Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,349
datasets available to search
ShareScore release 0.9.0
Dataset results
2,349 results for “gastric cancer”
Data from: Positive cytoplasmic UCHL5 tumor expression in gastric cancer is linked to improved prognosis
Gastric cancer is the second most common cause of cancer-related mortality worldwide. Accurate prediction of disease progression is difficult, and new biomarkers for clinical use are essential. Recently, we reported that the proteasome-associated deubiquitinating enzyme UCHL5/Uch37 is a new prognostic marker in both rectal cancer and pancreatic ductal adenocarcinoma. Here, we have assessed by immunohistochemistry UCHL5 tumor expression in gastric cancer. The study cohort comprised 650 patients, who underwent surgery in Helsinki University Hospital, Finland, between 1983 and 2009. We investigated the association of cytoplasmic UCHL5 tumor expression to assess clinicopathological parameters and patient survival. Positive cytoplasmic UCHL5 tumor immunoexpression is linked to increased survival of patients with small (<5 cm) tumors (p = 0.001), disease stages I-II (p = 0.025), and age 66 years or older (p = 0.037). UCHL5 is thus a potential marker in gastric cancer with new prognostic relevance.
Causality of 486 human blood metabolites on gastric cancer: a two-sample Mendelian randomization study
Open the record for dataset details and reuse information.
Gastric Cancer Spatial Transcriptomics
Open the record for dataset details and reuse information.
The HE images of 12 patients with Gastric cancer;
<p>Gastric cancer tissues and paired normal tissues were subjected to hematoxylin and eosin staining.</p>
Fig. 6 in Cytotoxicity of methanolic extract of Swertia petiolata against gastric cancer cell line SNU-5 is via induction of apoptosis ⁎
Fig. 6. LC–MS chromatogram of methanolic extract of S. petiolata.
Table 2 in Cytotoxicity of methanolic extract of Swertia petiolata against gastric cancer cell line SNU- 5 is via induction of apoptosis ⁎
<p><b>Table 2</b> LC–MS identification of major constituents of methanolic extract of <i>Swertia petiolata</i>. The retention time peak numbers are as per Fig. 6.</p><table><tbody><tr><th>Peak no.</th><th>Retention time (minutes)</th><th>M-H</th><th>M + H</th><th>Molecular weight</th><th>Compound</th></tr></tbody><tbody><tr><th>1</th><td>7.6</td><td>355</td><td>–</td><td>354</td><td>Chlorogenic acid</td></tr><tr><th>2</th><td>9.9</td><td>163</td><td>165</td><td>164</td><td><i>p</i> -Coumaric acid</td></tr><tr><th>3</th><td>11.2</td><td>–</td><td>455</td><td>456</td><td>Ursolic acid</td></tr><tr><th>4</th><td>12.4</td><td>–</td><td>465</td><td>464</td><td>Myrecetin 3- <i>O</i> -rahamnoside</td></tr><tr><th>5</th><td>15.1</td><td>326</td><td>–</td><td>327</td><td>Unidentified</td></tr><tr><th>6</th><td>15.9</td><td>433</td><td>435</td><td>434</td><td>Quercetin 3-arabinoside</td></tr><tr><th>7</th><td>18.5</td><td>447</td><td>–</td><td>448</td><td>Kaempherol 3- <i>O</i> -glucoside</td></tr><tr><th>8</th><td>21.3</td><td>271</td><td>273</td><td>272</td><td>Naringenin</td></tr><tr><th>9</th><td>22.4</td><td>269</td><td>–</td><td>270</td><td>Genistein</td></tr><tr><th>10</th><td>26.2</td><td>315</td><td>–</td><td>316</td><td>Isorhamnetin</td></tr><tr><th>11</th><td>28.7</td><td>287</td><td>289</td><td>288</td><td>Swerchirin</td></tr></tbody></table>
Table 1 in Cytotoxicity of methanolic extract of Swertia petiolata against gastric cancer cell line SNU- 5 is via induction of apoptosis ⁎
<p><b>Table 1</b> IC 50 of methanolic extract of <i>S</i>. <i>petiolata</i> against different cancer cell lines determined by MTT assay.</p><table><tbody><tr><th>Cell line</th><th>IC50 (μg/ml)</th></tr></tbody><tbody><tr><th>Human lung cancer cell line A-549</th><td>75</td></tr><tr><th>Human prostate cancer cell line PC-3</th><td>125</td></tr><tr><th>Human breast cancer cell line MCF-7</th><td>75</td></tr><tr><th>Human gastric cancer cell line SNU-5</th><td>64</td></tr><tr><th>Human pancreas cancer cell line MiaPaca-2</th><td>75</td></tr><tr><th>Human normal cell line fR2</th><td>250</td></tr></tbody></table>
CircNRIP1 drives the malignant phenotypes in gastric cancer through mediating the miR-148b-5p/CYR61 axis
