Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' prognoses. Instead of single-omics data, multi-omics data can improve the clustering performance because it obtains a comprehensive landscape for understanding biological systems and mechanisms. However, heterogeneous data from multiple sources raises high complexity and different kinds of noise, which are detrimental to the extraction of clustering information. We propose an end-to-end deep learning based method, called Multi-omics Clustering Variational Autoencoders (MCluster-VAEs), that can extract cluster-friendly representations on multi-omics data. First, a unified network architecture with an attention mechanism was developed for accurately modeling multi-omics data. Then, using a novel objective function built from the Variational Bayes technique, the model was trained to effectively obtain the posterior estimation of the clustering assignments. Compared with 12 other state-of-the-art multi-omics clustering methods, MCluster-VAEs achieved an outstanding performance on benchmark datasets from the TCGA database. On the Pan Cancer dataset, MCluster-VAEs achieved an adjusted Rand index of approximately 0.78 for cancer category recognition, an increase of more than 18% compared with other methods. Furthermore, a survival analysis and clinical parameter enrichment tests conducted on 10 cancer datasets demonstrated that MCluster-VAEs provides comparable and even better results than many common integrative approaches. These results demonstrate that MCluster-VAEs are a powerful new tool for dissecting complex multi-omics relationships and providing new insights for cancer subtype discovery.

Sarcopenic obesity and therapeutic outcomes in gastrointestinal surgical oncology: A meta-analysis

Background

Sarcopenic obesity (SO) has been indicated as a scientific and clinical priority in oncology. This meta-analysis aimed to investigate the impacts of preoperative SO on therapeutic outcomes in gastrointestinal surgical oncology.

Methods

We searched the PubMed, EMBASE, and Cochrane Library databases through March 4th 2022 to identify cohort studies. Endpoints included postoperative complications and survival outcomes. Newcastle Ottawa Scale was used for quality assessment. Heterogeneity and publication bias were assessed. Subgroup analyses and sensitivity analyses were performed.

Results

Twenty-six studies (8,729 participants) with moderate to good quality were included. The pooled average age was 65.6 [95% confidence interval (CI) 63.7–67.6] years. The significant heterogeneity in SO definition and diagnosis among studies was observed. Patients with SO showed increased incidences of total complications (odds ratio 1.30, 95% CI: 1.03–1.64, P = 0.030) and major complications (Clavien-Dindo grade ≥ IIIa, odds ratio 2.15, 95% CI: 1.39–3.32, P = 0.001). SO was particularly associated with the incidence of cardiac complications, leak complications, and organ/space infection. SO was also predictive of poor overall survival (hazard ratio 1.73, 95% CI: 1.46–2.06, P < 0.001) and disease-free survival (hazard ratio 1.41, 95% CI: 1.20–1.66, P < 0.001). SO defined as sarcopenia in combination with obesity showed greater association with adverse outcomes than that defined as an increased ratio of fat mass to muscle mass. A low prevalence rate of SO (< 10%) was associated with increased significance for adverse outcomes compared to the high prevalence rate of SO (> 20%).

Conclusion

The SO was associated with increased complications and poor survival in gastrointestinal surgical oncology. Interventions aiming at SO have potentials to promote surgery benefits for patients with gastrointestinal cancers. The heterogeneity in SO definition and diagnosis among studies should be considered when interpreting these findings.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=255286], identifier [CRD42021255286].

Lymphovascular invasion: A non-sized T descriptor for stage IA non-small cell lung cancer

Background

Lymphovascular invasion (LVI) has not been included in the tumor‐node‐metastasis (TNM) staging manual of non‐small‐cell lung cancer (NSCLC). We aimed to investigate the predictive value of LVI on stage IA NSCLC and proposed a method of incorporating LVI into the T category based on the latest TNM staging manual.

Methods

The least absolute shrinkage and selection operator (LASSO)‐penalized Cox multivariable regression model was performed to identify prognostic factors. The Kaplan–Meier method was used to compare overall survival (OS) and disease‐free survival (DFS) between groups. Propensity score matching (PSM) was used to minimize bias.

Results

A total of 1452 eligible stage I NSCLC cases (stage IA without LVI, 1022 cases; stage IA with LVI, 120 cases; stage IB, 310 cases) were included. LASSO‐penalized multivariable Cox analysis revealed that LVI was an independent prognostic factor for both OS and DFS. Survival analysis demonstrated that the survivals of stage IA NSCLCs without LVI were better than those of stage IA with LVI and stage IB NSCLCs. In the matched cohort, the survivals of stage IA NSCLCs with LVI were comparable to those of stage IB NSCLCs.

