Diagnosis Of Gastric Cancer Research Articles

Diagnosis and prognosis prediction of gastric cancer by high-performance serum lipidome fingerprints.

Early detection is warranted to improve prognosis of gastric cancer (GC) but remains challenging. Liquid biopsy combined with machine learning will provide new insights into diagnostic strategies of GC. Lipid metabolism reprogramming plays a crucial role in the initiation and development of tumors. Here, we integrated the lipidomics data of three cohorts (n = 944) to develop the lipid metabolic landscape of GC. We further constructed the serum lipid metabolic signature (SLMS) by machine learning, which showed great performance in distinguishing GC patients from healthy donors. Notably, the SLMS also held high efficacy in the diagnosis of early-stage GC. Besides, by performing unsupervised consensus clustering analysis on the lipid metabolic matrix of patients with GC, we generated the gastric cancer prognostic subtypes (GCPSs) with significantly different overall survival. Furthermore, the lipid metabolic disturbance in GC tissues was demonstrated by multi-omics analysis, which showed partially consistent with that in GC serums. Collectively, this study revealed an innovative strategy of liquid biopsy for the diagnosis of GC on the basis of the serum lipid metabolic fingerprints.

Current status of evaluation and treatment of early-stage remnant gastric cancer

Because of early diagnosis and improved prognosis, patients with gastric cancer are now surviving longer and remnant gastric cancer after gastrectomy is becoming more common. Remnant gastric cancer is traditionally considered a malignancy with a dismal outcome. However, recent advances in diagnostic and therapeutic strategies have improved outcomes. In recent years, the development of endoscopic therapy has provided us with new therapeutic ideas. Although with some drawbacks, such as limited working space, gastric fibrosis and staples under the suture line, endoscopic management is still an effective treatment option with potential long-term survival advantage. For gastrectomy patients, endoscopic surveillance should also be recommended, for prompt surveillance and detection in the early remnant gastric cancer. This review aims to provide an overview of remnant gastric cancer, especially focusing on its pathogenesis as well as new advances in the diagnosis and endoscopic treatment of early-stage remnant gastric cancer.

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis.

Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. This research aims to analyze salivary microRNA expression together with clinical and demographic features with the aim of diagnosing gastric cancer. The study included 19 patients with early-stage gastric cancer and 19 healthy controls. Saliva samples were collected and processed for RNA isolation. Salivary expression of miR-223-3p and miR-21-5p were measured using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curves were generated to evaluate the accuracy of diagnostic models. Machine learning algorithms, multiple logistic regression, and principal component analysis (PCA) were used to assess the predictive power of miRNAs in conjunction with clinical-demographic features. Significant upregulation of miR-223-3p and downregulation of miR-21-5p in saliva were observed in patients with gastric cancer. The area under ROC curve (AUC) values for salivary miR-21-5p, salivary miR-223-3p, and their multiple logistic regression were determined to be 0.723, 0.791, and 0.850, respectively. The AUC for multiple logistic regression model was 0.919. The PCA model led to the highest diagnostic odds ratio (DOR) of 134.33 (sensitivity = 0.785, specificity = 1.00, AUC = 903). Application of machine learning methods, and in particular a random forest algorithm, showed high accuracy in diagnosing patients with gastric cancer (sensitivity = 1.00, specificity = 0.857, AUC = 0.93). The application of validated salivary diagnostics in clinical practice could help facilitate earlier diagnosis of gastric cancer and improve medical outcome. Expression of miR-21 and miR-223-3p in saliva together with clinical and demographic features, appears promising in screening for GC.

Clinical application of oral contrast-enhanced ultrasound in evaluating the preoperative T staging of gastric cancer.

