The NCBI uses a search term syntax which can be associated with a
specific search field enclosed by a pair of square brackets. So, for
instance "Homo sapiens[ORGN]" denotes a search for
Homo sapiens in the "Organism" field. Details
see https://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html.
We can use the same term to query our desirable results in
geo_search(). geo_search() will parse the
searching results and return a data.frame object containing
all the records based on the search term. The internal of
geo_search() is based on rentrez
package, which provides functions working with the NCBI Eutils API,
so we can utilize NCBI API key to increase the searching
speed, details see https://docs.ropensci.org/rentrez/articles/rentrez_tutorial.html#rate-limiting-and-api-keys.
Providing we want GSE GEO records related
to human diabetes, we can get these records by
following code, the returned object is a data.frame:
diabetes_gse_records <- geo_search(
"diabetes[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
#> ■■■■■■■■■ 500/1893 [323/s] | ETA: 4s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■ 1500/1893 [341/s] | ETA: 1s
#> Get records from NCBI for 1893 queries in 5.7s
#>
#> → Parsing GEO records
head(diabetes_gse_records[1:5])
#> Title
#> 1 Calpain inhibition prevents high-glucose-induced autophagy blockade and mitochondrial fragmentation in endothelial cells and preserves vascular functions in diabetic mice
#> 2 Nonviral delivery of chemically modified tRNA rescues nonsense mutations
#> 3 Micro RNA expression profiling using NanoString technology from IgA Nephropathy patients and Non-IgA nephropathy patients from Indian cohort
#> 4 Protective IFIH1 variant reduces islet stress and dysfunction in a type 1 diabetes genetic background
#> 5 Single-nucleus and bulk transcriptomic atlas of human visceral adipose tissue across metabolic disease states identifies THBS1 as a fibro-inflammatory driver
#> 6 Engineering the insulin signal peptide to protect human pancreatic beta cells from autoimmune destruction
#> Summary
#> 1 Endothelial dysfunction is a major contributor to diabetic vascular complications, yet the mechanisms linking hyperglycemia to impaired endothelial homeostasis remain insufficiently defined. This study identifies calpains as key regulators of endothelial autophagy and mitophagy under diabetic conditions. In HUVECs and primary lung endothelial cells, hyperglycemia activated calpains and induced autophagic flux blockade, characterized by SQSTM1 accumulation, reduced LC3B expression, and impaired mitophagy associated with mitochondrial fragmentation. more...
#> 2 Suppressor transfer RNAs (sup-tRNAs) can rescue disease-causing nonsense mutations by promoting readthrough of premature termination codons (PTCs). Their clinical translation is limited by suboptimal activity and inefficient in vivo delivery. In this work, we combined site-specific chemical modification of sup-tRNAs with cargo-tailored pulmonary lipid nanoparticle (LNP) engineering to overcome these barriers. more...
#> 3 The current study makes use of NanoString targeted technology to profile urinary exosomal miRNAs from IgA nephropathy affected patients and corresponding healthy controls. In addition to IgA nephropathy, disease controls belonging to the condition of Lupus nephritis, Focal segmental glomerulosclerosis, Diabetic nephropathy, Minimal change disease, Membranous nephropathy and Hypertensive nephropathy were also assayed for their miRNA expression profile. more...
#> 4 Genome-wide association studies have identified IFIH1, which encodes the double-stranded RNA sensor MDA5, as a type 1 diabetes (T1D) risk locus. The IFIH1 E627* variant is associated with protection from T1D, whereas A946T is associated with increased risk. To examine how these variants influence islet responses to inflamattory and viral stress, we used CRISPR-Cas9 to engineer E627* or A946T into human pluripotent stem cells from a T1D donor and differentiated them into stem cell-derived islets (SC-islets). more...
#> 5 The cellular complexity of adipose tissue plays a critical role in the pathogenesis of metabolic disease; however, mechanisms underlying adipose dysfunction remain poorly understood. Here, we constructed a single-nucleus and bulk transcriptomic atlas of human visceral adipose tissues from an ethnically homogeneous cohort with defined metabolic states: lean with normal glucose tolerance, obesity, and obesity with type 2 diabetes. more...
#> 6 The autoreactive T cells that destroy beta cells in type 1 diabetes are largely targeting insulin signal peptide fragments. HLA knockout beta-cells have been proposed to create hypo-immune cells, but this strategy poses significant tumorigenic risks. As an alternative, we hypothesized that insulin signal peptide modification would give rise to beta-cells evading autoimmune recognition while maintaining insulin functionality. more...
