I need more gene expression data to supplement the data from the small number of samples in my study. I want microarray and/or RNA-seq data from Pheochromocytomas and Paragangliomas samples. I will use this data to identify cancer subtypes and genes that are differentially expressed between metastatic and non-metastatic cases.
I want to identify germline variants associated with neuroblastoma cases. I will identify SNPs and insertion/deletion variants in cancer predisposition genes. For this, I want WGS or WES data from normal tissue samples taken from patients with neuroblastomas.
I want to understand the role of the protein MCL-1 in lung cancer, and would like to investigate the relationship between MCL-1 gene copy number and mRNA expression levels in lung cancer samples.
In two types of brain stem cell lines, we observed differential gene expression of FOSL1. We would like to test whether this pattern is also observed between brain cancer types in patient samples. For this, we want gene expression data from glioma patient samples.
We plan to use machine learning to identify critical differentially expressed genes in colorectal cancer. We want whole-transcriptome profiling datasets from CRC samples. We would also like patient data if available to characterize our study sample.
I am looking for whole genome or whole exome sequencing data, as well as RNA sequencing data, from normal tissue and tumor samples from Neuroblastoma patient cases. I am investigating germline DNA variations in cancer predisposition genes associated with Neuroblastoma risk.
I want access to BAM files from whole transcriptome (RNA-Seq), whole exome (WESeq), and whole genome (WGSeq) sequencing data across all pediatric cancer types, including Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Neuroblastoma (NBL), Kidney Tumors (WT, RT, CCSK), and Osteosarcoma (OS). I plan to use this data to detect expression and splice junctions in pediatric tumors and identify genetic variants affecting splicing, with the aim of studying associations with clinical factors and outcomes. 
We are seeking genomic variation (SNP data) and protein abundance data from cancer samples from the same patients to conduct genome-wide association studies (GWAS) and generate protein quantitative trait loci (pQTLs). Ultimately, we aim to utilize proteome-wide Mendelian randomization to investigate potential causal links between various proteins and the development or progression of cancer.
