Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival
Open accessAbstract
Background: Transcriptome analysis of breast cancer discovered distinct disease subtypes of clinical significance. However, it remains a challenge to define disease biology solely based on gene expression because tumor biology is often the result of protein function. Here, we measured global proteome and transcriptome expression in human breast tumors and adjacent non-cancerous tissue and performed an integrated proteotranscriptomic analysis. Methods: We applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis using an untargeted approach and analyzed protein extracts from 65 breast tumors and 53 adjacent non-cancerous tissues. Additional gene expression data from Affymetrix Gene Chip Human Gene ST Arrays were available for 59 tumors and 38 non-cancerous tissues in our study. We then applied an integrated analysis of the proteomic and transcriptomic data to examine relationships between them, disease characteristics, and patient survival. Findings were validated in a second dataset using proteome and transcriptome data from “The Cancer Genome Atlas” and the Clinical Proteomic Tumor Analysis Consortium. Results: We found that the proteome describes differences between cancerous and non-cancerous tissues that are not revealed by the transcriptome. The proteome, but not the transcriptome, revealed an activation of infection-related signal pathways in basal-like and triple-negative tumors. We also observed that proteins rather than mRNAs are increased in tumors and show that this observation could be related to shortening of the 3′ untranslated region of mRNAs in tumors. The integrated analysis of the two technologies further revealed a global increase in protein-mRNA concordance in tumors. Highly correlated protein-gene pairs were enriched in protein processing and disease metabolic pathways. The increased concordance between transcript and protein levels was additionally associated with aggressive disease, including basal-like/triple-negative tumors, and decreased patient survival. We also uncovered a strong positive association between protein-mRNA concordance and proliferation of tumors. Finally, we observed that protein expression profiles co-segregate with a Myc activation signature and separate breast tumors into two subgroups with different survival outcomes. Conclusions: Our study provides new insights into the relationship between protein and mRNA expression in breast cancer and shows that an integrated analysis of the proteome and transcriptome has the potential of uncovering novel disease characteristics.
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In a dataset of 61 unselected breast cancer patients, aggregate survival analysis showed no significant difference in overall survival between patients with high vs. low tumor SLFN11 expression (HR = 1.24, p = 0.608).
“In a dataset of 61 unselected breast cancer patients [10], aggregate survival analysis showed no significant difference in overall survival (OS) between patients with high vs. low tumor SLFN11 expression (HR = 1.24, p = 0.608) (Fig 3A).”
From Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy (2019) · cites this paper as [10]
The Results section on proteomic profiling describes a breast-cancer dataset, but reports 65 tumors, not 61 patients. The citation is attached to the dataset description; the SLFN11 survival analysis is presented as the citing authors’ own result in Fig. 3A, so it need not appear in this source. Our copy does not establish the stated 61-patient subset or that patients were unselected. (LLM, conf=0.83)
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Kundu, S., Ali, M. A., Handin, N., Conway, L., Rendo, V., Artursson, P., He, L.-Q., Globisch, D., & Sjöblom, T. (2021) · Journal of experimental & clinical cancer research : CR · doi:10.1186/s13046-021-02025-2 · Cited by 25 · see citing works
TSPLASSO: A Two-Stage Prior LASSO Algorithm for Gene Selection Using Omics Data
Yang, S.-J., Chen, S., Wang, P., Chen, A.-M., & Tian, T. (2023) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2023.3326485 · Cited by 22 · see citing works
Begolli, R., Chatziangelou, M., Samiotaki, M., Goutas, A., Barda, S., Goutzourelas, N., Kevrekidis, D., Malea, P., Trachana, V., Liu, M., et al. (2023) · Human Genomics · doi:10.1186/s40246-023-00517-0 · Cited by 22 · see citing works
“Low level of agreement between gene expression and protein abundance is not unusual and has been observed in previous studies with cancer cell lines [54, 55].”
