Qualitative serum microRNA signatures for lung cancer screening

doi:10.21203/rs.3.rs-5264527/v1

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Qualitative serum microRNA signatures for lung cancer screening

https://doi.org/10.21203/rs.3.rs-5264527/v1

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Background: Lung cancer is the leading cause of morbidity and mortality among cancers worldwide. The early detection of lung cancer can effectively reduce the mortality rates among patients. Therefore, this study aims to construct signatures for the screening of lung cancer.

Methods: The serum miRNA expression profiles of 5078 non-cancer, 1951 lung cancer and 3504 other cancer samples from four datasets were used. Subsequently, the samples were classified into one training dataset and two validation datasets.

Results: In this study, we firstly demonstrate the differential expression pattern of serum miRNAs between lung cancer and non-cancer in four datasets, respectively. Subsequently, two qualitative serum microRNA signatures were established. The first signature, designated as LC-MPS2, was employed to distinguish lung cancer from non-cancer samples. The sensitivity and specificity of the signature were all over 99.0% in both the training and validation datasets. However, LC-MPS2 was unable to distinguish lung cancer from other types of cancer. Thus, an additional signature, comprising of six miRNA pairs designated as LC-MPS6, was constructed. This signature demonstrated 90.9% sensitivity and 91.1% specificity in the training datasets for discriminating lung cancer from multiple other types of cancers. In an independent validation dataset, LC-MPS6 achieved 80.2% sensitivity and 93.9% specificity.

Conclusions:This study demonstrates that the qualitative serum microRNA signatures can accurately identify lung cancer from multiple cancer and non-cancer samples.

Bioinformatics

serum microRNAs

multiple cancers

lung cancer

signature

qualitative

According to the "Global Cancer Statistics 2022", lung cancer was the most frequently diagnosed cancer and the first leading cause of cancer mortality worldwide. It was responsible for nearly 2.5 million new cases (12.4% of all cancers globally) and 1.8 million deaths (18.7%)[1]. If lung cancer is diagnosed at stage I, the 5-year survival rate can reach up to 80%. However, when diagnosed at stage IV, it drops to 6% [2]. Unfortunately, > 80% of lung cancer cases are detected at an advanced stage, which contributes to the high mortality rates[3]. Therefore, early diagnosis can bring great benefits to patients with lung cancer and is essential to reduce overall mortality.

Low-dose computed tomography (LDCT) has been demonstrated to be an efficacious screening method for high-risk populations of lung cancer[4, 5]. However, these trials only focused on high-risk populations, and the results may not be generalizable to other populations. Furthermore, studies have demonstrated that the sensitivity of LDCT for lung cancer detection is apporximately 61%, resulting in a high false positive rate. This may lead to the performance of unnecessary follow-up examinations and invasive surgery, which could cause pain and anxiety to patients[6, 7]. Liquid biopsy is a technique which get lesion information by detecting tumor cells or biomarkers in human blood or other body fluid samples. It has several advantages, including minimal invasiveness, rapid detection, convenient operation, high repeatability and real-time monitoring of tumor progression, thereby providing a novel option for early tumor diagnosis[8, 9]. However, the currently widely studied lung cancer tumor markers, such as CEA, NSE and SCC-Ag, have been observed to exhibit significant differences in their expression patterns across different subtypes of lung cancer[10].

Recently, several lung cancer diagnostic signature based on serum miRNAs have been established. However, these signatures do not consider the common characteristics of tumors, which makes it difficult to distinguish lung cancer from other non-lung cancer patients [11, 12]. Furthermore, these studies are mainly based on risk scores, which are usually calculated as summaries of expression levels of signature genes. However, the applications of these signatures require the preseting of risk score thresholds and data normalization. The risk scores of samples change significantly when they are normalized together with different samples[13]. Studies have demonstrated that within-sample relative expression orderings (REOs) of gene pairs are highly robust to batch effects, cancer cell proportion, and bias of gene values caused by partial RNA degradation and amplification[14]. In Comparison to signature based on risk scores, REOs-based signatures are more suitable for individual samples. And, this method has been widely employed to construct signatures for cancer diagnosis, prognosis, and drug response[15–18].

