Evidence map›Paper›PMID 36713537›Full record

ArticleFrontiers in oncology2022

Clinicopathological characteristics and features of molecular subtypes of breast cancer at high altitudes.

Qi Chen, Cheng-Bin Duan, Ye Huang, Kun Liu

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Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Qi ChenDepartment of Medical Oncology, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C. T.), Chengdu, China.
Cheng-Bin DuanDepartment of Medical Oncology, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C. T.), Chengdu, China.
Ye HuangDepartment of Medical Oncology, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C. T.), Chengdu, China.
Kun LiuDepartment of Medical Oncology, Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital. C. T.), Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is one of the major malignancies threatening women's health worldwide. The incidence of breast cancer at high altitudes increased over the years. But few studies focused on the characteristics of clinicopathology and molecular subtypes among breast cancer at high altitudes, which are still unknown. Tibet, with an average altitude over 4000 meters, is a representative city at high altitudes, lying in the Qinghai-Tibetan Plateau in southwestern China. This study aimed to identify the clinicopathological characteristics and features of molecular subtypes among Tibetan women with breast cancer, and provide evidence for cancer prevention and personalized therapeutics in high-altitude regions. Methods: Between May 2013 and March 2022, 104 Tibetan women from high-altitude regions (Tibetan-group) and 34 Han Chinese women from low-altitude regions (Han-group), consecutively diagnosed with breast cancer in the Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, were included in the study. We retrospectively reviewed the clinical character, altitudes of residence, tumor size, lymph nodes metastasis, distant metastasis, pathological type, immunohistochemical index, and molecular subtype. Results: In the study, we calculated the patient delay, equal to the period from symptoms onset to hospital visits. The patient delay of Tibetan-group was 7.47 ± 11.53 months, which was significantly longer than that of Han-group, 7.22 ± 22.96 months (p<0.05). Body Mass Index (BMI) was significantly different (p<0.05). Tumors in Tibetan-group were significantly larger than those in Han-group, 4.13 ± 2.98cm and 2.51 ± 0.82cm in diameter, respectively (p<0.05). According to ordinal logistic regression analysis, exposure to high altitudes might result in more advanced T stage (OR=2.45 95%CI 1.10-5.44). 41.3% (43/104) of cases in Tibetan-group had lymph node positive disease, whereas the percentage was found in 38.26% (13/34) in Han-group(p<0.05). The distribution of molecular subtypes was quite significantly different between two groups (p<0.05), according to the comparison of constituent ratios. Conclusion: Our study verified that breast cancer at high altitudes possessed its own unique clinicopathological characteristics and distinct features of molecular subtypes. It broadened the understanding of this heterogenous disease and also provided valuable evidence for cancer prevention and personalized therapeutics of breast cancer at high altitudes.

Indexed as

breast cancerhigh altitudemolecular subtypepathologytibet

Identifiers

PMID36713537
PMCPMC9880539

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.