Oral contrast-enhanced ultrasound (OCEUS) is widely used in the noninvasive diagnosis and screening of gastric cancer (GC) in China. To investigate the clinical application of OCEUS in evaluating the preoperative T staging of gastric cancer. OCEUS was performed before the operation, and standard ultrasound images were retained. The depth of infiltration of GC (T-stage) was evaluated according to the American Joint Committee on Cancer 8th edition of the tumor-node-metastasis staging criteria. Finally, with postoperative pathological staging as the gold standard reference, the sensitivity, specificity, negative predictive value, positive predictive value, and diagnostic value of OCEUS T staging were evaluated. OCEUS achieved diagnostic accuracy rates of 76.6% (T1a), 69.6% (T1b), 62.7% (T2), 60.8% (T3), 88.0% (T4a), and 88.7% (T4b), with an average of 75.5%. Ultrasonic T staging sensitivity exceeded 62%, aside from T1b at 40.3%, while specificity was over 91%, except for T3 with 83.5%. The Youden index was above 60%, with T1b and T2 being exceptions. OCEUS T staging corresponded closely with pathology in T4b (kappa > 0.75) and moderately in T1a, T1b, T2, T3, and T4a (kappa 0.40-0.75), registering a concordance rate exceeding 84%. OCEUS was effective, reliable, and accurate in diagnosing the preoperative T staging of GC. As a noninvasive diagnostic technique, OCEUS merits clinical popularization.

Tuning the peroxidase-mimic activity of CuX-trithiocyanuric acid complexes for colorimetric detection of gastric cancer-associated D-amino acids

A cascade colorimetric detection of salivary D-amino acids (DAAs) holds promise for the preliminary screening diagnosis of gastric cancer. Pursuing metal-organic complexes with high peroxidase-mimic (POD-mimic) activity is crucial to enhance diagnosis efficiency. In this work, we developed a straightforward strategy in a “one-pot” process to modulate the POD-mimic activity of CuX-trithiocyanuric acid (CuX-TTCA) complexes. By adjusting the molar ratios of CuX (Cl, Br, and I) and TTCA during synthesis to modify the coordination configuration of Cu(I) in CuX-TTCA, we easily tuned their POD-mimic activities. Among them, CuCl-TTCA-2 with a feeding molar ratio of 2:1 for CuCl:TTCA exhibited remarkable POD-mimic activity attributed to its exposed catalytic active sites and efficient mass transfer ability during catalysis. Long-lived 1O2 was identified as the primary reactive oxygen intermediate. A highly sensitive and selective cascade colorimetric detection platform was developed for two typical DAAs associated with gastric cancer, namely D-proline and D-alanine, achieving limit of detection values of 1.1 and 0.8 μM, respectively. As a proof-of-concept application, our cascade detection platform demonstrated excellent specificity in distinguishing saliva samples between gastric cancer patients and healthy individuals. The outstanding selectivity and reliable outcomes from DAAs assay make our detection platform highly promising for preliminary screening diagnosis of gastric cancer and patient self-detection applications. Our straightforward strategy for tuning POD-mimic activity provides an in-depth understanding on structure-activity relationship in nanozymes, offering valuable opportunities to advance enzyme-mimic optimization for other potential applications.

Integration of bioinformatics and cellular experiments unveils the role of SYT12 in gastric cancer

ObjectiveThis study employs integrated bioinformatics analysis and in vitro cellular experiments to elucidate the role of Synaptotagmin-12 (SYT12) in the progression of gastric cancer.MethodsWe utilized databases and platforms such as Xiantao Academic Tools, UALCAN, Kaplan-Meier plotter analysis, and The Cancer Genome Atlas (TCGA) to extract datasets on SYT12 in gastric cancer. We analyzed the relationship between SYT12 expression and the clinicopathological features, prognosis, diagnosis, and immune infiltration of stomach adenocarcinoma (STAD) patients. Verification was conducted using samples from 31 gastric cancer patients. Additionally, in vitro cellular experiments were performed to determine the role and potential mechanisms of SYT12 in the malignant behavior of gastric cancer cells.ResultsComprehensive bioinformatics analysis indicated that SYT12 is highly expressed in most cancers and is associated with promoter DeoxyriboNucleic Acid (DNA) methylation levels. SYT12 expression correlated with clinicopathological features, immune cell infiltration, immune checkpoint gene expression, and poor prognosis in STAD patients. In vitro experiments suggest that SYT12 may promote the proliferation and migration of gastric cancer cells by inducing epithelial-mesenchymal transition (EMT).ConclusionsThis study highlights the significant role of SYT12 in gastric cancer, suggesting its potential as a new target for early diagnosis, treatment, immunological, and prognostic evaluation in gastric cancer, offering new insights for precision medicine in this disease.

