Lung cancer (LC) continues to be the leading cause of cancer-related deaths despite declining smoking prevalence (Bray et al., 2018; Wild et al., 2020). Non-small-cell (NSCLC) and small-cell (SCLC) are the two major subtypes of LC. The symptoms generally occur at a late stage and the prognosis is poor. Stage at diagnosis typically determines patient survival (Aberle et al., 2011; Bach et al., 2012; Brustugun et al., 2018). Screening with low-dose computed tomography (LDCT) can be effective for early detection (Bach et al., 2012; Peled and Ilouze, 2015) and reduce LC mortality up to 20% in high-risk groups (de Koning et al., 2020; Hanash et al., 2018; Seijo et al., 2019). However, LDCT has limitations such as high false-positive rates, risk of overdiagnosis, and high costs (Gopal et al., 2010; Peled and Ilouze, 2015). Annual CT scans also cause harmful radiation exposure (Bach et al., 2012; Hanash et al., 2018). Robust biomarkers can help stratify high-risk groups and increase accuracy in patient inclusion criteria for LDCT-based screening programs (Hanash et al., 2018).

Liquid biopsies quantifying molecular biomarkers in circulation, such as tumor-derived DNAs, proteins, and RNAs, can be used to detect cancer (Hanash et al., 2018; Ko et al., 2018; Sandfeld-Paulsen et al., 2016). MicroRNAs (miRNA), a class of ~21 nucleotide long short RNAs, have been widely investigated for their biomarker potential (Fehlmann et al., 2020; Keller and Meese, 2016; Pichler and Calin, 2015; Tian et al., 2019). They can be found both in serum (Keller and Meese, 2016; Murillo et al., 2019; Umu et al., 2018) and in plasma (Freedman et al., 2016; Keller and Meese, 2016; Murillo et al., 2019) as cell-free circulating RNAs, which may originate from dying cells or be actively secreted (Zaporozhchenko et al., 2018). Some of them are bounded by proteins or confined in layered exosomes which can protect them from degradation (Fritz et al., 2016). MiRNAs can function as tumor suppressors or oncomiRs and regulate tumor traits such as cell growth, angiogenesis, immune evasion, and metastasis (Pichler and Calin, 2015; Svoronos et al., 2016). The search for RNA biomarkers is not limited to miRNAs. Aberrant expression of other RNA classes, such as protein coding mRNAs, tRNAs, piwi-interacting RNAs (piRNAs), and long-noncoding RNAs (lncRNAs), has been associated with cancer (Kim et al., 2017; Slack and Chinnaiyan, 2019). Despite the immense potential of cell-free RNAs, the promise of non-invasive RNA biomarkers of cancer has not yet been fulfilled.

One explanation of the lack of circulating RNAs used in clinical settings is our limited understanding of the prediagnostic dynamics of cell-free RNAs, since studies are usually based on samples at or after diagnosis. Carcinogenesis is a multistep process that turns cell functions from normal to malignant (Hanahan and Weinberg, 2000). It can cause temporal changes in RNA levels linked to cellular processes driven by the hallmarks of cancer (Gutschner and Diederichs, 2012; Hanahan and Weinberg, 2000). We have shown that prediagnostic RNA levels in serum are highly dynamic in LC patients, which may signal early carcinogenesis (Umu et al., 2020). A similar result was observed in breast cancer (Lund et al., 2016) and testicular cancer patients (Burton et al., 2020). A lack of reproducibility among studies is also a problem, caused by technical and biological factors such as storage time, sampling procedure, age, sex, smoking history, etc. (Rounge et al., 2018). It is therefore important to control for these factors.

In the present study, our objective was to identify serum RNA-based biomarkers for early diagnosis of LC using prediagnostic samples. We identified the optimal machine learning (ML) algorithm for RNA biomarker modeling. Optimization of prediction models was done with an ML workflow, including cross-validation and testing, which was repeated five times to increase the generalizability of our results. We also investigated the biological relevance of the best RNA separators in the context of cancer biomarkers.

In this study, we showed that ML models of prediagnostic serum RNA levels can be used to predict LC years before diagnosis and manifestation of disease symptoms. Our models achieved clinically relevant performance in terms of AUC, accuracy, sensitivity, and specificity (Tables 2 and 3). The model performance was further increased for specific prediagnostic time windows and histologies making it feasible to develop them as biomarkers for LC screening (Figure 3). A collection of the best models (and predictors) (Table 3 and Supplementary file 4) can predict risk for developing LC, which histologies to look for and indicate the level of cancer progression. The time window of the high-performance models may be a first indication of how often to screen for LC (Figure 4). Our study is unique in including serum samples collected up to 10 years prior to LC diagnosis and a large set of control samples.

Figure 4

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Suggested clinical uses of RNA biomarkers in lung cancer (LC) screening.

