling effector pathways. We will discuss the implications of these effects and strategies for targeting these new vulnerabilities.

Identification of a potential tumor suppressor gene, UBL3, in non-small cell lung cancer

Objective: Oncogenes have been shown to be drivers of non-small cell lung cancer (NSCLC), yet the tumor suppressing genes involved in lung carcinogenesis remain to be systematically investigated. This study aimed to identify tumor suppressing ubiquitin pathway genes (UPGs) that were critical to lung tumorigenesis.

Methods: The 696 UPGs were silenced by an siRNA screening in NSCLC cells; the potential tumor suppressing UPGs were analyzed, and their clinical significance was investigated.

Results: We reported that silencing of 11 UPGs resulted in enhanced proliferation of NSCLC cells, and four UPGs (UBL3, TRIM22, UBE2G2, and MARCH1) were significantly downregulated in tumor samples compared to that in normal lung tissues and their expression levels were positively associated with overall survival (OS) of NSCLC patients. Among these genes, UBL3 was the most significant one. UBL3 expression was decreased in tumor samples compared to that in paired normal lung tissues in 59/86 (68.6%) NSCLCs, was correlated with TNM stage and sex of NSCLC patients, and was significantly higher in non-smoking patients than in smoking patients. Silencing UBL3 accelerated cell proliferation and ectopic expression of UBL3 suppressed NSCLC in vitro and in vivo.

Conclusions: These results showed that UBL3 represented a tumor suppressor in NSCLC and may have potential for use in therapeutics and for the prediction of clinical outcome of patients.

Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression–based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.

Glucocorticoid receptor triggers a reversible drug-tolerant dormancy state with acquired therapeutic vulnerabilities in lung cancer

The glucocorticoid receptor (GR) regulates gene expression, governing aspects of homeostasis, but is also involved in cancer. Pharmacological GR activation is frequently used to alleviate therapy-related side-effects. While prior studies have shown GR activation might also have anti-proliferative action on tumours, the underpinnings of glucocorticoid action and its direct effectors in non-lymphoid solid cancers remain elusive. Here, we study the mechanisms of glucocorticoid response, focusing on lung cancer. We show that GR activation induces reversible cancer cell dormancy characterised by anticancer drug tolerance, and activation of growth factor survival signalling accompanied by vulnerability to inhibitors. GR-induced dormancy is dependent on a single GR-target gene, CDKN1C, regulated through chromatin looping of a GR-occupied upstream distal enhancer in a SWI/SNF-dependent fashion. These insights illustrate the importance of GR signalling in non-lymphoid solid cancer biology, particularly in lung cancer, and warrant caution for use of glucocorticoids in treatment of anticancer therapy related side-effects.

Prognostic value of baculoviral IAP repeat containing 5 expression as a new biomarker in lung adenocarcinoma: a meta-analysis

BACKGROUND

BIRC5 is associated with the prognosis of a variety of tumors. This meta-analysis aimed to identify whether BIRC5 is associated with the prognosis of lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

We conducted an in-depth review of seven Chinese and English databases and two high-throughput sequencing databases according to inclusion and exclusion criteria to find relevant studies. The pooled standardized mean differences (SMDs) and 95% confidence intervals (CIs) for the associations between the BIRC5 expression level and clinicopathological characteristics were calculated, and the pooled hazard ratios (HRs) and 95% CIs were calculated to estimate associations between the BIRC5 expression level and survival outcomes.

RESULTS

In total, 17 studies involving 2887 LUAD patients whose BIRC5 expression level was known were included in this meta-analysis. The BIRC5 expression level was higher in younger patients, males, and smokers and correlated with advanced AJCC, T and N stages but not M stage. A high BIRC5 expression level also correlated with poor overall survival (OS) and progression-free survival (PFS). There was no publication bias in this study.

CONCLUSIONS

This meta-analysis indicates that BIRC5 is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

TMEM229A suppresses non‑small cell lung cancer progression via inactivating the ERK pathway