#> Organism Type
#> 1 Homo sapiens Expression profiling by high throughput sequencing
#> 2 Homo sapiens Other
#> 3 Homo sapiens Other
#> 4 Homo sapiens Expression profiling by high throughput sequencing
#> 5 Homo sapiens Expression profiling by high throughput sequencing
#> 6 Homo sapiens Expression profiling by high throughput sequencing
#> FTP download
#> 1 GEO (RESULTS, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE339nnn/GSE339355/
#> 2 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE326nnn/GSE326462/
#> 3 GEO (RCC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE263nnn/GSE263198/
#> 4 GEO (CSV, MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE318nnn/GSE318038/
#> 5 GEO (CSV, MTX, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE342nnn/GSE342773/
#> 6 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE324nnn/GSE324575/Once you have the search results, you can filter them based on specific criteria. For instance, to filter for GSE datasets that contain at least 6 diabetic nephropathy samples with expression profiling, use the following code:
diabetes_nephropathy_gse_records <- diabetes_gse_records |>
dplyr::mutate(
number_of_samples = str_match(Contains, "(\\d+) Samples?")[
, 2L,
drop = TRUE
],
number_of_samples = as.integer(number_of_samples)
) |>
dplyr::filter(
dplyr::if_any(
c(Title, Summary),
~ str_detect(.x, regex("diabetes|diabetic", ignore_case = TRUE))
),
dplyr::if_any(
c(Title, Summary),
~ str_detect(.x, regex("nephropathy", ignore_case = TRUE))
),
str_detect(Type, regex("expression profiling", ignore_case = TRUE)),
number_of_samples >= 6L
)
head(diabetes_nephropathy_gse_records[1:5, 1:5])
#> Title
#> 1 DJ-1 alleviates high glucose-induced podocyte injury via activating ERK1/2 signaling
#> 2 RBBP6 orchestrates diabetic endothelial dysfunction viadisrupting JUNB-centric chromatin topology
#> 3 Integrative RNA-seq and CLIP-seq analysis reveals hnRNP-F regulation of the TNFα/NFκB signaling in high glucose conditions
#> 4 Effect of FGF9 on human renal tubular epithelial cells in high glucose culture
#> 5 Endothelial Kallikrein-Related Peptidase 8 Promotes Diabetic Nephropathy via Reducing SDC4 Expression and Enhancing LIF Release
#> Summary
#> 1 Diabetic nephropathy (DN) is one of the most common complications of diabetes. DJ-1 has been reported to participate in the response to renal ischemia/reperfusion (I/R) injury. However, the underlying mechanisms of DJ-1 in the regulation of high glucose-induced renal injury remain obscure. In this study, we performed RNA-seq to explore the function of high glucose on human podocyte cells (HPC), and found that high glucose widely regulated a variety of signaling pathways, including cell growth and death, signal transduction, etc. more...
#> 2 Diabetes mellitus, a chronic metabolic disease affecting over 536.6 million people globally, is closely associated with vascular endothelial dysfunction, an early hallmark of diabetic cardiovascular complications. This dysfunction is characterized by impaired endothelial nitric oxide synthase (eNOS) activity, reduced nitric oxide (NO) production, and diminished angiogenic capacity, ultimately contributing to tissue ischemia and complications such as diabetic nephropathy, coronary artery disease, and peripheral arterial disease. more...
#> 3 Using RNA-seq and ChIP-seq we found that we found that hnRNP-F may bind to lncRNA SNHG1 to negatively regulate the transcription of genes involved in the TNFα/NFκB signaling pathway in diabetic nephropathy. Our study suggests that hnRNP-F may play a role in diabetic nephropathy by regulating the differential expression and variable splicing of diabetic nephropathy-associated genes, especially those related to inflammatory response.
#> 4 Diabetic nephropathy is characterised by the accumulation of extracellular matrix in the glomerular tunica and tubular interstitium, which ultimately leads to excessive renal scarring and decreased excretory function. The main pathological changes of renal fibrosis are: glomerulosclerosis, tubulointerstitial fibrosis, infiltration of inflammatory mediators and activation of α-SMA-positive myofibroblasts. more...
#> 5 The molecular mechanisms underlying diabetic nephropathy (DN) are poorly defined. We sought to investigate the roles of kallikrein-related peptidases (KLKs) in DN pathogenesis. Screening of renal tissue from diabetic mice revealed KLK8 as the most highly induced gene in KLK family. KLK8 expression was greater in glomerular endothelial cells (GECs) than other glomerular cells in DN patients and diabetic mice. more...
#> Organism
#> 1 Homo sapiens
#> 2 Homo sapiens; Mus musculus
#> 3 Homo sapiens
#> 4 Homo sapiens
#> 5 Homo sapiens
#> Type
#> 1 Expression profiling by high throughput sequencing
#> 2 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other
#> 3 Expression profiling by high throughput sequencing
#> 4 Expression profiling by high throughput sequencing
#> 5 Expression profiling by high throughput sequencing
#> FTP download
#> 1 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE307nnn/GSE307956/
#> 2 GEO (BIGWIG, BW, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE291nnn/GSE291636/
#> 3 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273001/
#> 4 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE265nnn/GSE265918/
#> 5 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE255nnn/GSE255028/After applying the filter, we obtain 40 candidate datasets. This filtering step significantly reduces the time spent manually reviewing summary records.
You can also use geo_meta() to dynamically create a
self-knowledge-concerned database in real-time. See
vignette("geometadb") for details.
Session Information
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] stringr_1.6.0 geokit_0.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.7.3 httr_1.4.9 cli_3.6.6 knitr_1.51
#> [5] rlang_1.3.0 xfun_0.60 stringi_1.8.9 otel_0.2.0
#> [9] generics_0.1.4 textshaping_1.0.5 jsonlite_2.0.0 glue_1.8.1
#> [13] htmltools_0.5.9 XML_3.99-0.24 ragg_1.5.2 sass_0.4.10
#> [17] rmarkdown_2.32 tibble_3.3.1 evaluate_1.0.5 jquerylib_0.1.4
#> [21] fastmap_1.2.0 yaml_2.3.12 lifecycle_1.0.5 compiler_4.6.1
#> [25] dplyr_1.2.1 codetools_0.2-20 rentrez_1.2.4 fs_2.1.0
#> [29] pkgconfig_2.0.3 systemfonts_1.3.2 digest_0.6.39 R6_2.6.1
#> [33] tidyselect_1.2.1 pillar_1.11.1 curl_8.0.0 magrittr_2.0.5
#> [37] bslib_0.12.0 withr_3.0.3 tools_4.6.1 pkgdown_2.2.1
#> [41] cachem_1.1.0 desc_1.4.3