Applications of artificial intelligence in digital pathology for gastric cancer.

Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research.

Preparation and identification of a fluorescent probe with CsPbBr3perovskite quantum dots and CD44v6 specific peptide for gastric cancer imaging.

Since the sensitivity and accuracy of traditional detection for early gastric cancer diagnosis are still insufficient, it is significant to continuously optimize the optical molecular imaging detection technology based on an endoscopic platform. The signal intensity and stability of traditional chemical fluorescent dyes are low, which hinders the clinical application of molecular imaging detection technology. This work developed a probe based on perovskite quantum dots (PQDs) and peptide ligands. By utilizing CsPbBr3perovskite PQDs modified by azithromycin (AZI), combined with the specific polypeptide ligand of CD44v6, a gastric cancer biomarker, the perovskite-based probe (AZI-PQDs probe) which can specifically identify gastric cancer tumor was prepared. Owing to the high photoluminescence quantum yield of CsPbBr3PQDs, the naked eye can observe the imaging under the excitation of the hand-held ultraviolet light source. AZI-PQDs probe can accurately identify gastric cancer cells, tissues, and xenograft models with experiments ofex vivoandin vivofluorescence imaging detection. It also exhibited low toxicity and immunogenicity, indicating the safety of the probe. This work provides a probe combined with cancer specificity and a reliable fluorescent signal that has the potential for application in gastric cancer optical molecular imaging.

Enhancing Preoperative Diagnosis Accuracy of Stage III Gastric Cancer with Circulating circRNAs.

The prognosis remains poor for stage III gastric cancer, and neoadjuvant chemotherapy is increasingly used to improve outcomes. Accurate diagnosis prior to treatment is essential to develop appropriate treatment strategies for poor prognosis subgroups. This study aims to enhance the accuracy of pre-treatment gastric cancer diagnosis using a biological approach centered on circulating circular RNA (circRNA). We conducted a comprehensive analysis of circRNA expression profiles using two Gene Expression Omnibus datasets to identify circRNA candidates associated with stage III gastric cancer. Subsequently, we validated these circRNA biomarkers in two independent clinical cohorts comprising a total of 174 patients with gastric cancer and non-disease controls through real-time polymerase chain reaction (PCR). Genome-wide circRNA analysis identified a panel of four biomarkers capable of diagnosing pathologically confirmed stage III (pStage III) gastric cancer. In a training cohort (n = 83), a clinically applicable panel of four circRNAs was developed (AUC 0.81), which was successfully validated in an independent clinical cohort (n = 82; AUC 0.76). To assess clinical utility, we combined clinical imaging (cStage) with the circRNA panel. Among those initially diagnosed as cStage III but later confirmed as pStage I/II, 86% were accurately diagnosed using the molecular biological approach with circRNAs. We have developed a circRNA-based non-invasive liquid biopsy that can improve the diagnostic performance of pStage III gastric cancer before treatment. Our circRNA model could provide a sophisticated and personalized approach to assist in treatment planning for patients with advanced gastric cancer.

Efficacy of ursodeoxycholic acid for bile reflux after distal gastrectomy in patients with gastric cancer: A secondary analysis of the PEGASUS-D randomized clinical trial.