Conclusions

Stage IA NSCLCs with LVI and stage IB NSCLCs had similar survivals, and we proposed that LVI might be a non‐sized T descriptor that upstaged stage IA diseases to stage IB.

Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study

BACKGROUND

Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing.

METHODS

The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs.

FINDINGS

In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%.

INTERPRETATION

Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs.

FUNDING

National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

Integrative Analyses of Circulating mRNA and lncRNA Expression Profile in Plasma of Lung Cancer Patients

Circulating-free RNAs (cfRNAs) have been regarded as potential biomarkers for “liquid biopsy” in cancers. However, the circulating messenger RNA (mRNA) and long noncoding RNA (lncRNA) profiles of lung cancer have not been fully characterized. In this study, we profiled circulating mRNA and lncRNA profiles of 16 lung cancer patients and 4 patients with benign pulmonary nodules. Compared with benign pulmonary nodules, 806 mRNAs and 1,762 lncRNAs were differentially expressed in plasma of lung adenocarcinoma patients. For lung squamous cell carcinomas, 256 mRNAs and 946 lncRNAs were differentially expressed. A total of 231 mRNAs and 298 lncRNAs were differentially expressed in small cell lung cancer. Eleven mRNAs, 51 lncRNAs, and 207 canonical pathways were differentially expressed in lung cancer in total. Forty-five blood samples were collected to verify our findings via performing qPCR. There are plenty of meaningful mRNAs and lncRNAs that were found. MYC, a transcription regulator associated with the stemness of cancer cells, is overexpressed in lung adenocarcinoma. Transforming growth factor beta (TGFB1), which plays pleiotropic roles in cancer progression, was found to be upregulated in lung squamous carcinoma. MALAT1, a well-known oncogenic lncRNA, was also found to be upregulated in lung squamous carcinoma. Thus, this study provided a systematic resource of mRNA and lncRNA expression profiles in lung cancer plasma.

Collapse

Assessment of Breathomics Testing Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Esophageal Cancer

IMPORTANCE

A triage test is needed to increase the detection rate for esophageal cancer.

OBJECTIVE

To investigate whether breathomics can detect esophageal cancer among patients without a previous diagnosis of cancer using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included participants who planned to receive an upper endoscopy or surgery of the esophagus at a single center in China. Exhaled breath was collected with a self-designed collector and air bags before participants underwent these procedures. Sample collection and analyses were performed by trained researchers following a standardized protocol. Participants were randomly divided into a discovery data set and a validation data set. Data were collected from December 2020 to March 2021.

EXPOSURES

Breath samples were analyzed by HPPI-TOFMS, and the support vector machine algorithm was used to construct a detection model.

MAIN OUTCOMES AND MEASURES

The accuracy of breathomics was measured by the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve.

RESULTS

Exhaled breath samples were obtained from 675 patients (216 [32%] with esophageal cancer; 459 [68%] with noncancer diseases). Of all patients, 206 (31%) were women, and the mean (SD) age was 64.0 (11.9) years. In the validation data set, esophageal cancer was detected with an accuracy of 93.33%, sensitivity of 97.83%, specificity of 83.72%, positive predictive value of 94.74%, negative predictive value of 92.78%, and area under the receiver operating characteristic curve of 0.89. Notably, for 16 patients with high-grade intraepithelial neoplasia, 12 (75%) were predicted to have esophageal cancer.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, testing breathomics using HPPI-TOFMS was feasible for esophageal cancer detection and totally noninvasive, which could help to improve the diagnosis of esophageal cancer.

Circulating microbiome DNA: An emerging paradigm for cancer liquid biopsy

Dysbiosis of the human microbiome has long been reported to be closely associated with various cancers. Accumulating studies have shown that microbial dysbiosis can accelerate tumorigenesis through tumor-promoting inflammation, DNA damage, and inducing immune evasion. Differential composition of microbiome could be novel biomarkers for cancer detection or biomarkers of successful immunotherapy. More importantly, emerging evidence demonstrates that alterations of circulating microbiome DNA (cmDNA) could serve as promising noninvasive biomarkers for cancer detection. It has been reported that distinct circulating bacterial DNA could distinguish prostate cancer, lung cancer, and melanoma patients from healthy populations. Therefore, in this review, we summarized current literature on microbial biomarkers for cancer detection and unraveled the potential of cmDNA as a promising cancer detection tool.

Distinct tumor bacterial microbiome in lung adenocarcinomas manifested as radiological subsolid nodules

•

Subsolid nodules have higher microbiome diversity compared with solid nodules

•

Microbiome composition of Subsolid nodules is distinct from that of solid nodules

•

Microbial signatures show robust performance to predict lung adenocarcinoma or subsolid nodules

•

Some lung microbial species are associated with clinical characteristics

Objectives

Increasing evidence indicates that microbiota dysbiosis in the human body may play vital roles in carcinogenesis. However, the relationship between microbiome and lung cancer remains unclear. In this study, we aimed to characterize the microbiome in early stage of lung adenocarcinoma (LUAD), which presented as subsolid nodules (SSN) or solid nodules (SN).

