examined regional LNs

ahmeterdempmk/DataMedX-Hackathon-Cancer-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by zidana harisma

Contents

Cancer data sets used in this study.

Context

These two datasets consists of a group of colorectal cancer patients, who had surgery to remove their tumour. One dataset on patient data and one of their respective gene expression levels.

Content

This dataset (crc.txt) consists of the group of colorectal cancer patients data.

The patient dataset consists of the following variables:

Age: at Diagnosis (in Years) Dukes Stage: A to D (development/progression of disease) Gender: Male or Female Location: Left, Right, Colon or Rectum DFS: Disease-free survival, months (survival without the disease returning) DFS event: 0 or 1 (with 1 = event) Adj_Radio: If the patient also received radiotherapy Adj_Chem: If the patient also received chemotherapy

The gene expression dataset (crc_ge.txt) comprises of gene expression levels for the same set of patients.

This data has been pre-processed and log2 transformed. You need not make any further transformations to the data.

Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient’s breast tissue, which has the potential for identifying and characterizing cancer subtypes. However, the large dimensionality of this transcriptomic data and the heterogeneity between the molecular profiles of breast cancers poses a barrier to identifying minimal markers and mechanistic consequences. In this study, we develop an autoencoder to identify a reduced set of gene markers that characterize the four major breast cancer subtypes with high accuracy. The reduced feature space created by our model captures the functional characteristics of each breast cancer subtype highlighting mechanisms that are unique to each subtype as well as those that are shared. Our high prediction accuracy shows that our markers can be valuable for breast cancer subtype detection. Additionally, they have the potential to provide insights into mechanisms associated with each subtype.

Contingency table.

description:

The State Cancer Profiles (SCP) web site provides statistics to help guide and prioritize cancer control activities at the state and local levels. SCP is a collaborative effort using local and national level cancer data from the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and National Cancer Institute's Surveillance, Epidemiology and End Results Registries (SEER). SCP address select types of cancer and select behavioral risk factors for which there are evidence-based control interventions. The site provides incidence, mortality and prevalence comparison tables as well as interactive graphs and maps and support data. The graphs and maps provide visual support for deciding where to focus cancer control efforts.

The State Cancer Profiles (SCP) web site provides statistics to help guide and prioritize cancer control activities at the state and local levels. SCP is a collaborative effort using local and national level cancer data from the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and National Cancer Institute's Surveillance, Epidemiology and End Results Registries (SEER). SCP address select types of cancer and select behavioral risk factors for which there are evidence-based control interventions. The site provides incidence, mortality and prevalence comparison tables as well as interactive graphs and maps and support data. The graphs and maps provide visual support for deciding where to focus cancer control efforts.

Context and Content

The first dataset (Oesophageal Cancer Clinical.csv) has clinical data of Oesophageal Carcinoma patients.

The second dataset (Oesophageal Cancer Protein.csv) has the protein expression data for the same set of patients.

The two datasets contain information on the same patients. However, the clinical dataset contains a greater number of patient records than corresponding protein expression data in the second dataset. The clinical dataset has patient_barcode as the unique identifier, whereas, in the protein expression dataset the Sample_ID is used. In both datasets, the patient_barcode can be derived as, “TCGA”. - “ tissue_source_site”. - “patient_id”, e.g. -TCGA-2H-A9GF.

Inspiration

There are a huge amount of columns in these datasets, 83 clinical and 223 protein, giving you a great ability to try derive some interesting findings of oesophageal cancer from this real dataset.

This dataset can be used to analyse genes, mutations and interesting findings relating to this type of cancer.

The migration of cancer cells is highly regulated by the biomechanical properties of their local microenvironment. Using 3D scaffolds of simple composition, several aspects of cancer cell mechanosensing (signal transduction, EMC remodeling, traction forces) have been separately analyzed in the context of cell migration. However, a combined study of these factors in 3D scaffolds that more closely resemble the complex microenvironment of the cancer ECM is still missing. Here, we present a comprehensive, quantitative analysis of the role of cell-ECM interactions in cancer cell migration within a highly physiological environment consisting of mixed Matrigel-collagen hydrogel scaffolds of increasing complexity that mimic the tumor microenvironment at the leading edge of cancer invasion. We quantitatively show that the presence of Matrigel increases hydrogel stiffness, which promotes β1 integrin expression and metalloproteinase activity in H1299 lung cancer cells. Then, we show that ECM remodeling activity causes matrix alignment and compaction that favors higher tractions exerted by the cells. However, these traction forces do not linearly translate into increased motility due to a biphasic role of cell adhesions in cell migration: at low concentration Matrigel promotes migration-effective tractions exerted through a high number of small sized focal adhesions. However, at high Matrigel concentration, traction forces are exerted through fewer, but larger focal adhesions that favor attachment yielding lower cell motility.

Proteome analysis of extracellular vesicles

ABSTRACT

It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project.

In addition, we provide synthetic data for a mixture of isotropic distributions.

Instructions:

Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients.

The summary data contains only the numerical values. The columns are the features and the rows are the observations.

Inspiration:

This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"

https://cancer.ubrite.org/hackathon-2021/

Acknowledgements

Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090.

https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data

U-BRITE last update date: 07/21/2021

Classification results of prostate cancer data set.

A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

Dataset

This dataset was created by Rohith

Contents

The global cancer registry software market was valued at USD 48 million in 2025 and is projected to grow at a CAGR of 6.4% during the forecast period of 2025-2033. The market growth is attributed to the increasing incidence of cancer, rising adoption of electronic health records, and government initiatives to improve cancer data collection and analysis. The market is segmented into application-based types: Hospitals, Government Organizations, Cancer Research Centers, and others. The cloud-based segment is expected to hold a major market share due to its cost-effectiveness, scalability, and ease of deployment. Key market players include Onco Inc., Rocky Mountain Cancer Data Systems (RMCDS), Electronic Registry Systems (ERS), McKesson, C/Net Solutions, and Elekta AB. North America is projected to dominate the market due to the presence of well-established healthcare infrastructure and high awareness regarding cancer prevention and treatment. However, the Asia-Pacific region is anticipated to witness significant growth due to the increasing incidence of cancer and government initiatives to improve cancer care.

We conducted single-cell transcriptome (scRNA-seq) and whole-exome sequencing (WES) analyses on 9 esophageal cancer patients, consisting of three cases with complement mutations and six cases without mutations, resulting in a total of 52,727 cells after quality control. The findings indicate a significant increase in the proportion of NK cells and T cells among CD45+ cells in the complement mutation group, whereas the proportion of monocytes and macrophages decreases. Furthermore, gene set enrichment analysis (GSEA) of malignant cells reveals significant enrichment of pathways such as "interferon alpha response," "KRAS signaling DN," and "interferon gamma response" in the complement mutation group.

Mortality from malignant melanoma (ICD-10 C43 equivalent to ICD-9 172). To reduce deaths from malignant melanoma. Legacy unique identifier: P00646

Dataset

This dataset was created by siddharthpunn10

Contents