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Background Gastric cancer is a considerable burden for worldwide patients. And diffuse gastric cancer is the most insidious subgroup with poor survival. The phenotypic characterization of the diffuse gastric cancer cell line can be useful for gastric cancer researchers. In this article, we aimed to characterize the diffuse gastric cancer cells with MRI and transcriptomic data. We hypothesized that gene expression pattern is associated with the phenotype of the cells and that the heterogeneous enhancement pattern and the high tumorigenicity of SNU484 can be modulated by the perturbation of the highly expressed gene. Methods We evaluated the 9.4 T magnetic resonance imaging and transcriptomic data of the orthotopic mice models from diffuse gastric cancer cells such as SNU484, Hs746T, SNU668, and KATO III. We included MKN74 as an intestinal cancer control cell. After comprehensive analysis integrating MRI and transcriptomic data, we selected CD34 and validated the effect by shRNA in the BALB/c nude mice models. Results SNU484, SNU668, Hs746T, and MKN74 formed orthotopic tumors by the 5 weeks after cell injection. The diffuse phenotype was found in the SNU484 and Hs746T. SNU484 was the only tumor showing the heterogeneous enhancement pattern on T2 images with a high level of CD34 expression. Knockdown of CD34 decreased the round-void shape in the H&E staining ( P  = 0.028), the heterogeneous T2 enhancement, and orthotopic tumorigenicity (100% vs 66.7%). The RNAseq showed that the suppressed CD34 is associated with the downregulated gene-sets of the extracellular matrix remodeling. Conclusion Suppression of CD34 in the human-originated gastric cancer cell suggests that it is important for the round-void histologic shape, heterogeneous enhancement pattern on MRI, and the growth of gastric cancer cell line.

Adjuvant chemotherapy after surgery improves survival of patients with stage II–III, resectable gastric cancer. However, the overall survival benefit observed after adjuvant chemotherapy is moderate, suggesting that not all patients with resectable gastric cancer treated with adjuvant chemotherapy benefit from it. We aimed to develop and validate a predictive test for adjuvant chemotherapy response in patients with resectable, stage II–III gastric cancer. In this multi-cohort, retrospective study, we developed through a multi-step strategy a predictive test consisting of two rule-based classifier algorithms with predictive value for adjuvant chemotherapy response and prognosis. Exploratory bioinformatics analyses identified biologically relevant candidate genes in gastric cancer transcriptome datasets. In the discovery analysis, a four-gene, real-time RT-PCR assay was developed and analytically validated in formalin-fixed, paraffin-embedded (FFPE) tumour tissues from an internal cohort of 307 patients with stage II–III gastric cancer treated at the Yonsei Cancer Center with D2 gastrectomy plus adjuvant fluorouracil-based chemotherapy (n=193) or surgery alone (n=114). The same internal cohort was used to evaluate the prognostic and chemotherapy response predictive value of the single patient classifier genes using associations with 5-year overall survival. The results were validated with a subset (n=625) of FFPE tumour samples from an independent cohort of patients treated in the CLASSIC trial (NCT00411229), who received D2 gastrectomy plus capecitabine and oxaliplatin chemotherapy (n=323) or surgery alone (n=302). The primary endpoint was 5-year overall survival. We identified four classifier genes related to relevant gastric cancer features (GZMB, WARS, SFRP4, and CDX1) that formed the single patient classifier assay. In the validation cohort, the prognostic single patient classifier (based on the expression of GZMB, WARS, and SFRP4) identified 79 (13%) of 625 patients as low risk, 296 (47%) as intermediate risk, and 250 (40%) as high risk, and 5-year overall survival for these groups was 83·2% (95% CI 75·2–92·0), 74·8% (69·9–80·1), and 66·0% (60·1–72·4), respectively (p=0·012). The predictive single patient classifier (based on the expression of GZMB, WARS, and CDX1) assigned 281 (45%) of 625 patients in the validation cohort to the chemotherapy-benefit group and 344 (55%) to the no-benefit group. In the predicted chemotherapy-benefit group, 5-year overall survival was significantly improved in those patients who had received adjuvant chemotherapy after surgery compared with those who received surgery only (80% [95% CI 73·5–87·1] vs 64·5% [56·8–73·3]; univariate hazard ratio 0·47 [95% CI 0·30–0·75], p=0·0015), whereas no such improvement in 5-year overall survival was observed in the no-benefit group (72·9% [66·5–79·9] in patients who received chemotherapy plus surgery vs 72·5% [65·8–79·9] in patients who only had surgery; 0·93 [0·62–1·38], p=0·71). The predictive single patient classifier groups (chemotherapy benefit vs no-benefit) could predict adjuvant chemotherapy benefit in terms of 5-year overall survival in the validation cohort (pinteraction=0·036 in univariate analysis). Similar results were obtained in the internal evaluation cohort. The single patient classifiers validated in this study provide clinically important prognostic information independent of standard risk-stratification methods and predicted chemotherapy response after surgery in two independent cohorts of patients with resectable, stage II–III gastric cancer. The single patient classifiers could complement TNM staging to optimise decision making in patients with resectable gastric cancer who are eligible for adjuvant chemotherapy after surgery. Further validation of these results in prospective studies is warranted. Ministry of ICT and Future Planning; Ministry of Trade, Industry, and Energy; and Ministry of Health and Welfare.