ResultAlcaraz, A. J., Baraniuk, S., Mikulášek, K., Park, B., Lane, T., Burbridge, C., Ewald, J., Potěšil, D., Xia, J., Zdráhal, Z., et al. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.118667 · Cited by 22
Xu, X., Zhang, M.-X., Zhang, X., Liu, Y., Cai, L.-F., Zhang, Q., Chen, Q., Lin, L., Lin, S., Song, Y.-L., et al. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.1c05312 · Cited by 21 · see citing works
Maarouf, A., Boissard, A., Henry, C., Leman, G., Coqueret, O., Guette, C., & Lelièvre, E. (2021) · International Journal of Oncology · doi:10.3892/ijo.2021.5295 · Cited by 21 · see citing works
“The cohort contained samples from the three molecular subtypes of breast cancer: Luminal A, HER2‐positive and triple nega‐ tive (36).”
BackgroundMethodInfluentialJansson, M., Lindberg, J. A., Rask, G., Svensson, J., Billing, O., Nazemroaya, A., Berglund, A., Wärnberg, F., & Sund, M. (2024) · Clinical Breast Cancer · doi:10.1016/j.clbc.2024.02.015 · Cited by 20 · see citing works
Principles of phosphoproteomics and applications in cancer research
Higgins, L., Gerdes, H., & Cutillas, P. (2023) · Biochemical Journal · doi:10.1042/bcj20220220 · Cited by 19 · see citing works
Hu, G.-S., Zheng, Z.-Z., He, Y.-H., Wang, D.-C., Nie, R., & Liu, W. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2025.100919 · Cited by 18 · see citing works
Molecular Responses Mechanism of Synechocystis sp. PCC 6803 to Cadmium Stress
Ruan, G., Mi, W.-J., Yin, X.-W., Song, G.-F., & Bi, Y. (2022) · Water · doi:10.3390/w14244032 · Cited by 18 · see citing works
Multi-omics integration highlights the role of ubiquitination in endometriosis fibrosis
Yang, M., Jiang, H., Ding, X., Zhang, L., Zhang, H., Chen, J., Li, L., He, X., Huang, Z., & Chen, Q. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05245-0 · Cited by 17 · see citing works
“Studies show poor correlation between protein and transcript levels [18, 19], a discrepancy also observed in tubal endometriosis [20], emphasizing the importance of proteomics, particularly modified-proteomics, in discovering novel biomarkers and targets.”
BackgroundTGFβ-mediated MMP13 secretion drives myoepithelial cell dependent breast cancer progression
Gibson, S., Tomás-Bort, E., Rodríguez-Fernández, L., Allen, M. D., Gomm, J., Goulding, I., Keller, U. A. D., Agnoletto, A., Brisken, C., Peck, B., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00513-6 · Cited by 16 · see citing works
Identification of an mRNA isoform switch for HNRNPA1 in breast cancers
Erdem, M., Ozgul, İ., Dioken, D. N., Gurcuoglu, I., Ergün, S. G., Çetin-Atalay, R., Can, T., & Erson-Bensan, A. E. (2021) · Scientific Reports · doi:10.1038/s41598-021-04007-y · Cited by 16 · see citing works
Zingone, A., Sinha, S., Ante, M., Nguyen, C., Daujotytė, D., Bowman, E., Sinha, N., Mitchell, K. A., Chen, Q.-R., Yan, C.-H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25763-5 · Cited by 16 · see citing works
Self-citationRNA m6A Methylation Regulators Subclassify Luminal Subtype in Breast Cancer
Yang, L., Wu, S.-L., Ma, C.-H., Song, S.-H., Jin, F., Niu, Y., & Tong, W.-M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.611191 · Cited by 16 · see citing works
“The first proteomic study applied a quantitative liquid chromatography/mass spectrometry-based proteome analysis to 65 breast tumors and 53 adjacent noncancerous tissues (40).”