Transmembrane protein 229A (TMEM229A) is a member of the TMEM family that plays an important role in tooth differentiation and development. However, the expression level and biological role of TMEM229A in cancer remains unknown. The present study aimed to investigate the expression level of TMEM229A in non‑small cell lung cancer (NSCLC), as well as its effect and mechanism on NSCLC progression. Clinical specimens from patients with NSCLC were enrolled from the First People's Hospital of Huzhou (Huzhou, China). TMEM229A expression was detected using reverse transcription‑quantitative PCR (RT‑qPCR), western blotting and immunohistochemical analysis. The relationship between TMEM229A expression and the survival rate of patients with NSCLC was analyzed using Kaplan‑Meier Plotter datasets. The effects of TMEM229A on cell proliferation, migration and invasion were detected using Cell Counting Kit‑8, colony formation, soft agar, real‑time cellular analysis and Transwell assays. The expression levels of epithelial‑mesenchymal transition (EMT)‑related proteins, as well as ERK and AKT phosphorylation were determined via RT‑qPCR and western blot analysis. The results demonstrated that TMEM229A expression was significantly downregulated in human NSCLC tissues and in several cell lines compared with adjacent normal lung tissues and BEAS‑2B cells, respectively. Survival analysis of lung adenocarcinoma and squamous cell lung carcinoma cases identified that low TMEM229A expression was associated with a poor prognosis. The in vitro assays indicated that overexpressing TMEM229A significantly inhibited cell proliferation, migration and invasion, while TMEM229A knockdown had the opposite effect. Mechanistically, TMEM229A overexpression effectively increased E‑cadherin expression and reduced N‑cadherin, snail family transcriptional repressor 1 and MMP2 expression, indicating that EMT was suppressed. In addition, overexpression of TMEM229A reduced the expression levels of phosphorylated (p)‑ERK and p‑AKT, and this effect was partially suppressed by the incorporation of specific ERK inhibitor PD98059. Collectively, the results of the present study demonstrated that the effects of TMEM229A on inhibiting cell proliferation, migration and invasion were partially mediated by inactivating the ERK signaling pathway, thereby providing a potential therapeutic target for the treatment of NSCLC.

Lung Cancer Cell-Derived Exosomal let-7d-5p Down-Regulates OPRM1 to Promote Cancer-Induced Bone Pain

Cancer-induced bone pain (CIBP) is the pain caused by metastasis of malignant tumors to the bone, accounting for more than half of all chronic cancer pain, which seriously affects the quality of life among tumor patients. Up to 40% of patients with advanced lung cancer suffer from CIBP. MicroRNA (miRNA) transfers between cells through exosomes, mediates cell-to-cell communication, and performs various biological functions. Studies have shown that miRNAs secreted by cancer can modify the tumor microenvironment, but whether exosome-mediated miRNA transfer plays a role in CIBP is still unknown. In this study, the expression levels of 15 miRNAs in exosomes derived A549 cells and 18 miRNAs in exosomes derived NCI-H1299 cells were significantly up-regulated, and qRT-PCR further confirmed that the level of let-7d-5p was increased most considerably. In vitro, exosomal let-7d-5p (EXO let-7d-5p) can be taken up by dorsal root ganglion (DRG) neurons and inhibit the protein level of the target gene opioid receptor mu 1 (OPRM1). EXO let-7d-5p was further confirmed to be involved in the generation and maintenance of CIBP in vivo. Our findings clarify the molecular mechanism of CIBP caused by the inhibition of OPRM1 by EXO let-7d-5p, providing new clues and intervention targets for the prevention and treatment of CIBP.

Prognostic and clinicopathological value of BUB1B expression in patients with lung adenocarcinoma: a meta-analysis

BACKGROUND

Abnormal BUB1B expression has been proven to be related to the poor prognosis of various tumors. This meta-analysis aimed to identify the prognostic role of BUB1B in patients with lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

Relevant studies from the PubMed, Embase, Web of Science, and Cochrane Library databases and two public databases that stored sequencing data were retrieved. The standardized mean difference (SMD) and 95% confidence intervals (CIs) for the association between the BUB1B expression level and clinical characteristics were calculated. Pooled hazard ratios (HRs) and 95% CIs were calculated to estimate the association between BUB1B expression and survival outcomes.

RESULTS

A total of 16 studies involving 2771 LUAD patients with BUB1B expression were included in this meta-analysis. Patients with older age showed low BUB1B expression. High BUB1B expression was associated with male sex, a smoking history, and an advanced TNM stage. High BUB1B expression was predictive of poor overall survival (OS) and progression-free survival (PFS). In addition, no publication bias was found.