Few studies have been conducted on the prevention of bile reflux in gastric cancer patients who have undergone gastrectomy. The aim of this study was to evaluate the efficacy and safety of ursodeoxycholic acid (UDCA) in preventing bile reflux after gastrectomy in patients with gastric cancer. This study was a secondary analysis of the PEGASUS-D trial, a randomized, double-blind, placebo-controlled clinical trial. Adults with a diagnosis of gastric cancer who underwent gastrectomy were enrolled. Eligible participants were randomly assigned to receive 300mg of UDCA, 600mg of UDCA, or placebo at a ratio of 1:1:1. UDCA and placebo were administered daily for 52 weeks. The primary outcomes included bile reflux symptoms at each time point, percentage of participants with bile reflux, and the grade of gastritis. Among 521 participants who underwent randomization, 151, 164, and 150 participants were analyzed from the 300 mg UDCA, 600mg UDCA, and placebo groups, respectively. The difference in symptoms between the three groups was not significant. Bile reflux was less evident in the UDCA group than in the placebo group; however, this difference was significant only in the 300-mg group at 12 months post-operation (odds ratio, 0.44; P = 0.0076). A significant reduction in gastritis was also observed in the 300-mg group at 12 months post-operation (odds ratio, 0.50; P = 0.0368) compared to the placebo group. This study showed that UDCA administration significantly reduced bile reflux and gastritis by approximately 50% at the 12 months-postoperative follow-up in patients who underwent gastrectomy for gastric cancer.

Identification of FGG as a Biomarker in Early Gastric Cancer via Tissue Proteomics and Clinical Verification.

Early and accurate diagnosis of gastric cancer (GC) is essential for reducing mortality and improving patient well-being. However, methods for the early diagnosis of GC are still lacking. In this study, by isobaric tagging for relative and absolute quantitation (iTRAQ), we identified 336 proteins that overlapped among the upregulated differentially expressed proteins (DEPs) in early gastric cancer (EGC) versus progressive gastric cancer (PGC), upregulated DEPs in EGC versus nongastric cancer (NGC), and nonsignificant proteins in EGC versus NGC. These DEPs were involved primarily in the neutrophil-related immune response. Network analysis of proteins and pathways revealed that fibrinogen α (FGA), β (FGB), and γ (FGG) are candidates for distinguishing EGC. Furthermore, parallel reaction monitoring (PRM), immunohistochemistry (IHC), and Western blot (WB) assays of clinical samples confirmed that, compared with that in PGC and NGC, only FGG was uniquely and significantly upregulated in the gastric mucosa of EGC. Our results demonstrated that FGG in the gastric mucosa could be a novel biomarker to diagnose EGC patients via endoscopy.

Construction and interpretation of machine learning-based prognostic models for survival prediction among intestinal-type and diffuse-type gastric cancer patients

BackgroundGastric cancer is one of the most common malignant tumors worldwide, with high incidence and mortality rates, and it has a complex etiology and complex pathological features. Depending on the tumor type, gastric cancer can be classified as intestinal-type and diffuse-type gastric cancer, each with distinct pathogenic mechanisms and clinical presentations. In recent years, machine learning techniques have been widely applied in the medical field, offering new perspectives for the diagnosis, treatment, and prognosis of gastric cancer patients.MethodsThis study recruited 2158 gastric cancer patients and constructed prognostic prediction models for both intestinal-type and diffuse-type gastric cancer. Clinical pathological data were collected from patients, and machine learning algorithms were used for feature selection and model construction. The performance of the models was validated with training and testing datasets. The Shapley additive explanations (SHAP) values were used to interpret the model predictions and identify the main factors that influence patient survival.ResultsIn the prognostic model for intestinal-type gastric cancer, the gradient boosting decision tree (GBDT) model demonstrated the best performance, with key features including pTNM, CA125, tumor size, CA199, and PALB. Similarly, in the prognostic model for diffuse-type gastric cancer, the GBDT model was utilized, with key features comprising pTNM, Borrmann type IV disease, lymphocyte (LYM), lactate dehydrogenase (LDH), potassium (K), perineural invasion (PNI), tumor size, and whole stomach location. Risk stratification analysis revealed that the prognosis of high-risk patients was significantly worse than that of low-risk patients.ConclusionMachine learning shows great potential in predicting survival outcomes of gastric cancer patients, providing strong support for the development of personalized treatment plans.