MethodChoi, E., Jeon, K., Lee, H., Mun, G., Kim, J., Shin, J., Kwon, Y., Na, Y., & Lee, Y. (2024) · Cancer Science · doi:10.1111/cas.16174 · Cited by 15 · see citing works
“…CTSS, we investigated a liquid chromatography–mass spectrometry (LC-MS)- based proteomic dataset from a cohort of 65 breast cancer patients provided by Tang et al. 24 The OS analysis revealed that a higher expression of CTSS was strongly associated with reduced survival ( P = 0.012) (Figure 1a ).”
MethodInfluentialTROP2 as Patient-Tailoring but Not Prognostic Biomarker for Breast Cancer
Liu, X., Zhou, T., Wang, Y., Pei, M., Wang, G., Chu, W., Wang, Q., Du, S., Wang, H., & Wang, C. (2022) · OncoTargets and Therapy · doi:10.2147/ott.s354048 · Cited by 16 · see citing works
HERC1 Regulates Breast Cancer Cells Migration and Invasion
Rossi, F. A., Roitberg, E. C. C., Steinberg, J. H. E., Joshi, M., Espinosa, J. M., & Rossi, M. (2020) · bioRxiv · doi:10.3390/cancers13061309 · Cited by 15 · see citing works
“The association between the HERC1 protein expression levels and OS was analyzed using an online KM-Plotter database(42) using the protein expression data and the survival information of patients with breast cancer downloaded from the GEO (43).”
MethodShi, J.-H., Zhu, T.-Y., Lin, H.-M., Liu, Z., Zhou, M., Yu, Z.-Q., Zhou, X., Song, X., Wang, Y.-F., Jia, R., et al. (2022) · Journal of experimental & clinical cancer research : CR · doi:10.1186/s13046-022-02445-8 · Cited by 14 · see citing works
“Consistent with previous observations in breast cancer, our findings showed that high concordance is a disease-specific characteristic in OABL and associated with poor prognosis [11].”
BackgroundMethodResultInfluentialSelf-citationReciprocal antagonism of PIN1-APC/CCDH1 governs mitotic protein stability and cell cycle entry
Ke, S., Dang, F., Wang, L., Chen, J.-Y., Naik, M. T., Li, W.-X., Thavamani, A., Kim, N., Naik, N., Sui, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47427-w · Cited by 13 · see citing works
“37 that provided patient-level mRNA, protein and outcomes data.”
Self-citationLiu, X.-P., Hou, J., Chen, C., Guan, L., Hu, H., & Li, S. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00118 · Cited by 13 · see citing works
Hung, Y.-W., Ouyang, C., Ping, X., Qi, Y., Wang, Y.-C., Kung, H., & Ann, D. (2023) · Journal of Biomedical Sciences · doi:10.1186/s12929-023-00924-4 · Cited by 12 · see citing works
Sun, Z., Liu, Y.-F., He, X.-Y., Di, R., Wang, X.-Y., Ren, C.-H., Zhang, Z.-J., & Chu, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232314888 · Cited by 10 · see citing works
“Recent advances in multi-omics have facilitated the generation of large datasets that can be used to investigate complex life activities [24,25].”
BackgroundSelf-citationPrakash, A., Taylor, L., Varkey, M., Hoxie, N., Mohammed, Y., Goo, Y., Peterman, S., Moghekar, A., Yuan, Y., Glaros, T. G., et al. (2021) · Cancers · doi:10.3390/cancers13205034 · Cited by 10 · see citing works
“studied 65 breast tumors and 53 adjacent noncancerous tissues with extensive fractionation, leading to 118 RAW files [20].”
BackgroundProteomic heterogeneity reveals SOAT1 as a potential biomarker for hepatocellular carcinoma.