In this study, we aimed to construct a lung cancer early screening model based on serum miRNA expression profiles of lung cancer, non-cancer and non-lung cancer samples, combined with the REOs method.

Data sources and preprocessing

In this study, the serum miRNA expression profile data of 1951 lung cancer, 5078 non-cancer and 3504 non-lung cancer patients from four datasets were collected (Table 1).These datasets were named LC1591, LC972, LC4046 and LC3924 according to their sample size, respectively[11, 19–21]. The two datasets, LC1591 and LC972 were combined to form the training dataset, while LC4046 and LC3924 were used as the independent validation dataset.

Table 1

Samples used in this study.
Data set	data ID	GEO Accession	LC	NC	Others	Total sample
Training	LC1591	GSE112264	50	41	1500	1591
Training	LC972	GSE113486	40	100	832	972
Test	LC4046	GSE106817	115	2759	1172	4046
Test	LC3924	GSE137140	1746*	2178	-	3924
* Note: The dataset comprises 180 postoperative serum miRNA samples from lung cancer patients.

The Relative Expression Orderings Strategy

Within one sample, the relative expression orderings (REO) pattern of two miRNAs (miRNA i and miRNA j) is expressed as Mi > Mj (or Mj > Mi) if the expression of miRNA i is higher than that of miRNA j (or vice versa). All the REO patterns that were observed in more than 95% of lung cancer (or non-cancer) samples were selected as the stable REO patterns for lung cancer (or non-cancer). In datasets comprising both lung cancer and non-cancer samples, a miRNA pair (Mi > Mj) is defined as a stable reversed REO between lung cancer and non-cancer samples if the REO pattern of this miRNA pair is observed in more than 95% of lung cancer samples but reverses (Mi < Mj) in more than 95% of non-cancer samples. Similarly, in datasets including both lung cancer and other types of cancer samples, if the REO pattern of two miRNAs (Mi > Mj) is observed in more than 75% of other types of cancer samples but reverses (Mi < Mj) in more than 75% of lung cancer samples, this gene pair is defined as a stable reversed REO between lung cancer and other types of cancer samples.

Accuracy and F-score

In order to evaluate the predictive performance of miRNA pairs and signatures in identifying lung cancer from non-cancer samples, sensitivity, specificity and accuracy were used as criteria. For each miRNA pair (miRNA i and miRNA j) in sample t, the REO pattern $\:{R}_{ti}>{R}_{tj}$ was voted for lung cancer, while the other REO pattern $\:{R}_{ti}<{R}_{tj}$ was voted for non-cancer. A signature comprising a set of gene pairs was considered to indicate lung cancer in a sample if at least half of the gene pairs in the signature voted for lung cancer, and otherwise, as a non-cancer sample. Samples designated as lung cancer were classified as positive samples, while negative samples included those labeled as non-cancer. True positives (TP) represent the samples that were correctly identified as lung cancer. Similarly, TN, FP and FN denote the number of true negatives, false positives and false negatives, respectively. Sensitivity, specificity, accuracy and F-score are calculated as follows:

$$\:\text{S}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}=\frac{TP}{TP+FN}$$

$$\:\text{S}\text{p}\text{e}\text{c}\text{i}\text{f}\text{i}\text{c}\text{i}\text{t}\text{y}=\frac{TN}{TN+FP}$$

$$\:\text{A}\text{c}\text{c}\text{u}\text{r}\text{a}\text{c}\text{y}=\frac{TP+TN}{TP+FN+TN+FP}$$

$$\:F\text{-}\text{s}\text{c}\text{o}\text{r}\text{e}=\frac{2\text{*}\text{s}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}\text{*}\text{s}\text{p}\text{e}\text{c}\text{i}\text{f}\text{i}\text{c}\text{i}\text{t}\text{y}}{\text{s}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}+\text{s}\text{p}\text{e}\text{c}\text{i}\text{f}\text{i}\text{c}\text{i}\text{t}\text{y}}$$

In order to ascertain whether the accuracy of a miRNA pair was greater than that of a random guess (50%), a one-sided exact binomial test was conducted at the 1% significance level. The sensitivity, specificity, accuracy and F-score were evaluated in a similar manner when developing the signature to identify lung cancer from non-lung cancer samples of other types .