CONCLUSIONS

This meta-analysis demonstrates that BUB1B is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

Androgen Receptor Regulates CD44 Expression in Bladder Cancer

The androgen receptor (AR) is important in the development of both experimental and human bladder cancer. However, the role of AR in bladder cancer growth and progression is less clear, with literature indicating that more advanced stage and grade disease are associated with reduced AR expression. To determine the mechanisms underlying these relationships, we profiled AR-expressing human bladder cancer cells by AR chromatin immunoprecipitation sequencing and complementary transcriptomic approaches in response to in vitro stimulation by the synthetic androgen R1881. In vivo functional genomics consisting of pooled shRNA or pooled open reading frame libraries was employed to evaluate 97 genes that recapitulate the direction of expression associated with androgen stimulation. Interestingly, we identified CD44, the receptor for hyaluronic acid, a potent biomarker and driver of progressive disease in multiple tumor types, as significantly associated with androgen stimulation. CRISPR-based mutagenesis of androgen response elements associated with CD44 identified a novel silencer element leading to the direct transcriptional repression of CD44 expression. In human patients with bladder cancer, tumor AR and CD44 mRNA and protein expression were inversely correlated, suggesting a clinically relevant AR-CD44 axis. Collectively, our work describes a novel mechanism partly explaining the inverse relationship between AR and bladder cancer tumor progression and suggests that AR and CD44 expression may be useful for prognostication and therapeutic selection in primary bladder cancer. SIGNIFICANCE: This study describes novel AREs that suppress CD44 and an expected inverse correlation of AR-CD44 expression observed in human bladder tumors.

CD96 Correlates With Immune Infiltration and Impacts Patient Prognosis: A Pan-Cancer Analysis

Background

Immunotherapy has significantly improved patient outcomes, but encountered obstacles recently. CD96, a novel immune checkpoint expressed on T cells and natural killer (NK) cells, is essential for regulating immune functions. However, how CD96 correlating with immune infiltration and patient prognosis in pan-cancer remains unclear.

Methods

HPA, TCGA, GEO, GTEx, Oncomine, TIMER2.0, PrognoScan, Linkedomics, Metascape, and GEPIA2 databases were used to analyze CD96 in cancers. Visualization of data was mostly achieved by R language, version 4.0.2.

Results

In general, CD96 was differentially expressed between most cancer and adjacent normal tissues. CD96 significantly impacted the prognosis of diverse cancers. Especially, high CD96 expression was associated with poorer overall survival (OS) and disease-specific survival (DSS) in the TCGA lower grade glioma (LGG) cohort (OS, HR = 2.18, 95% CI = 1.79–2.66, P < 0.001). The opposite association was significantly observed in skin cutaneous melanoma (SKCM) cohort (OS, HR = 0.96, 95% CI = 0.94–0.98, P < 0.001). Notably, SKCM samples demonstrated the highest CD96 mutation frequency among all cancer types. Furthermore, in most cancers, CD96 expression level was significantly correlated with expression levels of recognized immune checkpoints and abundance of multiple immune infiltrates including CD8+ T cells, dendric cells (DCs), macrophages, monocytes, NK cells, neutrophils, regulatory T cells (Tregs), and follicular helper T cells (Tfh). CD96 was identified as a risk factor, protective factor, and irrelevant variable in LGG, SKCM and adrenocortical carcinoma (ACC), respectively. CD96 related genes were involved in negative regulation of leukocyte in LGG, however, involved in multiple positive immune processes in SKCM. Furthermore, CD96 was significantly associated with particular immune marker subsets. Importantly, it strongly correlated with markers of type 1 helper T cell (Th1) in SKCM, but not in LGG or ACC either.

Conclusions

CD96 participates in diverse immune responses, governs immune cell infiltration, and impacts malignant properties of various cancer types, thus standing as a potential biomarker for determining patient prognosis and immune infiltration in multiple cancers, especially in glioma and melanoma.

Prognostic and Clinicopathological Value of ZWINT Expression Levels in Patients with Lung Adenocarcinoma: A Systematic Review and Meta-analysis

The current study found that high Zeste White 10 interactor (ZWINT) expression is related to the poor prognosis of patients with a variety of cancers. This study mainly explored the relationship between the expression level of ZWINT and the prognosis of patients with lung adenocarcinoma (LUAD). Briefly, four English databases and two high-throughput sequencing databases were searched and relevant data for meta-analysis were extracted. Pooled mean difference and 95% confidence interval (CI) were used to assess the relationships between clinical features and the expression of ZWINT. Pooled hazard ratio and 95% CI were also used to assess the relationships between clinical features and the expression level of ZWINT. This meta-analysis was registered in PROSPERO (CRD42021249475). A total of 16 high-quality datasets comprising 2,847 LUAD patients were included in this study. Higher ZWINT expression levels were found in patients younger than 65 years, males, and smokers, and were correlated with advanced TNM stages and poor prognosis. Notably, there was no publication bias in this meta-analysis. Overall, our findings indicate that ZWINT is a potential biomarker for poor prognosis and clinicopathological outcomes of patients with LUAD.