Ferroptosis is an iron-dependent regulated necrosis mediated by lipid peroxidation. Cancer cells survive under metabolic stress conditions by altering lipid metabolism, which may alter their sensitivity to ferroptosis. However, the association between lipid metabolism and ferroptosis is not completely understood. In this study, we found that the expression of elongation of very longchain fatty acid protein 5 (ELOVL5) and fatty acid desaturase 1 (FADS1) is up-regulated in mesenchymal-type gastric cancer cells (GCs), leading to ferroptosis sensitization. In contrast, these enzymes are silenced by DNA methylation in intestinal-type GCs, rendering cells resistant to ferroptosis. Lipid profiling and isotope tracing analyses revealed that intestinal-type GCs are unable to generate arachidonic acid (AA) and adrenic acid (AdA) from linoleic acid. AA supplementation of intestinal-type GCs restores their sensitivity to ferroptosis. Based on these data, the polyunsaturated fatty acid (PUFA) biosynthesis pathway plays an essential role in ferroptosis; thus, this pathway potentially represents a marker for predicting the efficacy of ferroptosis-mediated cancer therapy.

Arachidonic and adrenic acids in the membrane play key roles in ferroptosis. Here, we reveal that lipoprotein-associated phospholipase A2 (Lp-PLA2) controls intracellular phospholipid metabolism and contributes to ferroptosis resistance. A metabolic drug screen reveals that darapladib, an inhibitor of Lp-PLA2, synergistically induces ferroptosis in the presence of GPX4 inhibitors. We show that darapladib is able to enhance ferroptosis under lipoprotein-deficient or serum-free conditions. Furthermore, we find that Lp-PLA2 is located in the membrane and cytoplasm and suppresses ferroptosis, suggesting a critical role for intracellular Lp-PLA2. Lipidomic analyses show that darapladib treatment or deletion of PLA2G7 , which encodes Lp-PLA2, generally enriches phosphatidylethanolamine species and reduces lysophosphatidylethanolamine species. Moreover, combination treatment of darapladib with the GPX4 inhibitor PACMA31 efficiently inhibits tumour growth in a xenograft model. Our study suggests that inhibition of Lp-PLA2 is a potential therapeutic strategy to enhance ferroptosis in cancer treatment. Ferroptosis is an iron-dependent cell death mechanism that is emerging as a target for cancer therapy. Here, the authors find that lipoprotein-associated phospholipase A2 modulates ferroptosis resistance by controlling phospholipid metabolism.

This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P  < 0.001 [internal validation]; 0.652, P  = 0.010 [external validation]) and the merged model (0.719, P  < 0.001; 0.651, P  = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

Successful development of ultra-sensitive molecular imaging nanoprobes for the detection of targeted biological objects is a challenging task. Although magnetic nanoprobes have the potential to perform such a role, the results from probes that are currently available have been far from optimal. Here we used artificial engineering approaches to develop innovative magnetic nanoprobes, through a process that involved the systematic evaluation of the magnetic spin, size and type of spinel metal ferrites. These magnetism-engineered iron oxide (MEIO) nanoprobes, when conjugated with antibodies, showed enhanced magnetic resonance imaging (MRI) sensitivity for the detection of cancer markers compared with probes currently available. Also, we successfully visualized small tumors implanted in a mouse. Such high-performance, nanotechnology-based molecular probes could enhance the ability to visualize other biological events critical to diagnostics and therapeutics.

Birefringence, an inherent characteristic of optically anisotropic materials, is widely utilized in various imaging applications ranging from material characterizations to clinical diagnosis. Polarized light microscopy enables high-resolution, high-contrast imaging of optically anisotropic specimens, but it is associated with mechanical rotations of polarizer/analyzer and relatively complex optical designs. Here, we present a form of lens-less polarization-sensitive microscopy capable of complex and birefringence imaging of transparent objects without an optical lens and any moving parts. Our method exploits an optical mask-modulated polarization image sensor and single-input-state LED illumination design to obtain complex and birefringence images of the object via ptychographic phase retrieval. Using a camera with a pixel size of 3.45 μm, the method achieves birefringence imaging with a half-pitch resolution of 2.46 μm over a 59.74 mm 2  field-of-view, which corresponds to a space-bandwidth product of 9.9 megapixels. We demonstrate the high-resolution, large-area, phase and birefringence imaging capability of our method by presenting the phase and birefringence images of various anisotropic objects, including a monosodium urate crystal, and excised mouse eye and heart tissues.