Uniform Manifold Approximation and Projection algorithm

The Uniform Manifold Approximation and Projection (UMAP) algorithm was employed to create a two-dimensional representation of the samples. This was performed in R using the package "umap".

Area under the curve

The area under the curve (AUC) was employed to assess the predictive efficacy of the signatures. This was performed in R using the package "pROC" [22].

The diverse expression patterns of serum miRNAs between lung cancer and non-cancer samples

In this study, we initially performed Uniform Manifold Approximation and Projection (UMAP) analysis on miRNA expression profiles of lung cancer and non-cancer samples in four datasets. Figure 1 illustrates that lung cancer and non-cancer samples are clearly segregated in distinct regions within the LC1591 and LC972 datasets. In LC4046 and LC3924, although some non-cancer samples were mixed with lung cancer samples, it was evident that the lung cancer and the majority of non-cancer samples were distributed in different regions. These findings indicate that there are significant differences in serum miRNA expression profiles between lung cancer and non-cancer samples. Consequently, it is feasible to develop signatures for the discrimination of lung cancer from non-cancer samples based on serum miRNAs.

The signature to discriminate lung cancer from non-cancer

As illustrated in Fig. 2, a total of 90 lung cancer and 141 non-cancer samples were extracted from the training dataset by pooling LC1591 and LC972. A total of 848,487 and 1,325,596 miRNA pairs with identical REOs patterns were identified in more than 95% of lung cancer and non-cancer samples, respectively. Subsequently, 466 miRNA pairs exhibiting reversal REOs between lung cancer and non-cancer samples were identified. A set of two miRNA pairs was obtained that achieved the highest F-score based on these 466 miRNA pairs (detail in methods). The two miRNA pairs (Table 2), along with their REO patterns, denoted as LC-MPS2, were selected as the signature for the diagnosis of lung cancer .

Table 2

The two miRNA pairs in LC-MPS2.
signatures	miRNA pairs	miRNA 1	miRNA 2
LC-MPS2	miRNA pairs1	hsa-miR-1290	hsa-miR-6800-5p
	miRNA pairs2	hsa-miR-1343-3p	hsa-miR-3184-5p
LC-MPS6	miRNA pairs1	hsa-miR-1343-3p	hsa-miR-6799-5p
	miRNA pairs2	hsa-miR-1343-3p	hsa-miR-6756-5p
	miRNA pairs3	hsa-miR-1343-3p	hsa-miR-6879-5p
	miRNA pairs4	hsa-miR-6746-5p	hsa-miR-6741-5p
	miRNA pairs5	hsa-miR-6746-5p	hsa-miR-197-5p
	miRNA pairs6	hsa-miR-5006-5p	hsa-miR-6831-5p
Note: a sample was considered to be lung cancer if at least half of the miRNA pairs in the signature had the specific relative expression order (miRNA 1 > miRNA 2). Otherwise, the sample was considered to be non-lung cancer.

The sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC) of LC-MPS2 were all 100% in the training datasets (Fig. 3A). In the independent validation dataset LC4046, which included 115 lung cancer samples and 2759 non-cancer control samples, the sensitivity, specificity, accuracy and AUC were 99.7%, 100.0%, 99.8% and 99.9%, respectively (Fig. 3B). Similarly, in the independent validation set LC3924, which included 1566 lung cancers (excluding 180 postoperative samples) and 2178 non-cancer control samples, the sensitivity, specificity, accuracy and AUC were 99.7%, 99.3%, 99.5% and 99.7% (Fig. 3C). The results demonstrated that LC-MPS2 is an effective tool for differentiating between lung cancer and non-cancer patients.