Khatib, S. A., & Wang, X.-W. (2019) · Translational Gastroenterology and Hepatology · doi:10.21037/tgh.2019.05.09 · Cited by 10
Self-citationSari, A. I. P., Roytrakul, S., Chittavanich, P., Saengwimol, D., Laosillapacharoen, N., Khamjerm, J., Borwornpinyo, S., Jinawath, A., Suvikapakornkul, R., Lertsithichai, P., et al. (2025) · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · doi:10.1016/j.biopha.2025.118359 · Cited by 9 · see citing works
Li, X., Poire, A., Jeong, K. J., Zhang, D., Chen, G., Sun, C., & Mills, G. B. (2023) · Cell Death Discovery · doi:10.1038/s41420-023-01586-9 · Cited by 9 · see citing works
“4E) [38, 39].”
Adipose-Derived Stromal/Stem Cell Response to Tumors and Wounds: Evaluation of Patient Age
Hamel, K., Liimatta, K. Q., Belgodere, J. A., Bunnell, B., Gimble, J. M., & Martin, E. (2022) · Stem Cells and Development · doi:10.1089/scd.2021.0280 · Cited by 9 · see citing works
A glucocorticoid–FAS axis controls immune evasion during metastatic seeding
Cassandras, M., Sanchez, X., Hsu, L., Huang, Y., Getzler, A. J., Ganguly, D., Baldominos, P., Codinachs, I., Chuong, J., Martin, E. E., et al. (2026) · Nature · doi:10.1038/s41586-026-10222-2 · Cited by 8 · see citing works
“Correlations between GR protein and patient outcome were analysed from a published proteomics dataset of patients with breast cancer 70 using the Kaplan–Meier Plotter (https://kmplot.com/analysis) breast protein database 71 .”
Restier-Verlet, J., Ferlazzo, M., Granzotto, A., Al-Choboq, J., Bellemou, C., Estavoyer, M., Lecomte, F., Bourguignon, M., Pujo-Menjouet, L., & Foray, N. (2024) · Cells · doi:10.3390/cells13201703 · Cited by 8 · see citing works
“However, similarly to the eye, such observations originate from the follow-up of breast cancer women treated by radiotherapy, i.e., from high and fractionated doses (more than 50% of patients elicit a significant risk of heart attack for 10 years post-radiotherapy against breast tumor [69–71]).”
BackgroundCerutti, C., Lucotti, S., Menendez, S. T., Reymond, N., Garg, R., Romero, I. A., Muschel, R., & Ridley, A. J. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113989 · Cited by 8 · see citing works
“Kaplan-Meier plots (https://kmplot.com/analysis/) 37,38 on breast cancer patient samples (Tang 2018 dataset 39 ) showed that high NWASP (Figure S4R) and IQGAP1 (Figure S4S) protein expression correlated with shorter survival.”
Kao, H.-Y., Pai, C.-P., Wang, H., Agarwal, N., Adams, J. A., Liu, Z., Seachrist, D. D., Keri, R., & Schiemann, W. P. (2023) · Research Square · doi:10.21203/rs.3.rs-3266720/v1 · Cited by 8
Proteomic Analysis Identifies p62/SQSTM1 as a Critical Player in PARP Inhibitor Resistance
Uddin, M. H., Zhou, J.-H., Pimentel, J. M., Patrick, S. M., Kim, S.-J., Shekhar, M. P. V., & Wu, G.-S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.908603 · Cited by 8 · see citing works
“(2018) (28) revealed a similar positive correlation between overall survival and p62 protein expression in 33 ER-negative breast cancer patients (HR, 0.”
BackgroundMethodResultInfluentialAndrieux, G., Chakraborty, S., Das, T., & Boerries, M. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.586479 · Cited by 9 · see citing works
“Interestingly, the authors utilized predicted mRNA and protein stability profiles and showed that the global mRNAto-protein correlation increased with the predicted stability of corresponding mRNA and protein molecules (Tang et al., 2018).”