Upregulation of CD109 Promotes the Epithelial-to-Mesenchymal Transition and Stemness Properties of Lung Adenocarcinomas via Activation of the Hippo-YAP Signaling

Metastasis is the leading cause of death in lung adenocarcinomas. Identifying potential prognostic biomarkers and exploiting regulatory mechanisms could improve the diagnosis and treatment of lung cancer patients. We previously found that cluster of differentiation 109 (CD109) was upregulated in lung tumor tissues, and CD109 overexpression was correlated with the invasive and metastatic capacities of lung adenocarcinoma cells. However, the contribution of CD109 to lung tumorigenesis remains to be elucidated. In the present study, we identified that CD109 was upregulated in metastatic lung adenocarcinoma cells, and elevation of CD109 was correlated with epithelial-to-mesenchymal transition (EMT) traits in patients with lung adenocarcinoma. Functionally, CD109 expression was crucial for EMT gene expressions, tumor invasiveness, and cancer stemness properties. Moreover, elevation of CD109 was accompanied by upregulation of the yes-associated protein (YAP) signature in metastatic lung cancer cells and lung cancer patients, and activation of YAP was demonstrated to participate in CD109-elicited EMT gene expressions and tumor invasiveness. Our study reveals the molecular mechanism underlying CD109 in lung tumor aggressiveness, and CD109 could be a potential diagnostic and therapeutic target for lung cancer patients.

Prognostic value of ESR2 expression on adjuvant chemotherapy in completely resected NSCLC

Background

Prognostic biomarker, which can inform the treatment outcome of adjuvant chemotherapy (ACT) after complete resection of early-stage non-small cell lung cancer (NSCLC), is urgently needed for the personalized treatment of these patients.

Patients and methods

The prognostic value of gene expression of the estrogen receptor (ER) on the effect of ACT in completely resected NSCLC was investigated in the present study. Two independent datasets from Gene Expression Omnibus (GEO) with a total of 309 patients were included in this study. The prognostic value of ER gene expression on ACT’s efficacy was evaluated by survival analysis and Cox hazards models.

Results

We found a consistent and significant prognostic value of ERβ (ESR2) expression for ACT’s efficacy in completely resected NSCLC in both of the two independent cohorts. After multivariate adjustment, a significant survival benefit of ACT was observed in patients with low expression of ESR2, with a hazard ratio (HR) of 0.19 (95%CI 0.05–0.82, p = 0.026) in the discovery cohort and an HR of 0.27 (95%CI 0.10–0.76, p = 0.012) in the validation group. No significant benefit of ACT in the subgroup of patients with high expression of ESR2 was observed, with an HR of 0.80 (95%CI 0.31–2.09, p = 0.644) in the discovery cohort and an HR of 1.05 (95%CI 0.48–2.29, p = 0.896) in the validation group.

Conclusion

A significant survival benefit from ACT was observed in patients with low ESR2 expression. No significant survival benefit was observed in patients with high ESR2 expression. Detection of ESR2 expression in NSCLC may help personalize its treatment after complete resection.

An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression

Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.

Expression of ATP/GTP Binding Protein 1 Has Prognostic Value for the Clinical Outcomes in Non-Small Cell Lung Carcinoma

ATP/GTP binding protein 1 (AGTPBP1) encodes a crucial protein, cytosolic carboxypeptidase 1 (CCP1), which plays a role in modulating the polyglutamylation of tubulin and has been studied in degenerative diseases. However, the role of AGTPBP1 in malignancy has not been completely studied yet. In this study, we examined the role of AGTPBP1 in cancer progression, its association with patient survival, and related mechanisms in lung cancer, using the A549 cell line and lung cancer gene expression datasets. AGTPBP1 knockdown increased the proliferation, migration, sphere formation, and drug resistance of A549 cells. Lung cancer datasets revealed significantly lower mRNA and protein expression levels of AGTPBP1 in lung cancer tissues, as compared to those in normal tissues. Importantly, AGTPBP1 expression positively correlated with patient survival. Analysis of co-expressed genes revealed that AGTPBP1 expression positively correlated with immune infiltration in lung cancer. Our results conclusively suggested that AGTPBP1 expression was correlated with cancer progression and immune infiltration in lung cancer.