Validation of LC-MPS2 in different types of lung cancer samples

Furthermore, the recognition of LC-MPS2 in different pathological stages, T stages, N stages, M stages and histological subtype of lung cancer samples was analysed using 1356 lung cancer samples with pathological stage and histological subtype information from the LC3924.The results demonstrated that LC-MPS2 identified more than 99% of patients with IA and IB lung cancer. For patients with IIA and IIB disease, the accuracy was 97.9% and 98.4%, respectively. Furthermore, 100% accuracy was achieved for patients with IIIA, IIIB, and IV disease (Fig. 4A). For patients with different T, N and M stages of lung cancer, LC-MPS2 identified T1a, T1b, T2a, T2b, T3, and T4 stage patients with an accuracy of 99.8%, 99.3%, 99.3%, 100%, 98.9%, and 100% (Fig. 4B). The accuracy of identifying patients with stage N0, N1 and N2 was 99.6%, 98.2% and 100%, respectively (Fig. 4C). Similarly, the accuracy of identifying patients with stage M0 and M1 disease was 99.3% and 100%, respectively (Fig. 4D). These findings demonstrate that LC-MPS2 has a robust predictive capacity for lung cancer patients at various pathological stages.

For lung cancer patients with different tissue subtypes, LC-MPS2 can accurately identify lung adenocarcinoma (ADC), small cell lung cancer (SCLC), squamous cell carcinoma (SQCC) and other (mixed subtypes other than the first three) lung cancers with accuracies of 99.2%, 100%, 99.0% and 100%, respectively. (Fig. 4E). Notebly, when LC-MPS2 was used to identify lung cancer in 180 samples from patients who had undergone surgical resection, only one sample (0.6%) was identified as lung cancer (Fig. 4F). This provides further evidence that LC-MPS2 can accurately identify lung cancer samples from non-cancer samples.

The performance of LC-MPS2 in non-lung other cancer tumor samples

A further analysis of the performance of LC-MPS2 was performed using different types of cancer samples from the pooled data of LC1591, LC972 and LC4046. The results showed that LC-MPS2 would identify 99.5% of lung cancers, 99.1% of bladder cancers, 96.7% of biliary tract cancers, 85.4% of colorectal cancers, 91.6% of esophageal squamous cell cancers, 100% of gastric cancers, 83.3% of gliomas, 88.3% of liver cancers, 85.5% of ovarian cancers, 85.9% of pancreatic cancers, 77.3% of prostate cancers and 71.2%of sarcomas as lung cancers (Fig. 4G). Only all breast cancers were identified as non-lung cancers. Similar results were seen in each of the three datasets (Supplementary Fig. S1). This suggests that LC-MPS2 has certain pan-cancer properties.

The signature to discriminate lung cancer from other types of cancer

Furthermore, we intend to construct a lung cancer specific signature that can distinguish lung cancer from other types of cancer samples. As shown in Fig. 2, 90 lung cancer and 2332 other types of cancer samples (including 12 different types, details in Supplementary Table S1) from the training dataset of LC1591 and LC972 were used. A total of 1,448,193 and 1,279,775 miRNA pairs with identical REOs patterns were identified in more than 75% of lung cancer and other cancer samples, respectively. Subsequently, 13 miRNA pairs with inverted REOs were identified between lung cancer and other cancer samples. Based on the 13 miRNA pairs, we obtained a set of six miRNA pairs that achieved the highest F-score using the majority voting rule (detail in methods). The six miRNA pairs (Table 2) together with their REO patterns, designated LC-MPS6, were selected as a new signature for lung cancer diagnosis.

In the training set (pooled data from LC1591 and LC972), the sensitivity, specificity, accuracy and AUC of LC-MPS6 were 90.9%, 91.1%, 90.9% and 95%, respectively (Fig. 5A). In the independent validation dataset LC4046, which included 115 lung cancer samples and 1172 other cancer samples, the sensitivity, specificity, accuracy and AUC were 80.2%, 93.9%, 81.4% and 89.0%, respectively (Fig. 5B). These results demonstrated that LC-MPS6 is an effective signature for the identification of lung cancer and other cancer samples.

