A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

Chromato-kinetic fingerprinting enables multiomic digital counting of single disease biomarker molecules

Early and personalized intervention in complex diseases requires robust molecular diagnostics, yet the simultaneous detection of diverse biomarkers-microRNAs (miRNAs), mutant DNAs, and proteins-remains challenging due to low abundance and preprocessing incompatibilities. We present Biomarker Single-molecule Chromato-kinetic multi-Omics Profiling and Enumeration (Bio-SCOPE), a next-generation, triple-modality, multiplexed detection platform that integrates both chromatic and kinetic fingerprinting for molecular profiling through digital encoding. Bio-SCOPE achieves femtomolar sensitivity, single-base mismatch specificity, and minimal matrix interference, enabling precise, parallel quantification of up to six biomarkers in a single sample with single-molecule resolution. We demonstrate its versatility in accurately detecting low-abundance miRNA signatures from human tissues, identifying upregulated miRNAs in the plasma of prostate cancer patients, and measuring elevated interleukin-6 (IL-6) and hsa-miR-21 levels in cytokine release syndrome patients. By seamlessly integrating multiomic biomarker panels on a unified, high-precision platform, Bio-SCOPE provides a transformative tool for molecular diagnostics and precision medicine.

Serum NLR combined with CA125 and HE4 improves the diagnostic and prognostic efficiency in patients with ovarian cancer

Background

Ovarian cancer (OC) represents a common neoplasm within the female reproductive tract. The prognosis for patients diagnosed at advanced stages is unfavorable, primarily attributable to the absence of reliable screening markers for early detection. An elevated neutrophil-to-lymphocyte ratio (NLR) serves as an indicator of host inflammatory response and has been linked to poorer overall survival (OS) across various cancer types; however, its examination in OC remains limited. This study seeks to identify combination diagnostic and prognostic markers for OC, aiming to improve diagnostic and prognostic efficacy, especially in the early stages.

Methods

We analyzed the targeted biomarkers in a cohort of 104 OC patients and 100 controls, which comprised 50 patients with benign ovarian tumors and 50 healthy women, using enzyme-linked immunosorbent assay (ELISA) and complete blood counting (CBC). After validating the biomarker panel, we compared the expression levels of the biomarkers in OC patients with various clinical features to assess their relevance. A biomarker panel was developed and validated with an independent cohort of 70 OC patients and 60 controls, including 30 with benign ovarian tumors and 30 healthy women. We evaluated the diagnostic accuracy using the area under the receiver-operating characteristic (ROC) curve and overall survival analysis was used for prognosis.

Results

The results from ELISA and CBC analyses indicated that the NLR was significantly higher in patients with OC. This elevation was especially notable in those with advanced stages of the disease, lymph node metastasis, and ascites. The diagnostic performance of the NLR, when combined with CA125 and HE4, outperformed each marker used individually, especially when compared to the traditional combination of CA125 and HE4. Importantly, we observed similar results in patients with early-stage ovarian cancer and those with low levels of CA125 and HE4. In addition, these results suggest that NLR combined with CA125 and HE4 levels in OC patients have significant prognostic value.

Conclusions

The effective combination of serum NLR, CA125, and HE4 significantly enhances diagnostic efficiency in patients with OC. Serum NLR, CA125, and HE4 levels were identified as independent prognostic markers for OC.

Machine-learning diagnostic models for ovarian tumors

Purpose

To create a diagnostic framework for clinical behavior and pathological tissue prognosis in ovarian cancer by using machine-learning (ML) methods based on multiple biomarkers.

Experimental design

Overall, 713 patients with ovarian tumors at Sun Yat Sen Memorial Hospital were randomized into training and test cohorts. Four supervised ML classifiers, namely Support Vector Machine, Random Forest, k-nearest neighbor, and logistic regression were used to derive diagnostic and prognostic information from 10 parameters commonly available from pretreatment peripheral blood tests and age. The best prediction model was selected and validated by comparing the accuracy and the area under the ROC curve of each prediction model and by applying the external data of Guangdong Maternal and Child Health Center.

