Junna Oba

3.5k total citations
24 papers, 640 citations indexed

About

Junna Oba is a scholar working on Oncology, Molecular Biology and Immunology. According to data from OpenAlex, Junna Oba has authored 24 papers receiving a total of 640 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 6 papers in Molecular Biology and 6 papers in Immunology. Recurrent topics in Junna Oba's work include Cutaneous Melanoma Detection and Management (5 papers), Immunotherapy and Immune Responses (4 papers) and Cell Adhesion Molecules Research (4 papers). Junna Oba is often cited by papers focused on Cutaneous Melanoma Detection and Management (5 papers), Immunotherapy and Immune Responses (4 papers) and Cell Adhesion Molecules Research (4 papers). Junna Oba collaborates with scholars based in Japan, United States and Australia. Junna Oba's co-authors include Elizabeth A. Grimm, Scott E. Woodman, Chandrani Chattopadhyay, Masutaka Furue, Bita Esmaeli, Michelle D. Williams, Dan S. Gombos, Yong Qin, Dae Won Kim and Jennifer A. Wargo and has published in prestigious journals such as PLoS ONE, Cancer and Cancer Research.

In The Last Decade

Junna Oba

23 papers receiving 633 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Junna Oba Japan 12 278 233 227 189 83 24 640
Tobias Schimming Germany 11 277 1.0× 213 0.9× 381 1.7× 185 1.0× 59 0.7× 18 704
Marina Gualco Italy 14 221 0.8× 125 0.5× 335 1.5× 354 1.9× 56 0.7× 26 697
Long V. Ly Netherlands 14 244 0.9× 498 2.1× 250 1.1× 393 2.1× 57 0.7× 19 753
Marlana Orloff United States 17 358 1.3× 604 2.6× 561 2.5× 393 2.1× 46 0.6× 80 1.1k
Inge H. G. Bronkhorst Netherlands 13 262 0.9× 661 2.8× 244 1.1× 438 2.3× 56 0.7× 17 838
Courtney W. Hudgens United States 16 215 0.8× 56 0.2× 544 2.4× 269 1.4× 136 1.6× 30 877
Xuefei Zhu China 11 272 1.0× 408 1.8× 81 0.4× 195 1.0× 35 0.4× 24 747
Andrea Pelosi Italy 18 489 1.8× 49 0.2× 202 0.9× 297 1.6× 300 3.6× 35 912
Daniëlle Krijgsman Netherlands 14 157 0.6× 37 0.2× 317 1.4× 450 2.4× 47 0.6× 22 688
Daiju Iwata Japan 16 175 0.6× 730 3.1× 67 0.3× 126 0.7× 28 0.3× 54 986

Countries citing papers authored by Junna Oba

Since Specialization
Citations

This map shows the geographic impact of Junna Oba's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Junna Oba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junna Oba more than expected).

Fields of papers citing papers by Junna Oba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Junna Oba. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Junna Oba. The network helps show where Junna Oba may publish in the future.