The performance of the LC-MPS6 was further analyzed in each of the 12 types of tumor samples. The results demonstrated that LC-MPS6 could accurately identify 91.1% of lung cancers, 98.6% of bladder cancers, 100% of breast cancers, 71.1% of biliary tract cancers, 68.9% of colorectal cancers, 98.9% of esophageal squamous cell cancers, 13.3% of gastric cancers, 96.7% of gliomas, 70.0% of liver cancers, 100% of ovarian cancers, 97.8% of pancreatic cancers, 96.4% of prostate cancers and 97.8% of sarcoma cancers as non-lung cancer samples in the train data (Fig. 6A). Even for non-cancer samples included in the training data, LC-MPS6 was able to discriminate them from lung cancer with 100% accuracy. In LC4046, LC-MPS6 was able to accurately identify 99.9% of non-cancer, 93.9% of lung cancer, 100% of breast cancer, 15.7% of colorectal cancer, 97.7% of esophageal squamous cell cancer, 8.7% of gastric cancer, 72.8% of liver cancer, 99.8% of ovarian cancer, 100% of pancreatic cancer and 95.7% of sarcoma cancer as non-lung cancer samples (Fig. 6B). Similar results were observed in the pooled datasets of the training data and LC4046 (Supplementary Fig. S2).

In this study, we first developed a signature, designated as LC-MPS2. The signature was developed based on the expression of miRNAs in lung cancer and non-cancer control samples. The signature demonstrated a high degree of accuracy in discriminating between lung cancer and non-cancer control samples, with a sensitivity of over 99.0% and a specificity of over 99.0%. Furthermore, LC-MPS2 demonstrated the capacity to discriminate ovarian cancer, sarcoma, biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancer from non-cancer control samples with accuracy ranging from 71.2–100%. The results demonstrate the pan-cancer ability of LC-MPS2 to identify cancers from non-cancers. This may be attributed to the pan-cancer properties when lung cancer and non-cancer cancers were employed to extract the miRNA pairs. For instance, hsa-miR-1290 was found to be overexpressed in a number of cancers, including breast[23], colorectal[24], lung[25], pancreatic[26], and esophageal squamous cell carcinoma[27, 28]. Additionally, hsa-miR-1343-3p was also identified as playing as important role in lung cancer[29], pancreatic cancer[30], colorectal cancer[31], glioma[32], gastric cancer[33] and other cancers. Furthermore, hsa-miR-6800-5p was found to be overexpressed in ovarian cancer[34]. hsa-miR-3184-5p was associated with the development of gastric cancer[35] and ovarian cancer[36].

However, LC-MPS2 was unable to distinguish lung cancer from other types of cancers. Consequently, we further constructed another signature, designated as LC-MPS6, which was trained on the miRNA expression profiles of lung cancer and 12 other types of cancers samples. The signature was able to identify bladder cancer, breast cancer, esophageal squamous cell cancer, glioma, ovarian cancer, pancreatic cancer, prostate cancer and sarcoma cancer as non-lung cancer samples with an accuracy of above 95.0% in the training and validation datasets. And, it even could accurately identify non-cancer control samples, which were not involved in the signature training. This may be attributed to the fact that lung cancer and a variety of other cancers were used when screening of gene pairs which lead to lung cancer-specific gene pairs were extracted and lung cancer-specific signatures were further constructed. However, LC-MPS6 exhibits a limited ability to discriminate gastrointestinal cancers such as biliary tract cancer, colorectal cancer, gastric cancer, and liver cancer. This may be attributed to the fact that the lung is one of the most common sites of metastasis for gastrointestinal cancers[37–41].

In the current lung cancer biomarkers research based on blood samples, researchers usually obtain features by comparing the expression profiles of lung cancer and non-cancer samples, and then construct models based on these features. However, our research indicates that such models are not lung cancer-specific models, but rather exhibit pan-cancer characteristics. Similar results have also been reported[12, 42]. Consequently, when constructing cancer prediction models based on samples from non-tissue sites such as blood, it is essential to use multiple cancer types as control to identify single cancer type-specific markers.

In conclusion, our study offers valuable insights into the significant alterations in miRNA profiles in lung cancer and non-cancer patients. Two highly sensitive miRNA-based signatures were developed. The first signature, LC-MPS2, is a pan-cancer signature which was able to effectively distinguish multiple cancer patients from non-cancer controls. The second signature, LC-MPS6, is a lung cancer specific signature that is effective in distinguishing lung cancer patients from non-lung cancer patients with other types of cancers.