Malic enzyme 1 (ME1) in the biology of cancer: it is not just intermediary metabolism

Malic enzyme 1 (ME1) is a cytosolic protein that catalyzes the conversion of malate to pyruvate while concomitantly generating NADPH from NADP. Early studies identified ME1 as a mediator of intermediary metabolism primarily through its participatory roles in lipid and cholesterol biosynthesis. ME1 was one of the first identified insulin-regulated genes in liver and adipose and is a transcriptional target of thyroxine. Multiple studies have since documented that ME1 is pro-oncogenic in numerous epithelial cancers. In tumor cells, the reduction of ME1 gene expression or the inhibition of its activity resulted in decreases in proliferation, epithelial-to-mesenchymal transition and in vitro migration, and conversely, in promotion of oxidative stress, apoptosis and/or cellular senescence. Here, we integrate recent findings to highlight ME1's role in oncogenesis, provide a rationale for its nexus with metabolic syndrome and diabetes, and raise the prospects of targeting the cytosolic NADPH network to improve therapeutic approaches against multiple cancers.

Emerging roles of IL-34 in health and disease

Macrophages are part of the innate immune system and are present in every organ of the body. They fulfill critical roles in tissue homeostasis and development and are involved in various pathologies. An essential factor for the development, homeostasis, and function of mononuclear phagocytes is the colony stimulating factor-1 receptor (CSF-1R), which has two known ligands: CSF-1 and interleukin-34 (IL-34). While CSF-1 has been extensively studied, the biology and functions of IL-34 are only now beginning to be uncovered. In this review, we discuss recent advances of IL-34 biology in health and disease with a specific focus on mononuclear phagocytes.

Evaluation of immune infiltrating of thyroid cancer based on the intrinsic correlation between pair-wise immune genes

INTRODUCTION

Unlike most mutation-driven cancers, thyroid cancer is thought to be highly dependent on changes in human hormone levels. It has become research hotspot using the change of gene expression level as a detection and diagnostic marker. The internal relationship between two genes and disease development is used to avoid the instability caused by single gene fluctuation. Aim It is possible to achieve early diagnosis in thyroid cancer during tumorigenesis and recurrence using IGPS (immune gene pairs).

METHODS

We extracted thyroid cancer data from The Cancer Genome Atlas (TCGA), using CIBERSORT algorithm to infiltrate out 22 immune cells types. We screened out IGPS that differ significantly between different groups, then used LinearSVC model to learn and screen features, combined with deep learning neural network model to predict benign and malignant cancer as well as patients at different groups.

KEY FINDINGS

There are significant differences of immune cell ratio in tumor stages and relapse samples. We screen out 42 and 64 IGPS for in normal-tumor and non-relapsed groups respectively, for example ASCC3-MAP3K7 and ATF2-SOCS5, have significant correlation in IGPS expression. Then we use the IGPS to train the tumor diagnostic classifier, obtain average AUC are both 0.99 after ten times cross-validation.

SIGNIFICANCE

The IGPS gives us new insight to explore immune cell infiltration of thyroid cancer, deep learning model can be further used in early diagnosis of thyroid cancer and estimation of the risk of recurrence.

Comprehensive Analysis of the Expression, Relationship to Immune Infiltration and Prognosis of TIM-1 in Cancer

TIM-1 is a critical gene that regulates T-helper cell development. However, little research has revealed the distribution, prognosis, and immune infiltration of TIM-1 in cancers. TCGA, GEO, Oncomine, TIMER, Kaplan-Meier, PrognoScan, GEPIA, TISIDB, and HPA databases were used to analyze TIM-1 in cancers. High TIM-1 expression was observed in bladder, cholangio, head and neck, colorectal, gastric, kidney, liver, lung adenocarcinoma, skin, uterine corpus endometrial, and pancreatic cancers compared to the normal tissues, and immunofluorescence shows that TIM-1 is mainly localized in vesicles. Simultaneously, high TIM-1 expression was closely related with poorer overall survival in gastric, lung adenocarcinoma, and poorer disease-specific survival in gastric cancer in the TCGA cohort, and was validated in the GEO cohort. Moreover, high expression of TIM-1, correlated with clinical relevance of gastric cancer and lung adenocarcinoma, was associated with tumor-infiltrating lymphocytes in lung adenocarcinoma and gastric cancer. Finally, immunohistochemistry showed TIM-1 expression was higher in lung adenocarcinoma and gastric cancer compared to the normal tissues. In summary, we applied integrated bioinformatics approaches to suggest that TIM-1 can be used as a prognostic biomarker in gastric and lung adenocarcinoma, which might provide a novel direction to explore the pathogenesis of gastric and lung adenocarcinoma.