Results

ML techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to ovarian tumor. Ensemble methods combining weak decision trees and RF showed the best reference in diagnosis, especially for malignant ovarian cancer. The values for the highest accuracy and area under the ROC curve for malignant ovarian cancer from benign or borderline ovarian tumors with RF were 99.82 % and 0.86 (micro-average ROC curve), respectively. The greatest accuracy and AUC for the diagnosis of pathological tissue with logistic regression curve were 78.0 % and 0.95 (micro-average ROC curve), respectively. In external validation, the random forest prediction model had an accuracy of 0.789 for applying data from external centers to verify tumor benignity and malignancy, and the logistic regression model had an accuracy of 0.719 for predicting the nature of the tumor.

Conclusions

An ovarian tumor can be diagnosed and characterized before initial treatment via ML systems to provide critical diagnostic and prognostic information. The use of predictive algorithms can facilitate customized treatment options with patient preprocessing stratification.

Recent advances in surface plasmon resonance for the detection of ovarian cancer biomarkers: a thorough review

Early detection of ovarian cancer (OC) is crucial for effective management and treatment, as well as reducing mortality rates. However, the current diagnostic methods for OC are time-consuming and have low accuracy. Surface plasmon resonance (SPR) biosensors offer a promising alternative to conventional techniques, as they enable rapid and less invasive screening of various circulating indicators. These biosensors are widely used for biomolecular interaction analysis and detecting tumor markers, and they are currently being investigated as a rapid diagnostic tool for early-stage cancer detection. Our main focus is on the fundamental concepts and performance characteristics of SPR biosensors. We also discuss the latest advancements in SPR biosensors that enhance their sensitivity and enable high-throughput quantification of OC biomarkers, including CA125, HE4, CEA, and CA19-9. Finally, we address the future challenges that need to be overcome to advance SPR biosensors from research to clinical applications. The ultimate goal is to facilitate the translation of SPR biosensors into routine clinical practice for the early detection and management of OC.

Methods and clinical biomarker discovery for targeted proteomics using Olink technology

PURPOSE

This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment.

EXPERIMENTAL DESIGN

We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology.

RESULTS

Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments.

CONCLUSIONS

Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

BACKGROUND

This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors.

METHODS

The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram.

RESULTS

Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0.

CONCLUSIONS

The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.

Nanopore sequencing of DNA-barcoded probes for highly multiplexed detection of microRNA, proteins and small biomarkers

There is an unmet need to develop low-cost, rapid and highly multiplexed diagnostic technology platforms for quantitatively detecting blood biomarkers to advance clinical diagnostics beyond the single biomarker model. Here we perform nanopore sequencing of DNA-barcoded molecular probes engineered to recognize a panel of analytes. This allows for highly multiplexed and simultaneous quantitative detection of at least 40 targets, such as microRNAs, proteins and neurotransmitters, on the basis of the translocation dynamics of each probe as it passes through a nanopore. Our workflow is built around a commercially available MinION sequencing device, offering a one-hour turnaround time from sample preparation to results. We also demonstrate that the strategy can directly detect cardiovascular disease-associated microRNA from human serum without extraction or amplification. Due to the modularity of barcoded probes, the number and type of targets detected can be significantly expanded.

Identification of Plasma Inflammatory Markers of Adolescent Depression Using the Olink Proteomics Platform

Purpose

The quality of life of worldwide adolescents has been seriously affected by depression. Notably, the inflammatory response is closely associated with the pathophysiology of depression. The present study applied a novel targeted proteomics technology, Olink proximity extension assay (PEA), to profile circulating immune-related proteins in adolescents with depression.

Methods

In the present study, the expression levels of 92 inflammation-related proteins were compared between adolescents with depression (ADs) (n=15) and healthy controls (HCs) (n=15), using the OLINK PEA inflammation panel. We further validated 5 top proteins that were identified through KEGG and GO analyses between 40 HCs and 50 ADs, including CCL4, CXCL5, CXCL6, CXCL11, and IL-18 using enzyme linked immunosorbent assay (ELISA).