BackgroundDing, N., Zhang, B.-T., Ying, W., Song, J., Feng, L., Zhang, K., Li, H., Xu, J., Xiao, T., & Cheng, S.-J. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00224-5 · Cited by 8
Water as a reactant in the differential expression of proteins in cancer
Dick, J. M. (2020) · bioRxiv · doi:10.1101/2020.04.09.035022 · Cited by 8
Tovar-Hernández, K., Salinas-Vera, Y. M., Carlos-Reyes, Á., García-Hernández, A., Marchat, L., Mandujano-Lázaro, G., Ríos-Castro, E., Velasco-Suárez, A., Mendez-Gómez, I., Tecalco-Cruz, Á. C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-76053-1 · Cited by 7
Behera, R. N., Bisht, V. S., Giri, K., & Ambatipudi, K. (2023) · PROTEOMICS - Clinical Applications · doi:10.1002/prca.202300016 · Cited by 7 · see citing works
“…cancer complexities such as organ metastasis, immune evasion, cellular plasticity, cancer dormancy, chemoresistance, and BC reoccurrence at the molecular level by disclosing the cancer signaling, monitoring the disease progression, checking drug response, and neutralizing drug resistance [23–27].”
BackgroundFeng, X.-Y., Cao, A., Qin, T., Zhang, Q.-Q., Fan, S.-J., Wang, B., Song, B., Yu, X.-T., & Li, L.-H. (2021) · Oncology Report · doi:10.3892/or.2021.8187 · Cited by 7 · see citing works
“The analysis of data (n=118) from Tang et al (28) revealed that UBQLN1 protein expression was also associated with a poor prognosis (Fig.”
MethodRida, P., Andreae, R., Bikhazi, N., Jackson, B., Wang, I.-C., & Jinna, N. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27041823 · Cited by 6
The UFL1-AKT positive feedback loop promotes breast cancer progression by enhancing lipid synthesis
Meng, F., Du, Y., Liang, J., Li, H., Wang, J., Tang, K., Kong, R., Sun, H.-R., Yin, T., Qin, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68492-3 · Cited by 6
Zhao, P.-P., Zhang, Y.-Y., Yu, Y., Zhang, Q., Liu, X.-Y., Zhang, X.-D., Chen, S., de Bock, C., Thorne, R. F., & Shi, Y. (2025) · International Journal on Biological Sciences · doi:10.7150/ijbs.104921 · Cited by 6
“Nevertheless, not all genes display good correlation between mRNA and protein levels in breast cancer tissues [38], but importantly we found the subtype variations in FAT1 mRNA expression were maintained at the protein level with the highest levels in TNBC (Fig.”
Feng, D., Pu, D.-Q., Ren, J., Liu, M., Sun, X., Zhang, Z., & Li, J.-W. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1443648 · Cited by 6 · see citing works
“Several factors contribute to this phenomenon, including the decoupling of gene and protein expression, post-transcriptional regulatory mechanisms, the impact of epigenetic modi fi cations, and discrepancies in protein stability and degradation rates (33 – 35).”
Evaluation of a Proteomics-Guided Protein Signature for Breast Cancer Detection in Breast Tissue.
Moreno-Ulloa, A., Zárate-Córdova, V. L., Ramírez-Sánchez, I., Cruz-López, J., Perez-Ortiz, A., Villarreal-Garza, C., Díaz-Chávez, J., Estrada-Mena, B., Antonio-Aguirre, B., López-Almanza, P. X., et al. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00295 · Cited by 6
Sholehrasa, H., & Jaberi-Douraki, M. (2024) · arXiv.org · doi:10.48550/arxiv.2410.01755 · Cited by 6
“…expression levels in breast cancer enables the construction of a biologically comprehensive representation of tumor biology, facilitating downstream analyses such as clustering tumors into proteomic subgroups and associating these clusters with patient survival outcomes and molecular subtypes [10].”
Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer.