Materials and Methods

We performed 16S rRNA sequencing of 35 pairs (10 SSN and 25 SN) of LUAD tumor tissues and paired adjacent normal tissues. Machine learning was used to identify microbial signatures and construct predictive models.

Results

SSN has higher microbiome richness and diversity compared with SN (richness p = 0.017, Shannon index p = 0.17), and the microbiome composition of SSN is distinct from that of SN (Bray-Curtis p = 0.013, unweighted unifrac p = 0.001). Phylum Chloroflexi (p = 0.009), Gemmatimonadetes (p = 0.018) and genus including Cloacibacterium (p = 0.003), Subdoligranulum (p = 0.002), and Mycobacterium (p = 0.034) were significantly increased in SSN. Tumor and normal tissues had similar richness and diversity, as well as overall microbiome composition. Probiotics with anti-cancer potential, like Lactobacillus, showed elevated levels in normal tissues (p = 0.018). A random forest model with 20 genera-based biomarkers achieved high accuracy for LUAD prediction (area under curve, AUC = 0.879). Meanwhile, a five genera-based signature can accurately discriminate SSN between SN (AUC = 0.950). Cross-validation of these two models also showed high predictive performance (LUAD AUC = 0.813, SSN AUC = 0.933).

Conclusions

This study demonstrates, for the first time, the tumor bacterial microbiome composition of LUAD manifested as SSN is distinct from that presented as SN, which adds new knowledge to SSN in the perspective of microbiome. Furthermore, microbiome signatures showed good performance to predict LUAD or SSN.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer

Importance

Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer.

Objective

To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS).

Design, Setting, and Participants

This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020.

Exposures

Breath testing and SVM algorithm.

Main Outcomes and Measures

The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC).

Results

Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586.

Conclusions and Relevance

This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

Decoding the multicellular ecosystem of lung adenocarcinoma manifested as pulmonary subsolid nodules by single-cell RNA sequencing

Lung adenocarcinomas (LUAD) that radiologically display as subsolid nodules (SSNs) exhibit more indolent biological behavior than solid LUAD. The transcriptomic features and tumor microenvironment (TME) of SSN remain poorly understood. Here, we performed single-cell RNA sequencing analyses of 16 SSN samples, 6 adjacent normal lung tissues (nLung), and 9 primary LUAD with lymph node metastasis (mLUAD). Approximately 0.6 billion unique transcripts were obtained from 118,293 cells. We found that cytotoxic natural killer/T cells were dominant in the TME of SSN, and malignant cells in SSN undergo a strong metabolic reprogram and immune stress. In SSN, the subtype composition of endothelial cells was similar to that in mLUAD, while the subtype distribution of fibroblasts was more like that in nLung. Our study provides single-cell transcriptomic profiling of SSN and their TME. This resource provides deeper insight into the indolent nature of SSN and will be helpful in advancing lung cancer immunotherapy.

A novel circular RNA, circXPO1, promotes lung adenocarcinoma progression by interacting with IGF2BP1

Studies have demonstrated that noncoding RNAs play important roles in various types of cancer; however, noncoding RNAs derived from regions of genomic alterations have rarely been explored, especially for circular RNAs (circRNA). Previously, we found several circRNAs were upregulated in lung adenocarcinoma (LUAD) tumor tissues by RNA sequencing. Here, we characterized a novel circRNA, circXPO1, in LUAD, which is derived from a well-established cancer therapeutic target, XPO1. circXPO1, is formed by back-splicing of exon 3 and exon 4 of XPO1 gene. circXPO1 was highly expressed in LUAD tissues compared with paired adjacent non-tumor tissues, and high circXPO1 expression correlated with worse overall survival. circXPO1 expression was positively correlated with the XPO1 gene copy number. Mechanically, circXPO1 could bind with IGF2BP1 and enhance CTNNB1 mRNA stability, and subsequently promote LUAD progression. In a LUAD patient-derived xenograft model, intratumoural injection of cholesterol-conjugated siRNA specifically targeting circXPO1 efficiently suppressed tumor growth. To summary, these results suggest that circXPO1 is critical for LUAD progression and may serve as a biomarker for poor prognosis and a therapeutic target. On the other hand, the functional roles of noncoding transcripts derived from coding genes should be re-evaluated.