Co-authorship network of co-authors of Junna Oba

This figure shows the co-authorship network connecting the top 25 collaborators of Junna Oba. A scholar is included among the top collaborators of Junna Oba based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Junna Oba. Junna Oba is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sakurada, Kazuhiro, et al.. (2025). Medical AI and AI for Medical Sciences. JMA Journal. 8(1). 26–37. 2 indexed citations
2.
Osumi, Hiroki, Akira Ooki, Tetsuo Ishikawa, et al.. (2025). Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery. Current Oncology. 32(11). 588–588.
3.
4.
Nakamura, Kohei, Eriko Aimono, Junna Oba, et al.. (2021). Estimating copy number using next-generation sequencing to determine ERBB2 amplification status. Medical Oncology. 38(4). 36–36. 19 indexed citations
5.
Oba, Junna & Scott E. Woodman. (2021). The genetic and epigenetic basis of distinct melanoma types. The Journal of Dermatology. 48(7). 925–939. 6 indexed citations
6.
Ogata, Dai, Jason Roszik, Junna Oba, et al.. (2020). The Expression of CD74-Regulated Inflammatory Markers in Stage IV Melanoma: Risk of CNS Metastasis and Patient Survival. Cancers. 12(12). 3754–3754. 6 indexed citations
7.
Chattopadhyay, Chandrani, Junna Oba, Jason Roszik, et al.. (2019). Elevated Endogenous SDHA Drives Pathological Metabolism in Highly Metastatic Uveal Melanoma. Investigative Ophthalmology & Visual Science. 60(13). 4187–4187. 35 indexed citations
8.
9.
Oba, Junna, Wei Wei, Jeffrey E. Gershenwald, et al.. (2016). Elevated Serum Leptin Levels are Associated With an Increased Risk of Sentinel Lymph Node Metastasis in Cutaneous Melanoma. Medicine. 95(11). e3073–e3073. 33 indexed citations
10.
Oba, Junna, Takeshi Nakahara, Akiko Hashimoto‐Hachiya, et al.. (2016). CD10-Equipped Melanoma Cells Acquire Highly Potent Tumorigenic Activity: A Plausible Explanation of Their Significance for a Poor Prognosis. PLoS ONE. 11(2). e0149285–e0149285. 11 indexed citations
11.
Chattopadhyay, Chandrani, Dae Won Kim, Dan S. Gombos, et al.. (2016). Uveal melanoma: From diagnosis to treatment and the science in between. Cancer. 122(15). 2299–2312. 281 indexed citations
12.
Nakahara, Takeshi, et al.. (2015). Early Tumor-Infiltrating Dendritic Cells Change their Characteristics Drastically in Association with Murine Melanoma Progression. Journal of Investigative Dermatology. 136(1). 146–153. 12 indexed citations
13.
Oba, Junna, Chandrani Chattopadhyay, Jeffrey E. Gershenwald, et al.. (2015). Abstract A32: A role for elevated leptin, independent of obesity, in the progression of melanoma. Cancer Research. 75(14_Supplement). A32–A32. 1 indexed citations
14.
Oba, Junna, et al.. (2011). Expression of milk fat globule epidermal growth factor-VIII may be an indicator of poor prognosis in malignant melanoma. British Journal of Dermatology. 165(3). 506–512. 24 indexed citations
15.
Oba, Junna, Takeshi Nakahara, Takeru Abe, et al.. (2011). Expression of c-Kit, p-ERK and cyclin D1 in malignant melanoma: An immunohistochemical study and analysis of prognostic value. Journal of Dermatological Science. 62(2). 116–123. 38 indexed citations
16.
Chen, Hongxiang, Masakazu Takahara, Junna Oba, et al.. (2011). Clinicopathologic and prognostic significance of SATB1 in cutaneous malignant melanoma. Journal of Dermatological Science. 64(1). 39–44. 54 indexed citations
17.
Moroi, Yoichi, Masakazu Takahara, Gaku Tsuji, et al.. (2011). CD10 expressed by fibroblasts and melanoma cells degrades endothelin-1 secreted by human keratinocytes. European Journal of Dermatology. 21(4). 505–509. 7 indexed citations
18.
Ito, Takamichi, Yoichi Moroi, Junna Oba, et al.. (2011). The prognostic value of a reverse transcriptase-PCR assay of sentinel lymph node biopsy for patients with cutaneous melanoma. Melanoma Research. 22(1). 38–44. 12 indexed citations
19.
Oba, Junna, Takeshi Nakahara, Sayaka Hayashida, et al.. (2011). Expression of CD10 predicts tumor progression and unfavorable prognosis in malignant melanoma. Journal of the American Academy of Dermatology. 65(6). 1152–1160. 30 indexed citations
20.
Takahara, Masakazu, Takeshi Nakahara, Junna Oba, et al.. (2010). CD10-bearing fibroblasts may inhibit skin inflammation by down-modulating substance P. Archives of Dermatological Research. 303(1). 49–55. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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