Enantiospecific profiling of D-amino acid for gastric cancer diagnosis by using a biocatalytic MHOF nanoreactor

The dynamic variation of D-amino acids (D-AAs) in human saliva has great potential as the indicator for individual physiological states and disease progression; however, enantiospecific profiling of D-AAs is a great challenge. In this work, a nanoreactor based on mesoporous hydrogen-bonded organic framework (MHOF) has been fabricated for the enantiospecific profiling of D-AAs. The nanoreactor is prepared by using MHOFs as the scaffold to organize the biocatalytic cascade of D-AA oxidase (DAAO) and horseradish peroxidase (HRP). In the meanwhile, the large, stable, and robust transport channels of MHOFs allow the unimpeded transport of the involved biocatalytic substrates. Once D-AA substrates efficiently transport into MHOFs, the chromogenic reactions of 3,3′,5,5′-tetramethylbenzidine (TMB) and H2O2 can be activated. So, using this MHOF nanoreactor, enantiospecific and visual detection of D-AAs can be achieved. Moreover, the stable nanoreactor can be operated in complex solution and utilized for the profiling of D-AA levels in saliva samples collected from gastric cancer patients, suggesting that D-AAs may be a powerful fingerprint for the diagnosis of gastric cancer. Therefore, the fabricated MHOF-based nanoreactor may provide an emerging tool for the enantiospecific profiling of D-AAs and supports the D-AA fingerprint as the indicator of gastric cancer diagnosis.

Identification of plasma exosomal lncRNA as a biomarker for early diagnosis of gastric cancer.

There were about 1,090,000 gastric cancer (GC) cases in 2020 in China. The incidence and mortality rates ranked the fifth and third among all kinds of cancers in China. Early diagnosis plays an important role in the treatment and prognosis of gastric cancer. In recent years, noninvasive diagnosis, especially plasma exosome lncRNAs, has become a promissing biomarkers with high specificity and sensitivity for early diagnosis of cancers. In this study, plasma exosomes of patients with early gastric cancer were extracted efficiently by affinity membrane separation technology, including affinity adsorption, elution, affinity membrane regeneration and other steps. After identified by electron microscopy observation, particle size analysis and Western blot verification, the lncRNAs in the exosomes were extracted and were analysized by high-throughput RNA sequencing (RNA-Seq). The differentially expressed lncRNAs were verified by RT-qPCR in 93 patients with early gastric cancer and 49 normal controls. Electron microscopy, particle size analysis and Western blot showed that exosomes were successfully isolated from plasma. RNA-Seq results show that 76 lncRNAs were upregulated and 260 lncRNAs were downregulated in plasma exosomes of early gastric cancer patients compared with normal controls. RT-qPCR analysis indicated that a total of 6 lncRNAs were significantly and differentially expressed in gastric cancer patients compared to normal controls, with 2 (lncmstrg. 1319590, Lncmstrg. 2312697) highly expressed and 4 lowly expressed (lncmstr-g.1004024.1, lncmstrg. 2441832.8, lncmstrg. 315376.1, lncmstrg.907985.2,) (p < 0.05). The survival curve analysis indicated that lncmstrg.2441832.8 and lncmstrg.2312697 had higher sensitivity and specificity for the diagnosis of gastric cancer, respectively and AUC curve areas were 0.6211 and 0.631, p < 0.05, respectively, which were greater than the traditional clinical detection indexes CEA (0.61) and AFP (0.57). When combined lncmstrg.2441832.8 and lncmstrg.2312697 in gastric cancer diagnosis, AUC curve area reached 0.73, which was greater than CA199 (0.71). Lncmstrg.2441832.8 and lncmstrg.2312697 may be a potential and promissing biomarkers for early diagnosis of gastric cancer.

