Table 4.
The top 20 most total cited publications in the field of sequencing and omics studies in prostate cancer.
| Rank | Publication | LCs | TCs | LCs/TCs Ratio (%) | TCs Per Year | Raw data | Methodology | Main discovery |
|---|---|---|---|---|---|---|---|---|
| 1 | GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses | 34 | 5381 | 0.63 | 768.71 | Public databases | Bioinformatic analysis | Development of a web server for interactive and customizable functions |
| 2 | Gene ontology analysis for RNA-seq: accounting for selection bias | 7 | 4207 | 0.17 | 300.50 | Self-test data of prostate cancer, liver, and kidney | Bioinformatic analysis | Development of a statistical methodology that enables the application of GO analysis to RNA-seq data |
| 3 | ONCOMINE: a cancer microarray database and integrated data-mining platform | 35 | 2786 | 1.26 | 139.30 | Public databases | Bioinformatic analysis | Development of a web-based data-mining platform for genome-wide expression analyses |
| 4 | rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data | 3 | 1097 | 0.27 | 109.70 | Public databases and self-test data | Bioinformatic analysis, RT-qPCR | Development of a new statistical model and computer program to detect differential alternative splicing from replicate RNA-Seq data |
| 5 | Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer | 64 | 1094 | 5.85 | 91.17 | Self-test data of prostate cancer | Bioinformatic analysis, RT-qPCR, immunohistochemistry, WST-1 cell proliferation experiment, Transwell experiment | Identification of new recurrent mutations in prostate cancer |
| 6 | Gene expression profiling identifies clinically relevant subtypes of prostate cancer | 56 | 985 | 5.69 | 49.25 | Self-test data of prostate cancer | Bioinformatic analysis, immunohistochemistry | Identification of clinically relevant subtypes of prostate cancer |
| 7 | MicroRNA-373 induces expression of genes with complementary promoter sequences | 2 | 917 | 0.22 | 57.31 | Self-test data of prostate cancer | RT-qPCR, ChIP analysis | Identification of a new miRNA targeting promoter sequences |
| 8 | Predicting immunogenic tumor mutations by combining mass spectrometry and exome sequencing | 0 | 799 | 0.00 | 79.90 | Self-test data of prostate cancer and colon cancer | Bioinformatic analysis, flow cytometry | Prediction of immunogenic tumor mutations |
| 9 | Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression | 33 | 791 | 4.17 | 60.85 | Self-test data of prostate cancer | Bioinformatic analysis, qPCR, RT-PCR, western blot, ChIP analysis, in vitro translational assays | Identification of a new lincRNA |
| 10 | Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front | 39 | 757 | 5.15 | 32.91 | Self-test data of prostate cancer | Bioinformatic analysis, western blot | Identification of the state of pro-survival checkpoint proteins in the dynamic development of prostate cancer |
| 11 | Emerging applications of metabolomics in drug discovery and precision medicine | 8 | 756 | 1.06 | 94.50 | / | / | Review |
| 12 | Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression | 20 | 755 | 2.65 | 37.75 | Public databases | Bioinformatic analysis | Identification of common transcriptional profiles of neoplastic progression |
| 13 | Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer | 9 | 744 | 1.21 | 39.16 | Public databases and self-test data | Bioinformatic analysis, anoikis assay, apoptosis assay, flow cytometry, RT-PCR, RT-qPCR | Identification of a stem cell-like expression profile in patients with multiple types of cancer |
| 14 | Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer | 64 | 738 | 8.67 | 33.55 | Self-test data of breast cancer | Bioinformatic analysis | Identification of potential serum biomarkers of breast cancer |
| 15 | MicroRNA expression profiling in prostate cancer | 20 | 727 | 2.75 | 42.76 | Self-test data of prostate cancer | Bioinformatic analysis, dot blot hybridization, RT-qPCR, | Identification of the miRNA expression profiles in prostate cancer |
| 16 | Characterization of human plasma-derived exosomal RNAs by deep sequencing | 3 | 712 | 0.42 | 64.73 | Self-test data of plasma | Bioinformatic analysis, qPCR | Exploration of human plasma-derived exosomal RNAs |
| 17 | Cytidine methylation of regulatory sequences near the pi-class glutathione S-transferase gene accompanies human prostatic carcinogenesis | 14 | 682 | 2.05 | 22.73 | Self-test data of prostate cancer | Immunohistochemistry, western blot, northern blot, southern blot | Identification of epigenomic alterations associated with prostate cancer |
| 18 | Mining the plasma proteome for cancer biomarkers | 15 | 642 | 2.34 | 40.13 | / | / | Review |
| 19 | Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture | 1 | 638 | 0.16 | 53.17 | Self-test data of prostate cancer | Bioinformatic analysis | Development of a novel method for sequencing |
| 20 | Transcriptome sequencing to detect gene fusions in cancer | 38 | 629 | 6.04 | 41.93 | Self-test data of prostate cancer | Bioinformatic analysis, qPCR, FISH | Discovery of new gene fusions using integrative transcriptome sequencing |
LCs, local citations; TCs, total citations; TCs Per Year = TCs/(2023–Year+1); GO, gene ontology; RT-qPCR, quantitative real-time reverse transcription PCR; ChIP, chromatin immunoprecipitation; qPCR, quantitative real-time PCR; RT-PCR, reverse transcription PCR; lincRNA, long intergenic non-coding RNA; FISH, fluorescence in situ hybridization.