Localized surface plasmon resonance (LSPR) is induced by incident light when it interacts with noble metal nanoparticles that have smaller sizes than the wavelength of the incident light. Recently, LSPR-based nanobiosensors were developed as tools for highly sensitive, label-free, and flexible sensing techniques for the detection of biomolecular interactions. In this paper, we describe the basic principles of LSPR-based nanobiosensing techniques and LSPR sensor system for biomolecule sensing. We also discuss the challenges using LSPR nanobiosensors for detection of biomolecules as a biomarker.

Gastric cancer (GC) is recognized as the fifth most prevalent malignant tumor worldwide. It is characterized by diverse clinical symptoms, treatment responses, and prognoses. In GC prognosis, the promotion of epithelial–mesenchymal transition (EMT) fosters cancer cell invasion and metastasis, thereby triggering the dissemination of tumor cells. This study proposes a nucleic acid amplification circuit‐based hydrogel (NACH) assay for identifying exosomal miRNA derived from metastatic GC. The NACH assay employs the rolling circle amplification method and targets miRNA‐21, a tumor‐related oncogene, and miRNA‐99a, which promotes EMT. Specific amplification probes for each target are immobilized within the hydrogel, enabling a streamlined, one‐step amplification reaction. The NACH assay exhibits a detection limit of 1 fm for miRNA‐21 and miRNA‐99a, thereby enabling rapid and highly sensitive on‐site detection. Performance evaluation using exosomal miRNA extracted from cell culture media, mouse plasma, and human plasma revealed fluorescence intensity patterns similar to those obtained in qRT‐PCR. Furthermore, deploying a custom‐developed portable fluorometer for the NACH assay allows for diagnostic performance assessment and point‐of‐care testing using clinical samples from GC patients. These findings emphasize the potential of the NACH assay to be used as a robust tool for the genetic diagnosis of GC based on exosome detection. The Nucleic Acid Amplification Circuit‐Based Hydrogel (NACH) assay is developed to detect metastatic gastric cancer (GC)‐related exosomal miRNAs biomarkers. This assay can analyze exosomal miRNA expression levels in a one‐step, and its performance has been demonstrated using blood samples from GC patients at both early and metastatic stages. The NACH assay shows potential as a diagnostic tool for liquid biospy.

Background Driver genes of GBM may be crucial for the onset of isocitrate dehydrogenase ( IDH )-wildtype (WT) glioblastoma (GBM). However, it is still unknown whether the genes are expressed in the identical cluster of cells. Here, we have examined the gene expression patterns of GBM tissues and patient-derived tumorspheres (TSs) and aimed to find a progression-related gene. Methods We retrospectively collected primary IDH -WT GBM tissue samples ( n  = 58) and tumor-free cortical tissue samples (control, n  = 20). TSs are isolated from the IDH -WT GBM tissue with B27 neurobasal medium. Associations among the driver genes were explored in the bulk tissue, bulk cell, and a single cell RNAsequencing techniques (scRNAseq) considering the alteration status of TP53 , PTEN , EGFR , and TERT promoter as well as MGMT promoter methylation. Transcriptomic perturbation by temozolomide (TMZ) was examined in the two TSs. Results We comprehensively compared the gene expression of the known driver genes as well as MGMT , PTPRZ1 , or IDH1 . Bulk RNAseq databases of the primary GBM tissue revealed a significant association between TERT and TP53 (p < 0.001, R = 0.28) and its association increased in the recurrent tumor (p  < 0.001, R = 0.86). TSs reflected the tissue-level patterns of association between the two genes (p < 0.01, R = 0.59, n = 20). A scRNAseq data of a TS revealed the TERT and TP53 expressing cells are in a same single cell cluster. The driver-enriched cluster dominantly expressed the glioma-associated long noncoding RNAs. Most of the driver-associated genes were downregulated after TMZ except IGFBP5 . Conclusions GBM tissue level expression patterns of EGFR , TERT , PTEN , IDH1 , PTPRZ1 , and MGMT are observed in the GBM TSs. The driver gene-associated cluster of the GBM single cells were enriched with the glioma-associated long noncoding RNAs.