The importance of blood parameters in the detection of intestinal metaplasia and early diagnosis of gastric cancer

Background: We aimed to determine whether complete blood count(CBC) parameters, such as the white blood count(WBC), hemoglobin(Hb), platelet(PLT), red cell distribution width(RDW), mean platelet volume(MPV), platelet distribution width(PDW), neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), and monocyte-to-lymphocyte ratio(MLR), have a predictive value in the detection of gastric cancer(GC) and intestinal metaplasia(IM). Methods: While proven GC, IM, and healthy control(HC) patients were included, patients with the comorbid disease were excluded, and univariate analyses compared three groups. The receiver operating characteristic(ROC) curve analysis was run for CBC parameters. The area under the curve(AUC) was evaluated, and a cut-off value was determined. The sensitivity and specificity of each parameter were considered. Results: The GC, IM, and HC groups consisted of 72(33%), 73(34%), and 72(33%) patients, respectively. RDW, PLT, NLR, PLR, and MLR were significant between GC and IM. The highest AUC (0.727) was obtained for the PLT yielding a 56.9% sensitivity and 79.4% specificity at a cut-off value of 151.8. The AUC of RDW was found as 0.691 and 0.626 for pairwise comparisons of GC-HC and IM-HC, respectively. At a cut-off value of 13.4, PLR yielded 70.8% sensitivity and 61.1% specificity in the detection of GC, while 64.4% sensitivity and 51.1% specificity for IM. Conclusion: CBC parameters, such as RDW, PLT, NLR, PLR, and MLR, have value in detecting GC. RDW also has diagnostic value in helping to detect IM. PLR can help to distinguish patients with GC from those with IM. These inexpensive, easily accessible parameters may help in the timely diagnosis of GC and IM. Keywords: Gastric cancer; intestinal Metaplasia; Monocyte to lymphocyte ratio; red cell distribution; hemoglobin; platelet; neutrophil-lymphocyte ratio; platelet-lymphocyte ratio; inflammation.

Clinical and pathological characteristics of gastric large cell neuroendocrine carcinoma: A report of 2 cases series and literature review.

Gastric large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive neuroendocrine carcinoma that arises from the stomach and with high malignancy. Patients with gastric LCNEC usually have a poor prognosis. The standard treatment plan has not been established and its curative effect is poor. The present study described 2 cases diagnosed with gastric LCNEC and reviewed in depth the literature to improve our understanding more about this uncommon tumor and further to provide more experience to diagnose and treat this disease. The present study reported 2 cases of gastric LCNEC in male patients aged 51 and 73 years old, respectively. Both patients had epigastric discomfort, pain, acid reflux and heartburn. The medical history was unremarkable. The ulcerative lesion located at gastric was examined in the 2 patients by taking the esophagogastroduodenoscopy (EGO), computed tomography (CT) and digital gastrointestinal radiography (GI), that both were suspected gastric malignancy. Endoscopic biopsies of the tumor led to the initial diagnosis of gastric cancer. Postoperative pathological and immunohistochemical examinations of the surgical specimens confirmed that 1 case had mixed adeno-neuroendocrine carcinoma (MANEC) and the other had LCNEC. Both patients underwent surgical resection and received etoposide-cisplatin combination chemotherapy following surgery. The operation was successful. Both patients had uneventful recoveries following surgery, and had been followed-up regularly. The general condition was satisfactory, and no tumor metastasis was observed at present.