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We previously reported that prediagnostic circulating RNA signals are highly dynamic in LC patients and they can be histology and stage dependent (Umu et al., 2020). In the present study, ML models using all samples regardless of stage, histology, or prediagnostic time successfully separated LC patients from controls. All the tested algorithms consistently produced acceptable AUC values (Figure 2A). The best algorithm, XGBoost, resulted in an average accuracy of 69% without feature selection. An analysis of the features showed a large panel of selected RNAs: more than 300 out of available 3306 (with no feature selection implemented). This may be interpreted as a general shift in the levels of RNAs during cancer development, consistent with our previous study that showed hundreds of RNAs were differentially expressed up to 10 years before diagnosis (Umu et al., 2020).

We found that some features were considerably more important (and frequent) separators than others with or without feature selection. The list includes piRNAs (e.g. piR-hsa-28723), miRNAs (e.g. hsa-miR-574–5p, hsa-miR-30a-5p, hsa-miR-106b-5p), isomiRs (e.g. isomiR of hsa-miR-423–5p (iso-20-5KP25HFF), hsa-miR-486–5p (iso-23-8YUYFYKSY)), and miscRNAs (e.g. RN7SL181P). Some of them were particularly interesting since they were associated with cancer or proposed as cancer biomarkers. Hsa-miR-30a-5p is a tumor suppressor and downregulated in LC tissues (Yanaihara et al., 2006). It regulates oncogenes such as RAB38 and RAB27B (The RNAcentral Consortium, 2019). Another notable example is hsa-miR-574–5p which promotes metastasis in NSCLC by targeting PCP2 in tumor tissues (Zhou et al., 2016) and has been proposed as an early stage NSCLC serum biomarker (Foss et al., 2011). Hsa-miR-574–5p was among the most important features in lasso-selected and miRNA-only histology-specific SCLC models. It was also one of the most important features in our histology-specific NSCLC models. There were also isomiRs among the most important features such as hsa-miR-486–5p canonical form, which was the best separator for all histologies. Hsa-miR-486–5p targets PIK3R1 to suppress cell growth. Its overexpression inhibits cell proliferation and invasion and it was significantly downregulated in both tissue and serum (Tian et al., 2019). Hsa-miR-486–5p was proposed as a diagnostic and prognostic biomarker for NSCLC (ElKhouly et al., 2020; Tian et al., 2019).

Besides miRNAs and isomiRs, RNAs of other classes were noteworthy and linked to carcinogenesis. For example, 7SL, a member of miscRNAs, is upregulated in tumor cells. It binds to TP53 mRNA at the 3'UTR region and downregulates its expression (Abdelmohsen et al., 2014). 7SL-related transcripts (e.g. RN7SL181P) were among the most important separators in the cell histology, NSCLC- and SCLC-specific models. Another example is Y-RNA and we found that Y-RNA and related genes (e.g. RNY4P30) were among the most important features for NSCLC models. Y-RNA was also chosen as an important feature by the lasso-selected NSCLC models. Y-RNA-derived small RNAs function as tumor suppressors in NSCLC. They inhibit cell proliferation and were proposed as circulating RNA biomarkers since they were upregulated in NSCLC EVs (Li et al., 2018).

Inclusion of both prediagnostic time and histology produced better models in certain time windows (e.g. 2–5 years before diagnosis for SCLC) (Figure 3). This can be explained by the dynamic nature of prediagnostic RNA levels (Lund et al., 2016; Umu et al., 2020). Important features of these models can also be linked to early carcinogenesis and some were specific to these models. For example, hsa-miR-339–3p was among the most important features of SCLC prediagnostic models. Hsa-miR-339–3p is a tumor suppressor and was proposed as a serum biomarker of LC (Yu et al., 2019). We retrained some of these prediagnostic models using the most frequent features and achieved higher prediction performance than the full-time models in specific time intervals. We reported these models in supplementary (Supplementary file 3).

The most important features of histology-specific models also showed associations with carcinogenesis-related KEGG pathways, which were common or specific to histology. The common ones include well-known signaling (e.g. RAS, PI3K-Akt, MAPK, ErbB) and cancer-related pathways (e.g. proteoglycans in cancer and pathways in cancer). Choline metabolism in cancer pathway was one of the common ones and enriched in some histology-specific prediagnostic models. Altered choline profiles are characteristics of tumor tissues (Glunde et al., 2006). Moreover, a lipidome serum profiling study on early stage NSCLC patients proposed choline-containing phospholipids as potential LC biomarkers (Klupczynska et al., 2019). Enrichment of choline metabolism pathway years before diagnosis (i.e. NSCLC 6–8 and SCLC 2–5) supports this conclusion. We also reported enrichment of this pathway for all histologies before diagnosis in our previous study (Umu et al., 2020).