Results

We identified 13 differentially expressed proteins between the two cohorts, including 5 up-regulated and 8 down-regulated proteins. Among them, the TRAIL protein levels were significantly negatively correlated with the HAMA-14 score (r=-0.538, p= 0.038), and the levels of transforming growth factor α (TGF-α) were significantly associated with a change in appetite (r = -0.658, p = 0.008). After validation by ELISA, CCL4, CXCL5, CXCL11, and IL-18 showed significant changes between ADs and HCs (p < 0.05), while CXCL6 showed an up-regulated tendency in ADs (p=0.0673). The pooled diagnostic efficacy (area under the curve [AUC]) of these five inflammation markers in clinical diagnosis for adolescent depression was 0.819 (95% CI: 0.735-0.904).

Conclusion

We report a number of inflammation-related plasma biomarkers, which uncover a potential involvement of chemokines, cytokines, and cytokine receptors in adolescent depression. Their roles in the pathophysiology of depression need to be further elucidated.

Tumor Markers and Their Diagnostic Significance in Ovarian Cancer

Ovarian cancer (OC) is characterized by silent progression and late-stage diagnosis. It is critical to detect and accurately diagnose the disease early to improve survival rates. Tumor markers have emerged as valuable tools in the diagnosis and management of OC, offering non-invasive and cost-effective options for screening, monitoring, and prognosis.

PURPOSE

This paper explores the diagnostic importance of various tumor markers including CA-125, CA15-3, CA 19-9, HE4,hCG, inhibin, AFP, and LDH, and their impact on disease monitoring and treatment response assessment.

METHODS

Article searches were performed on PubMed, Scopus, and Google Scholar. Keywords used for the searching process were "Ovarian cancer", "Cancer biomarkers", "Early detection", "Cancer diagnosis", "CA-125","CA 15-3","CA 19-9", "HE4","hCG", "inhibin", "AFP", "LDH", and others.

RESULTS

HE4, when combined with CA-125, shows improved sensitivity and specificity, particularly in early-stage detection. Additionally, hCG holds promise as a prognostic marker, aiding treatment response prediction and outcome assessment. Novel markers like microRNAs, DNA methylation patterns, and circulating tumor cells offer potential for enhanced diagnostic accuracy and personalized management. Integrating these markers into a comprehensive panel may improve sensitivity and specificity in ovarian cancer diagnosis. However, careful interpretation of tumor marker results is necessary, considering factors such as age, menopausal status, and comorbidities. Further research is needed to validate and refine diagnostic algorithms, optimizing the clinical significance of tumor markers in ovarian cancer management. In conclusion, tumor markers such as CA-125, CA15-3, CA 19-9, HE4, and hCG provide valuable insights into ovarian cancer diagnosis, monitoring, and prognosis, with the potential to enhance early detection.

Analytical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis

PURPOSE

To characterize and analytically validate the MSDA Test, a multi-protein, serum-based biomarker assay developed using Olink^® PEA methodology.

EXPERIMENTAL DESIGN

Two lots of the MSDA Test panel were manufactured and subjected to a comprehensive analytical characterization and validation protocol to detect biomarkers present in the serum of patients with multiple sclerosis (MS). Biomarker concentrations were incorporated into a final algorithm used for calculating four Disease Pathway scores (Immunomodulation, Neuroinflammation, Myelin Biology, and Neuroaxonal Integrity) and an overall Disease Activity score.

RESULTS

Analytical characterization demonstrated that the multi-protein panel satisfied the criteria necessary for a fit-for-purpose validation considering the assay's intended clinical use. This panel met acceptability criteria for 18 biomarkers included in the final algorithm out of 21 biomarkers evaluated. VCAN was omitted based on factors outside of analytical validation; COL4A1 and GH were excluded based on imprecision and diurnal variability, respectively. Performance of the four Disease Pathway and overall Disease Activity scores met the established acceptability criteria.