Expression of Serum DCLK1 in Gastric Cancer Patients and Its Relationship with CEA, CA19-9, and CA72-4

Objective: To investigate the expression of Doublecortin-like kinase-1 (DCLK1) in the serum of gastric cancer patients and its relationship with carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), and carbohydrate antigen 72-4 (CA72-4). Methods: Fifty patients diagnosed with gastric cancer at the hospital from January to December 2021 were selected as the gastric cancer group, and 50 patients diagnosed with chronic atrophic gastritis during the same period were selected as the control group. The serum concentrations of DCLK1, CEA, CA19-9, and CA72-4 were measured in both groups. Cut-off values and AUC (Area Under the Curve) were determined based on the ROC curve, and the expression of DCLK1 and its relationship with CEA, CA19-9, and CA72-4 were analyzed. Results: The average concentrations of DCLK1, CEA, CA19-9, and CA72-4 in the serum of gastric cancer patients were significantly higher than those in the control group (P &lt; 0.05). CA72-4 had the highest sensitivity (62%), CEA had the highest specificity (98%), and DCLK1 had the largest AUC (0.709). The combined diagnosis of gastric cancer using DCLK1, CEA, and CA19-9 resulted in the largest AUC (0.826), with a sensitivity of 82% and a specificity of 76%. Conclusion: The expression of DCLK1, CEA, CA19-9, and CA72-4 in the serum of gastric cancer patients is significantly higher than that in the control group. The combined detection of DCLK1, CEA, and CA19-9 offers better sensitivity and specificity for the diagnosis of gastric cancer.

B-357 Siemens CA 72-4: an assay for measurement of Tumor Associated Glycoprotein (TAG) 72

Abstract Background The tumor associated glycoprotein (TAG) 72, also known as CA 72-4 is a mucin protein of high molecular weight. CA 72-4 is found on the surface of many cancer cells, including stomach (gastric), ovary, breast, colon and pancreatic cells. CA 72-4 is currently one of the primary markers for the diagnosis of gastric cancer and monitoring the course and efficacy of gastric cancer. In ovarian cancer, it has a higher sensitivity for mucin- type ovarian cancer, and its combination with CA125 for detection can significantly improve the clinical sensitivity. In colorectal cancer, CA 72-4 combined with CEA detection was also found to significantly improve the sensitivity of the initial diagnosis. An assay for CA 72-4 on Siemens Atellica IM (Siemens CA 72-4) is being developed and analytical performance is being presented. Methods The Siemens CA 72-4 assay (in development) is a one-step chemiluminescent microparticle immunoassay (CMIA) for the quantitative determination of CA 72-4 in human serum and plasma. The specimen is incubated with paramagnetic microparticles coated with the monoclonal antibody (mAb) B72.3 and acridinium-labeled mAb CC49 conjugate on the Siemens Atellica IM® System. After incubation and washing, Acid and Base reagents are added. The resulting relative light units (RLUs) are directly proportional to the amount of CA 72-4 in the sample allowing for the quantitative determination of CA 72-4 in the specimen. Results The following performance were determined using 3 unique lots of reagents and calibrators with the calibration range for the assay being 0.00 to 200.00 U/mL. The limit of blank (LoB), limit of detection (LoD), and limit of quantitation at 20% CV (LoQ) were determined and met the goal of ≤ 1.00 U/mL. Linearity according to CLSI EP06 Ed2 was demonstrated for a range of 1.00 through 200.00 U/mL. A Precision study of 2 controls and 5 panels spanning the range of the assay demonstrated a total %CV ≤ 6% for samples ≥ 5.00 U/mL and ≤ 0.25 SD for samples &amp;lt;5.00 U/mL. In the sample tube type study, a matrix comparison of 23 matched serum and plasma samples were evaluated. Passing-Bablock slopes of &amp;lt; 8% and r &amp;gt; 0.99 were observed when evaluating samples within the measurement range for plasma (LiHep, LiHep separator, K2EDTA and K3EDTA) tubes and serum separator tubes (SST) compared to the Red Top serum samples. Five endogenous substances were evaluated for interference in the CA 72-4 assay. The average percent difference between test and control samples for all endogenous interferents was &amp;lt; 10%. Percent difference in the presence of 28 potentially interfering drugs was ≤ 4.0%. Conclusions The Siemens CA 72-4 assay currently in development is a sensitive and precise assay for the quantitative determination of CA 72-4 in human serum and plasma.