Acknowledgements

Not applicable.

Author contributions

WDD: funding acquisition, formal analysis, validation, writing—original draft; YNL: data curation, formal analysis; YSY: methodology; ZYC: formal analysis; SXP: funding acquisition, supervision, writing—review and editing; HJ: conceptualization, funding acquisition, writing—original draft, writing—review and editing.

Funding information

This work was supported by Fujian Medical University Research Foundation of Talented Scholars (XRCZX2020021, XRCZX2020027), Natural Science Foundation of Fujian Province (2022J05054, 2023J01321), Youth Scientific Research Fund Program of School of Medical Technology and Engineering, Fujian Medical University(2021xy001), Startup Fund for Scientific Research, Fujian Medical University (2022QH1011) .

Data availability

The datasets supporting the conclusions of this article are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/).

Ethics approval and consent to participate

Not applicable.

Consent for publication

All patients gave informed consent for the publication of the results.

Competing interests

The authors declare no competing interests

Author details

¹ School of Nuring, Fujian Medical University, Fuzhou 350122, China.

² Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Institute of Precision Medicine, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China.

³ Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China.

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024, 74:229-263.
Chansky K, Detterbeck FC, Nicholson AG, Rusch VW, Vallieres E, Groome P, Kennedy C, Krasnik M, Peake M, Shemanski L, et al: The IASLC Lung Cancer Staging Project: External Validation of the Revision of the TNM Stage Groupings in the Eighth Edition of the TNM Classification of Lung Cancer. J Thorac Oncol 2017, 12:1109-1121.
Guerreiro T, Forjaz G, Antunes L, Bastos J, Mayer A, Aguiar P, Araujo A, Nunes C: Lung cancer survival and sex-specific patterns in Portugal: A population-based analysis. Pulmonology 2023, 29 Suppl 4:S70-S79.
Oudkerk M, Liu S, Heuvelmans MA, Walter JE, Field JK: Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2021, 18:135-151.
Screening for lung cancer. CA Cancer J Clin 2024, 74:82-83.
Jonas DE, Reuland DS, Reddy SM, Nagle M, Clark SD, Weber RP, Enyioha C, Malo TL, Brenner AT, Armstrong C, et al: Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2021, 325:971-987.
Tailor TD, Choudhury KR, Tong BC, Christensen JD, Sosa JA, Rubin GD: Geographic Access to CT for Lung Cancer Screening: A Census Tract-Level Analysis of Cigarette Smoking in the United States and Driving Distance to a CT Facility. J Am Coll Radiol 2019, 16:15-23.
Liu L, Lin F, Ma X, Chen Z, Yu J: Tumor-educated platelet as liquid biopsy in lung cancer patients. Crit Rev Oncol Hematol 2020, 146:102863.
Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N: Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 2017, 17:223-238.
Nooreldeen R, Bach H: Current and Future Development in Lung Cancer Diagnosis. Int J Mol Sci 2021, 22.
Asakura K, Kadota T, Matsuzaki J, Yoshida Y, Yamamoto Y, Nakagawa K, Takizawa S, Aoki Y, Nakamura E, Miura J, et al: A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. Commun Biol 2020, 3:134.
Zhang A, Hu H: A Novel Blood-Based microRNA Diagnostic Model with High Accuracy for Multi-Cancer Early Detection. Cancers (Basel) 2022, 14.
Qi L, Chen L, Li Y, Qin Y, Pan R, Zhao W, Gu Y, Wang H, Wang R, Chen X, Guo Z: Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer. Brief Bioinform 2016, 17:233-242.
Guan Q, Yan H, Chen Y, Zheng B, Cai H, He J, Song K, Guo Y, Ao L, Liu H, et al: Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer. BMC Genomics 2018, 19:99.
Guan Q, Zeng Q, Jiang W, Xie J, Cheng J, Yan H, He J, Xu Y, Guan G, Guo Z, Ao L: A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions. Front Genet 2020, 11:573787.
Guan Q, Zeng Q, Yan H, Xie J, Cheng J, Ao L, He J, Zhao W, Chen K, Guo Y, et al: A qualitative transcriptional signature for the early diagnosis of colorectal cancer. Cancer Sci 2019, 110:3225-3234.
He J, Cheng J, Guan Q, Yan H, Li Y, Zhao W, Guo Z, Wang X: Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy. Cancer Sci 2020, 111:253-265.
Li M, Chen H, He J, Xie J, Xia J, Liu H, Shi Y, Guo Z, Yan H: A qualitative classification signature for post-surgery 5-fluorouracil-based adjuvant chemoradiotherapy in gastric cancer. Radiother Oncol 2020, 155:65-72.
Urabe F, Matsuzaki J, Yamamoto Y, Kimura T, Hara T, Ichikawa M, Takizawa S, Aoki Y, Niida S, Sakamoto H, et al: Large-scale Circulating microRNA Profiling for the Liquid Biopsy of Prostate Cancer. Clin Cancer Res 2019, 25:3016-3025.