CONCLUSIONS AND CLINICAL RELEVANCE

Analytical validation of this multi-protein, serum-based assay is the first step in establishing its potential utility as a quantitative, minimally invasive, and scalable biomarker panel to enhance the standard of care for patients with MS.

Circulating microRNAs in gallbladder cancer: Is serum assay of diagnostic value?

The microRNAs (miRNAs) in circulation could serve as biomarkers for cancer detection. Gallbladder carcinoma (GBC) is mostly asymptomatic; therefore, using microRNAs (miRNAs) as an early diagnostic biomarker could be a valuable tool. We aimed to identify the tumor-associated miR-1, miR130, miR-146, miR-182, and miR-21expression in serum as a biomarker for early detection of GBC and identify their possible diagnostic role. The study group comprised of paired serum and tissue samples from 34 GBC, 19 cholecystitis (CC), 21 normal controls (uninflamed gall bladder), and additional 29 serum-only samples of GBC. Total RNA was isolated using a commercially available RNA isolation kit (Applied Biosystem, USA) and reverse transcribed using Advanced Taqman MicroRNA reverse transcription kit. The relative expression of miRNAs was analyzed using Quantitative real-time polymerase chain reaction. The diagnostic potential of these miRNAs was assessed by ROC analysis. In paired samples, the trend towards up and down regulation for miR-182, miR-21, miR-1, miR-130, and miR-146 was similar in both tissue and sera of GBC. The expression pattern of serum miR-1, miR130, and miR-146 gradually decreased from normal control (NC) to CC to GBC, while miR-21 and miR-182 gradually increased from NC to CC to GBC. The miR-1, miR-121, miR-182, and miR-146 significantly differed between CC vs. early stage and early stage vs. NC. Among these miRNAs, the sensitivity of miR-1 (85.71 %) was the highest, and the specificity of miR-21 was the highest (92.73 %). The combined sensitivity for miRNAs ranged from 73.13 % (CI: 60.90-83.24 %) to 98.63 % (CI: 89.0-99.61 %); however, the specificity was lower. In stage I&II vs. III&IV discrimination, the diagnostic sensitivity of miR-1 was highest (89.36 %, CI: 76.90-96.45). The two miRNAs, in combination, increase the diagnostic sensitivity. Circulating serum miRNAs may provide a new approach for clinical application. Panels of specific circulating miRNA, which require further validation, could be potential non-invasive diagnostic biomarkers for GBC in combination with abnormal radio diagnostic scans.

Development of a Multiprotein Classifier for the Detection of Early Stage Ovarian Cancer

BACKGROUND

Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most treatable.

METHODS

The Olink Proseek Multiplex Oncology II panel was used to simultaneously quantify the expression levels of 92 cancer-related proteins in sera.

RESULTS

In the discovery phase, we generated a multiprotein classifier that included CA125, HE4, ITGAV, and SEZ6L, based on an analysis of sera from 116 women with early stage ovarian cancer and 336 age-matched healthy women. CA125 alone achieved a sensitivity of 87.9% at a specificity of 95%, while the multiprotein classifier resulted in an increased sensitivity of 91.4%, while holding the specificity fixed at 95%. The performance of the multiprotein classifier was validated in a second cohort comprised of 192 women with early stage ovarian cancer and 467 age-matched healthy women. The sensitivity at 95% specificity increased from 74.5% (CA125 alone) to 79.2% with the multiprotein classifier. In addition, the multiprotein classifier had a sensitivity of 95.1% at 98% specificity for late stage ovarian cancer samples and correctly classified 80.5% of the benign samples using the 98% specificity cutpoint.

CONCLUSIONS

The inclusion of the proteins HE4, ITGAV, and SEZ6L improved the sensitivity and specificity of CA125 alone for the detection of early stages of ovarian cancer in serum samples. Furthermore, we identified several proteins that may be novel biomarkers of early stage ovarian cancer.