A strength of our study is the large sample size from prediagnostic cases and a large control group from cancer-free individuals from the same cohort. We have detailed information on histological subtype and stage at diagnosis from the Cancer Registry of Norway (CRN) and smoking history from survey data. We also accounted for other potential confounders (i.e. age, sex, and blood donor group [BDg]) (Rounge et al., 2018). Some of our potential biomarkers (e.g. hsa-miR-30a-5p, sa-miR-339–3p, 7SL) were already associated with carcinogenesis or proposed as biomarkers, which shows consistent results with current literature. Further, we found potential biomarkers from overlooked RNA classes which add important new knowledge into the field. We shared the average feature importance of all RNAs as supplementary tables (Supplementary file 2). We investigated performance of different algorithms which showed consistent results in terms of AUCs and features. We compiled shortlists from the most important features and tested their performance in a leave-out dataset on both smokers and non-smokers. We also found that smokers with a positive test had more than two times higher risk of getting LC diagnosis in future (Table 3).

There are some weaknesses in our study that we need to address. First, an independent cohort should replicate our results. However, only a few cohorts include prediagnostic samples that can be used for discovery and validation. We tried to overcome this issue by using training-testing repeats for assessing generalisability. We also reported our results with and without feature selection since some feature selection methods (e.g. lasso and univariate) can cause overfitting. Second, using more than one sample from the same individual can potentially cause overfitting. However, we did not detect any effect related to this issue (Figure 2—figure supplement 2). Third, our study focused only on smokers (since case samples are mostly smokers). However, our results show acceptable performance when including non-smokers as a test dataset as well. Fourth, reuse of the same data for frequent biomarker models (as reported in Table 3) can also result in overfitting. We tried to overcome this issue by including a leave-out dataset (which was never used) into the test set and reported performance. Lastly, since our samples are long-term stored, some unstable RNA molecules may have been degraded over the years, though we have already shown that this effect is negligible (Umu et al., 2018). Yet, we matched cases and controls for BDg which includes the effect of storage time (see Materials and methods).

In LC screening programs, RNA biomarkers can be used as a tool of initial assessment or combined with LDCT for early detection (Hanash et al., 2018). We found that smokers with a positive test had higher risk of getting LC diagnosis in future (Table 3). We also found that our biomarkers can be potentially used on non-smokers, especially SCLC biomarkers. However, we do not have enough non-smoker cases to further validate this interpretation. The dynamic nature of the prediagnostic signal for cancer may pose challenges for the performance of modeling and biomarker development. However, using a set of models specific for histology and time might provide additional information useful in evaluating LC risk (Figure 4). Our proposed use of RNA biomarkers starts with risk assessment using standard full-time models which can be used for an initial assessment in smokers when the disease is undetectable. A positive signal (i.e. high probability of being in LC group) classifies those individuals into an elevated risk group. Since prediagnostic models have a 2-year peak performance, every second-year testing with these models can provide confirmation of preneoplasia or an early stage tumor for individuals with elevated risk and selection criteria for CT monitoring. Prediagnostic models had higher overall specificity (more than 80%) which can help to determine future diagnosis histology. However, it requires further research. We selected two sets of histology-specific diagnostic models for early/late NSCLC and SCLC diagnosis and reported these in the supplementary document (Supplementary file 4). RNA biomarkers can prevent unnecessary use of LDCT while improving the chance of an early diagnosis of LC in an early stage. This hypothesis can be investigated in screening programs for validation.

The datasets generated for this manuscript are not readily available because of the principles and conditions set out in articles 6 (1) (e) and 9 (2) (j) of the General Data Protection Regulation (GDPR). National legal basis as per the Regulations on population-based health surveys and ethical approval from the Norwegian Regional Committee for Medical and Health Research Ethics (REC) is also required. Requests to access the datasets should be directed to the corresponding authors with a project proposal. Please refer to our project website for the latest information on data sharing (kreftregisteret.no/en/janusrna). Our scripts, plot data, and bioinformatics workflow files can be accessed from our Github repo (https://github.com/sinanugur/LCscripts copy archived at swh:1:rev:26bccc86a551f71284559db11bb74230f5d00cc4).

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Author details

1. Sinan U Umu

   Department of Research, Cancer Registry of Norway, Oslo, Norway

   Contribution

   Formal analysis, Investigation, Methodology, Software, Writing – original draft

   For correspondence

   sinan.ugur.umu@kreftregisteret.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8081-7819

2. Hilde Langseth

   1. Department of Research, Cancer Registry of Norway, Oslo, Norway
   2. Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Verena Zuber

   Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Åslaug Helland

   1. Department of Oncology, Oslo University Hospital, Oslo, Norway
   2. Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
   3. Institute of Clinical Medicine, University of Oslo, Oslo, Norway

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

5. Robert Lyle

   1. Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
   2. Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

6. Trine B Rounge

   1. Department of Research, Cancer Registry of Norway, Oslo, Norway
   2. Department of Informatics, University of Oslo, Oslo, Norway

   Contribution

   Conceptualization, Funding acquisition, Project administration, Writing – original draft, Writing - review and editing

   For correspondence

   trine.rounge@kreftregisteret.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2677-2722

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