<p><b>Background: </b>Gastric cancer (GC) is a frequent disease with a poor prognosis worldwide. Circular RNAs (circRNAs) are considered to be important regulators that mediate the occurrence and development of cancers, including GC. However, the regulatory mechanism of circRNAs in GC progression is not fully understood.</p> <p><b>Methods: </b>The expression of circular RNA nuclear receptor-interacting protein 1 (circNRIP1), microRNA (miR)-148b-5p and Cysteine-rich 61 (CYR61) was gauged using quantitative real-time polymerase chain reaction (qRT-PCR). The stability of circNRIP1 was determined by Ribonuclease R (RNase R) assay.</p> <p><b>Results:</b> CircNRIP1 was upregulated in GC tissues and cells, and it was a stable circular RNA. CircNRIP1 overexpression facilitated cell proliferation, cycle progression, colony formation, invasion, migration and angiogenesis, while circNRIP1 knockdown exhibited opposite effects in GC cells. Mechanistically, miR-148b-5p could mediate the regulation of circNRIP1 on GC cell progression by serving as a target of circNRIP1. Furthermore, miR-148b-5p inhibited GC cell malignant behaviors by sponging CYR61. Meanwhile, circNRIP1 could regulate CYR61 expression via downregulating miR-148b-5p. Besides, circNRIP1 knockdown suppressed tumor growth in xenograft models.</p> <p><b>Conclusion:</b> CircNRIP1 contributed to GC progression through regulating the miR-148b-5p/CYR61 axis, hinting that circNRIP1 might be a new target for GC treatment.</p>
Multivisceral Resection for Locally Advanced Gastric Cancer: A Systematic Review and Evi-dence Quality Assessment
<p>Dataset concerning the systematic review of locally advanced gastric cancer.</p>
Robust model training strategy via hard negative mining in the weakly labeled dataset for lymphatic invasion in gastric cancer
<p>Gastric cancer poses a significant public health concern, emphasizing the need for accurate evaluation of lymphatic invasion (LI) for determining prognosis and treatment options. However, this task is time-consuming, labor-intensive, and prone to intra- and inter-observer variability. Furthermore, the scarcity of annotated data presents a challenge, particularly in the field of digital pathology. Therefore, there is a demand for an accurate and objective method to detect LI using a small dataset, benefiting pathologists. In this study, we trained convolutional neural networks (CNNs) to classify LI using a four-step training process: (1) weak model training; (2) identification of false positives; (3) hard negative mining in a weakly labeled dataset; and (4) strong model training. To overcome the lack of annotated datasets, we applied a hard negative mining approach in a weakly labeled dataset, which contained only final diagnostic information, resembling the typical data found in hospital databases, and improved classification performance. Ablation studies were performed to simulate the lack of datasets and severely unbalanced datasets, further confirming the effectiveness of our proposed approach. Notably, our results demonstrated that despite the small number of annotated datasets, efficient training was achievable, with the potential to extend to other image classification approaches used in the field of medicine.</p><p> </p><p><strong>Key words: Artificial intelligence, Computational pathology, Gastric cancer, Lymphatic invasion, Hard negative mining</strong></p>
Study Of Sunitinib In Combination With Cisplatin And 5-Fluorouracil In Patients With Advanced Gastric Cancer
ClinicalTrials.gov study NCT00555672. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Lapatinib in Combination With Weekly Paclitaxel in Patients With ErbB2 Amplified Advanced Gastric Cancer
ClinicalTrials.gov study NCT00486954. IPD Sharing: Not stated. Countries: 4. Publications: 0.
Nab-paclitaxel Combined With Cadonilimab (AK104) for the Second-line Treatment of Advanced Gastric Cancer
ClinicalTrials.gov study NCT06349967. IPD Sharing: NO. Countries: 0. Publications: 1.
FLX475 Combined With Pembrolizumab in Patients With Advanced or Metastatic Gastric Cancer
ClinicalTrials.gov study NCT04768686. IPD Sharing: Not stated. Countries: 1. Publications: 0.
S1201: Combination Chemo for Patients W/Advanced or Metastatic Esophageal, Gastric, or Gastroesophageal Junction Cancer
ClinicalTrials.gov study NCT01498289. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Pemetrexed Plus Cisplatin in Advanced Gastric Cancer
ClinicalTrials.gov study NCT00320515. IPD Sharing: Not stated. Countries: 4. Publications: 0.
SOX Combined With Tislelizumab and LDRT for Neoadjuvant Treatment of Locally Advanced Gastric Cancer
ClinicalTrials.gov study NCT06266871. IPD Sharing: NO. Countries: 0. Publications: 1.
Biomarker Analysis of HIPEC Combined With PD1/PDL1 Inhibitor for Gastric Cancer With Peritoneal Metastasis
ClinicalTrials.gov study NCT05661110. IPD Sharing: NO. Countries: 0. Publications: 3.
Combination Chemotherapy as First-Line Therapy in Treating Patients With Stage IV Gastric Cancer That Cannot Be Removed By Surgery
ClinicalTrials.gov study NCT00448682. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Intraperitoneal Chemotherapy and Systemic Chemotherapy Versus Systemic Chemotherapy After Curative Resection of Serosa-positive Gastric Cancer
ClinicalTrials.gov study NCT02205008. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.