Usuba W, Urabe F, Yamamoto Y, Matsuzaki J, Sasaki H, Ichikawa M, Takizawa S, Aoki Y, Niida S, Kato K, et al: Circulating miRNA panels for specific and early detection in bladder cancer. Cancer Sci 2019, 110:408-419.
Yokoi A, Matsuzaki J, Yamamoto Y, Yoneoka Y, Takahashi K, Shimizu H, Uehara T, Ishikawa M, Ikeda SI, Sonoda T, et al: Integrated extracellular microRNA profiling for ovarian cancer screening. Nat Commun 2018, 9:4319.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M: pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011, 12:77.
Endo Y, Toyama T, Takahashi S, Yoshimoto N, Iwasa M, Asano T, Fujii Y, Yamashita H: miR-1290 and its potential targets are associated with characteristics of estrogen receptor alpha-positive breast cancer. Endocr Relat Cancer 2013, 20:91-102.
Wu J, Ji X, Zhu L, Jiang Q, Wen Z, Xu S, Shao W, Cai J, Du Q, Zhu Y, Mao J: Up-regulation of microRNA-1290 impairs cytokinesis and affects the reprogramming of colon cancer cells. Cancer Lett 2013, 329:155-163.
Kim KB, Kim K, Bae S, Choi Y, Cha HJ, Kim SY, Lee JH, Jeon SH, Jung HJ, Ahn KJ, et al: MicroRNA-1290 promotes asiatic acid‑induced apoptosis by decreasing BCL2 protein level in A549 non‑small cell lung carcinoma cells. Oncol Rep 2014, 32:1029-1036.
Wei J, Yang L, Wu YN, Xu J: Serum miR-1290 and miR-1246 as Potential Diagnostic Biomarkers of Human Pancreatic Cancer. J Cancer 2020, 11:1325-1333.
Kalhori MR, Soleimani M, Arefian E, Alizadeh AM, Mansouri K, Echeverria J: The potential role of miR-1290 in cancer progression, diagnosis, prognosis, and treatment: An oncomiR or onco-suppressor microRNA? J Cell Biochem 2022, 123:506-531.
Guz M, Jeleniewicz W, Cybulski M: An Insight into miR-1290: An Oncogenic miRNA with Diagnostic Potential. Int J Mol Sci 2022, 23.
Zhao L, Song X, Guo Y, Ding N, Wang T, Huang L: Long non‑coding RNA SNHG3 promotes the development of non‑small cell lung cancer via the miR‑1343‑3p/NFIX pathway. Int J Mol Med 2021, 48.
Chen X, Wang J, Xie F, Mou T, Zhong P, Hua H, Liu P, Yang Q: Long noncoding RNA LINC01559 promotes pancreatic cancer progression by acting as a competing endogenous RNA of miR-1343-3p to upregulate RAF1 expression. Aging (Albany NY) 2020, 12:14452-14466.
Li H, Liu J, Lai Y, Huang S, Zheng L, Fan N: LINC01559 promotes colorectal cancer via sponging miR-1343-3p to modulate PARP1/PTEN/AKT pathway. Pathol Res Pract 2021, 224:153521.
Qi J, Wang Z, Zhao Z, Liu L: EIF3J-AS1 promotes glioma cell growth via up-regulating ANXA11 through sponging miR-1343-3p. Cancer Cell Int 2020, 20:428.
Zhang Y, Wang X, Liu W, Lei T, Qiao T, Feng W, Song W: CircGLIS3 promotes gastric cancer progression by regulating the miR-1343-3p/PGK1 pathway and inhibiting vimentin phosphorylation. J Transl Med 2024, 22:251.
Hamidi F, Gilani N, Belaghi RA, Sarbakhsh P, Edgunlu T, Santaguida P: Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence. Front Genet 2021, 12:724785.
Lin S, Que Y, Que C, Li F, Deng M, Xu D: Exosome miR-3184-5p inhibits gastric cancer growth by targeting XBP1 to regulate the AKT, STAT3, and IRE1 signalling pathways. Asia Pac J Clin Oncol 2023, 19:e27-e38.
Hamidi F, Gilani N, Arabi Belaghi R, Yaghoobi H, Babaei E, Sarbakhsh P, Malakouti J: Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta. Front Digit Health 2023, 5:1187578.
Wang J, Bo X, Nan L, Wang CC, Gao Z, Suo T, Ni X, Liu H, Lu P, Wang Y, Liu H: Landscape of distant metastasis mode and current chemotherapy efficacy of the advanced biliary tract cancer in the United States, 2010-2016. Cancer Med 2020, 9:1335-1348.
Wang L, Liang B, Jiang Y, Huang G, Tang A, Liu Z, Wang Y, Zhou R, Yang N, Wu J, et al: Subsite-specific metastatic organotropism and risk in gastric cancer: A population-based cohort study of the US SEER database and a Chinese single-institutional registry. Cancer Med 2023, 12:19595-19606.
Qin BD, Jiao XD, Liu J, Liu K, He X, Wu Y, Ling Y, Duan XP, Qin WX, Wang Z, Zang YS: The effect of liver metastasis on efficacy of immunotherapy plus chemotherapy in advanced lung cancer. Crit Rev Oncol Hematol 2020, 147:102893.
Ciardiello F, Ciardiello D, Martini G, Napolitano S, Tabernero J, Cervantes A: Clinical management of metastatic colorectal cancer in the era of precision medicine. CA Cancer J Clin 2022, 72:372-401.
Qiu MZ, Shi SM, Chen ZH, Yu HE, Sheng H, Jin Y, Wang DS, Wang FH, Li YH, Xie D, et al: Frequency and clinicopathological features of metastasis to liver, lung, bone, and brain from gastric cancer: A SEER-based study. Cancer Med 2018, 7:3662-3672.
Chen JW, Dhahbi J: Identification of four serum miRNAs as potential markers to screen for thirteen cancer types. PLoS One 2022, 17:e0269554.

