High-resolution genomic profiles of human lung cancer is a research paper published in Proceedings of the National Academy of Sciences (2005). On theSindex it has a DataRank of 0.897. It has been cited 395 times. Its calibrated FAIR score is 55/100.
Lung cancer is the leading cause of cancer mortality worldwide, yet there exists a limited view of the genetic lesions driving this disease. In this study, an integrated high-resolution survey of regional amplifications and deletions, coupled with gene-expression profiling of non-small-cell lung cancer subtypes, adenocarcinoma and squamous-cell carcinoma (SCC), identified 93 focal copy-number alterations, of which 21 span <0.5 megabases and contain a median of five genes. Whereas all known lung cancer genes/loci are contained in the dataset, most of these recurrent copy-number alterations are previously uncharacterized and include high-amplitude amplifications and homozygous deletions. Notably, despite their distinct histopathological phenotypes, adenocarcinoma and SCC genomic profiles showed a nearly complete overlap, with only one clear SCC-specific amplicon. Among the few genes residing within this amplicon and showing consistent overexpression in SCC is p63, a known regulator of squamous-cell differentiation. Furthermore, intersection with the published pancreatic cancer comparative genomic hybridization dataset yielded, among others, two focal amplicons on 8p12 and 20q11 common to both cancer types. Integrated DNA-RNA analyses identified WHSC1L1 and TPX2 as two candidates likely targeted for amplification in both pancreatic ductal adenocarcinoma and non-small-cell lung cancer.
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Base Score Contribution
0.897
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.