Characterization of gene expression profiles of esophageal cancer patients with different nonsynonymous tumor mutation burden

Background

Nonsynonymous tumor mutation burden (NSTMB) could affect the prognosis of esophageal cancer (EC) patients, but differentially expressed genes between EC patients with different NSTMB have not been explored. Our study aimed to compare differentially expressed genes between EC patients with different NSTMB (high vs. low).

Methods

RNA‐seq data for EC patients were downloaded from The Cancer Genome Atlas (TCGA). The edgeR package was used to identify differentially expressed genes between patients with different NSTMB. Cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) software was employed to underscore immune cell differences between patients with different NSTMB.

Results

In total, we discovered 2215 differentially expressed genes between patients with different NSTMB, among which 842 genes were upregulated and 1373 downregulated in patients with high NSTMB. The differentially expressed genes were enriched in pathways such as heme binding and structural molecule activity. We built a logistic model that may be used to predict patients' NSTMB. We found that tumors with high NSTMB had a significantly higher percentage of resting natural killer (NK) cells than those with low NSTMB (P = 0.028). The percentages of regulatory T (Treg) and CD8⁺ T cells were also higher in those with high NSTMB, although it was not statistically significant (P = 0.064 for Treg cells and P = 0.12 for CD8⁺ T cells).

Conclusions

NSTMB may cause changes in gene expression and immune cell infiltration in EC patients, and affect the overall survival of EC patients.

Key points

Significant findings of the study

This study found differentially expressed genes and differences in infiltration of immune cells between esophageal cancer (EC) with different NSTMB.

What this study adds

This study highlights differences between EC patients with different NSTMB.

Long Noncoding RNA SBF2-AS1 Is Critical for Tumorigenesis of Early-Stage Lung Adenocarcinoma

Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) are deeply involved in the development of various cancers. This study identified that SBF2-AS1, an early-stage-specific lncRNA, is critical for the tumorigenesis of lung adenocarcinoma (LUAD). We first analyzed LUAD transcriptome data from The Cancer Genome Atlas and the GEO database by weighted gene co-expression network analysis (WGCNA). Five early LUAD-specific lncRNAs were filtered out, and only SBF2-AS1 was upregulated in LUAD. High expression of SBF2-AS1 indicates poor survival of LUAD, especially the early-stage LUAD, but not lung squamous cell carcinoma. SBF2-AS1 promotes LUAD cells proliferation in vitro, and RNA-sequencing data shows that many cell-cycle-related genes were downregulated after SBF2-AS1 knockdown. Mechanically, SBF2-AS1 could competitively bind with miR-338-3p and miR-362-3p to increase E2F1 expression. Finally, we show that the SBF2-AS1-miR-338-3p/362-3p-E2F1 axis could promote LUAD tumorigenesis in vitro and in vivo. Our study demonstrates that SBF2-AS1, an early-stage-specific lncRNA, promotes LUAD tumorigenesis by sponging miR-338-3p and miR-362-3p and increasing E2F1 expression. The SBF2-AS1-miR-338-3p/362-3p-E2F1 regulatory axis may serve as a prognostic marker and potential therapeutic target for LUAD.

Profiling expression of coding genes, long noncoding RNA, and circular RNA in lung adenocarcinoma by ribosomal RNA-depleted RNA sequencing

Noncoding RNA play important roles in various biological processes and diseases, including cancer. The expression profile of circular RNA (circRNA) has not been systematically investigated in lung adenocarcinoma (LUAD). In this study, we performed genomewide transcriptome profiling of coding genes, long noncoding RNA (lncRNA), and circRNA in paired LUAD and nontumor tissues by ribosomal RNA‐depleted RNA sequencing. The detected reads were first mapped to the human genome to analyze expression of coding genes and lncRNA, while the unmapped reads were subjected to a circRNA prediction algorithm to identify circRNA candidates. We identified 1282 differentially expressed coding genes in LUAD. Expression of 19 023 lncRNA was detected, of which 244 lncRNAs were differentially expressed in LUAD. AFAP1‐AS1, BLACAT1, LOC101928245, and FENDRR were most differentially expressed lncRNAs in LUAD. Also identified were 9340 circRNA candidates with ≥ 2 backspliced, including 3590 novel circRNA transcripts. The median length of circRNA was ~ 530 nt. CircRNA are often of low abundance, and more than half of circRNAs we identified had < 10 reads. Agarose electrophoresis and Sanger sequencing were used to confirm that four candidate circRNA were truly circular. Our results characterized the expression profile of coding genes, lncRNA, and circRNA in LUAD; 9340 circRNAs were detected, demonstrating that circRNA are widely expressed in LUAD.

Database

The raw RNA sequencing data have been submitted to Gene Expression Omnibus (GEO) database and can be accessed with the ID GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104854.