KNTC1 functions as a potential biomarker and oncogene regulating proliferation, migration and apoptosis in gastric cancer

BackgroundAs one of the most prevalent cancers, gastric cancer (GC) exhibits a remarkably high morbidity and mortality rate. To date, effective diagnostic and prognostic markers and therapeutic targets for GC are still lacking. Kinetochore associated 1 (KNTC1) is one of the proteins involved in chromosome segregation. However, the diagnostic and prognostic value of KNTC1 and its biological function in GC remain unknown. MethodsIn this study, Gene Expression Omnibus (GEO) datasets were utilized to identify differentially expressed genes (DEGs). Prognostic and diagnostic value were assessed by Kaplan-Meier plotter and receiver operating characteristic (ROC) curve. The expression of KNTC1 was verified by q-PCR, immunohistochemistry (IHC) and Western blotting. Subsequently, KNTC1 knockdown was employed to investigate its effect on GC cells. Gene set enrichment analysis (GSEA) revealed a pathway regulated by KNTC1, which was further verified by Western blotting. ResultsFour highly expressed genes (ESPL1, RAD54L, KNTC1, TACC3) were identified as biomarkers for GC diagnosis and prognosis. Notably, the value of KNTC1 as a biomarker for GC was newly revealed. Single-cell and immune analyses revealed that KNTC1 contributed to the suppression of the GC immune microenvironment. In clinical samples, we demonstrated high expression of KNTC1 in GC tissues. KNTC1 knockdown suppressed proliferation and migration while promoting apoptosis of GC cells. Additionally, KNTC1 may affect GC cells by regulating the PI3K/Akt/mTOR pathway. ConclusionsKNTC1 acts as a potential diagnostic and prognostic marker for GC. It may promote proliferation and migration while inhibiting apoptosis of GC cells via the PI3K/Akt/mTOR pathway.

Impact of splenectomy on long-term outcomes after gastrectomy for gastric cancer: a population-based study

BackgroundNo national studies comparing long-term survival after total or partial gastrectomy with splenectomy due to injury or oncologic reasons or spleen preservation exist. This study aimed to examine the 5-year overall survival (OS) of patients with gastric adenocarcinoma who underwent total or partial gastrectomy with splenectomy due to injury or oncologic reasons or spleen preservation in a population-based nationwide setting. MethodsThis study included all patients undergoing total or partial gastrectomy with splenectomy or spleen preservation for gastric adenocarcinoma in Finland from 2005 to 2016, with follow-up until December 31, 2019. A total of 2196 patients with gastric cancer diagnosis and total or partial gastrectomy were identified in the registries. Of these patients, 2118 were applicable for this study. Cox proportional hazard models provided hazard ratios (HRs) with 95% CIs of 5-year OS. The results were adjusted for age, sex, year of operation, comorbidities, tumor location, pathologic stage, and neoadjuvant therapy. ResultsThe observed 5-year OS rates were 38.7% in patients with no or minor spleen injury, 39.7% in patients with splenectomy due to injury, and 30.8% in patients with splenectomy due to oncologic reasons (P = .032). Patients who underwent R0 gastrectomy with splenectomy due to oncologic reasons had higher 5-year mortality (the adjusted model HR, 1.26; 95% CI, 1.01–1.56) than patients who underwent spleen preservation. ConclusionThe OS was worst in patients who underwent gastrectomy with splenectomy due to oncologic reasons, highlighting the poor prognosis in patients with advanced gastric cancer. Splenectomy due to injury does not compromise the prognosis.