The authors declare no competing interests.

FigureS1.tif
Fig. S1 Performance of LC-MPS2 in different types of cancer. Performance of LC-MPS2 in LC1591 (a), LC972 (b)and LC4046 (c). Note: BLC: bladder cancer; BRCA: breast cancer; BTC: biliary tract cancer; CRC: colorectal cancer; ESCC: esophageal squamous cell carcinoma; GC: gastric cancer; GM: glioma; HCC: liver cancer; OC: ovarian cancer; PaC: pancreatic cancer; PrC: prostate cancer.
FigureS2.tif
Fig. S2 Performance of LC-MPS6 in different types of cancer. Performance of LC-MPS6 in LC1591 (a), LC972 (b) and all samples (c).
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Qualitative serum microRNA signatures for lung cancer screening

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Abstract

Figures

Introduction

Methods

Data sources and preprocessing

The Relative Expression Orderings Strategy

Uniform Manifold Approximation and Projection algorithm

Area under the curve

Results

The diverse expression patterns of serum miRNAs between lung cancer and non-cancer samples

The signature to discriminate lung cancer from non-cancer

Validation of LC-MPS2 in different types of lung cancer samples

The performance of LC-MPS2 in non-lung other cancer tumor samples

The signature to discriminate lung cancer from other types of cancer

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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