Applicability of polygenic risk scores in endometriosis clinical presentation

Background

Risk prediction is an essential part of preventative medicine and in recent years genomic information has become an interesting factor in risk models. Polygenic risk scores (PRS) combine the effect of many genetic variations into a single score which has been shown to have predictive value for many diseases. This study aimed to investigate the association between PRS for endometriosis and the clinical presentation of the disease.

Methods

Women with endometriosis (N = 172) were identified at the Department of Gynecology. All participants answered questionnaires regarding sociodemographic factors, lifestyle habits and medical history, registered bowel symptoms on the Visual Analog Scale for Irritable Bowel Syndrome and passed blood samples. DNA was extracted and samples were genotyped, and a PRS was calculated based on previous genome-wide association studies of endometriosis. Inflammatory proteins and TSH receptor antibodies (TRAb) in serum were analyzed.

Results

Inverse associations were identified between PRS and spread of endometriosis, involvement of the gastrointestinal tract and hormone treatment. However, significance was lost when calculated as p for trend and the specificity and sensitivity were low. There were no correlations between PRS and TRAb or inflammatory proteins.

Conclusion

The findings indicate that specific PRS should be developed to predict clinical presentations in patient with endometriosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-022-01788-w.

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

BACKGROUND

CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer.

METHODS

In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer.

RESULTS

Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9-18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone.

CONCLUSION

Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0-9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

Efficacy of circulating microRNA-130b and blood routine parameters in the early diagnosis of gastric cancer

Patients with gastric cancer (GC) have a poor prognosis, which is mainly due to the low rate of early diagnosis. The present study aimed to evaluate whether circulating microRNA-130b (miR-130b) and blood routine parameters [neutrophil count (N#), lymphocyte count (L#), monocyte count (M#), neutrophil percentage (N%), lymphocyte percentage (L%), monocyte percentage (M%), hemoglobin (Hb) level, hematocrit (Hct), red blood cell distribution width (RDW), platelet count, platelet distribution width (PDW), mean platelet volume (MPV), MPV to platelet count ratio (MPV/PC), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR)] are useful biomarkers for GC, early stage GC (EGC) and precancerous lesion (Pre) detection, and to identify more effective diagnostic models by combining circulating blood markers. Circulating levels of M#, M%, RDW-coefficient of variation (RDW-CV), MPV, PDW, MLR and NLR were significantly higher, and the levels of Hb and L% were significantly lower in patients with GC and Pre compared with those in healthy controls (NCs) (all P<0.05). The N#, N% and PLR in patients with GC were significantly higher and the Hct was significantly lower than those in the NCs (all P<0.05). The values of MPV/PC were significantly higher in the Pre cohort compared with those in the NCs. The area under the curve (AUC) of the receiver operating characteristic curve of potential biomarkers for GC was 0.634-0.887 individually, and this increased to 0.978 in the combination model of miR-130b-PDW-MLR-Hb. Additionally, the values for RDW-CV, PLR, NLR, N# and N% were positively correlated with cancer stage, while the values for MPV, L#, L%, Hb and Hct were negatively correlated with cancer stage. Furthermore, the circulating levels of miRNA-130b, and the values for NLR, RDW-CV, PDW, M%, red blood cell count, Hct, Hb and MLR differed between the EGC and NC groups. The AUC values of these biomarkers were 0.6491-0.911 individually in the diagnosis of EGC, and these increased to 0.960 in combination. In addition, the AUC values for miR-130b, RDW-CV, MPV/PC ratio, MLR, NLR, PDW, L%, M%, M# and Hb in the diagnosis of Pre were 0.638-0.811 individually. The dual-model of miR-130b-PDW manifested the largest AUC of 0.896 in the diagnosis of Pre, and the sensitivity and accuracy were increased when miR-130b and PDW were combined. All these results suggested that circulating miR-130b and blood routine parameters might be potential biomarkers, and combinations of measurements of these biomarkers may improve the GC, EGC and Pre diagnostic accuracy.