Banerjee, S., Hatimuria, M., Sarkar, K., Das, J., Pabbathi, A., & Sil, P. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00223 · Cited by 6
Cellular Transcriptomics of Carboplatin Resistance in a Metastatic Canine Osteosarcoma Cell Line
Hodge, M., Miller, T., Weinman, M. A., Wustefeld-Janssens, B. G., Bracha, S., & Davis, B. W. (2023) · Genes · doi:10.3390/genes14030558 · Cited by 6 · see citing works
“The transient nature of the expressed genes tends to be more dynamic, and the proteome more static [110,111].”
BackgroundRuhen, O., Qu, X.-Y., Jamaluddin, M. F. B., Salomon, C., Gandhi, A., Millward, M., Nixon, B., Dun, M. D., & Meehan, K. (2021) · Membranes · doi:10.3390/membranes11110880 · Cited by 6
“1, histone H2A type 2-A, alstrom syndrome protein 1, fibulin-1, titin, glyceraldehyde-3-phosphate dehydrogenase, tropomodulin-2, cofilin-1, and peroxiredoxin-2 were significantly associated with shorter overall survival (Figure 6) [22].”
BackgroundMethodIntegrated multi-omics analysis for lung adenocarcinoma in Xuanwei, China
Jiang, B., Yang, J., He, R., Wang, D., Huang, Y., Zhao, G., Ning, M., Zeng, T., & Li, G. (2023) · Aging · doi:10.18632/aging.205300 · Cited by 5 · see citing works
“Tang et al. found that protein-mRNA consistency in breast tumors was enhanced and could be used as a novel disease characteristic and prognostic factor [58].”
Jin, H., Wang, X., Li, L.-Y., Rui, C., Gan, H., Wang, Q.-N., Tao, F.-B., & Zhu, Y.-M. (2024) · iScience · doi:10.1016/j.isci.2024.111423 · Cited by 5 · see citing works
“13,14 In particular, to our knowledge, none of the previous studies has used transcriptomic profiles and corresponding proteomic information in the placenta to systematically portray molecular explanations for the occurrence of SGA.”
Self-citationWu, Q., Tang, X.-M., Zhu, W., Li, Q., Zhang, X., & Li, H.-Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.751430 · Cited by 5 · see citing works
“In general, a comprehensive analysis of the proteome and transcriptome has the potential to reveal the characteristics of new diseases (9).”
BackgroundZhang, W., Fei, S.-J., Tong, R.-F., Liu, Y., He, J.-J., Wan, B., & Xu, J. (2021) · Annals of Diagnostic Pathology · doi:10.1016/j.anndiagpath.2021.151834 · Cited by 5
Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer
Karvelsson, S. T., Wang, Q., Hilmarsdottir, B., Sigurdsson, A., Moestue, S., Mælandsmo, G., Halldorsson, S., Gudmundsson, S., & Rolfsson, Ó. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00195-5 · Cited by 5 · see citing works
Alamukii, N. A., Kovács, A., Raghavan, S., Ilio, J., Karlsson, P., Helou, K., & Parris, T. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00881-1 · Cited by 4 · see citing works
Identification of candidate biomarkers correlated with the pathogenesis of breast cancer patients
Yu, S.-Q., Wang, C., Ouyang, J., Luo, T., Zeng, F.-F., Zhang, Y., Gao, L.-Y., Huang, S., & Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-93208-w · Cited by 4 · see citing works
Self-citationComparative Analysis of Epididymis Cauda of Yak before and after Sexual Maturity
Ding, Z.-Q., Xiong, L., Wang, X.-D., Guo, S.-K., Cao, M.-L., Kang, Y., La, Y., Bao, P., Pei, J., & Guo, X. (2023) · Animals · doi:10.3390/ani13081355 · Cited by 4
“humans, cattle, and mice, which may be related to differences in experimental techniques, shortening of 3′UTR, and differences in spatiotemporal expression [16,33].”
BackgroundSelf-citationMonteiro, F., Stepanauskaitė, L., Williams, C., & Helguero, L. (2022) · Cancers · doi:10.3390/cancers14246029 · Cited by 4 · see citing works
“Analysis based on high SETD7 protein data [50] in KM plotter also showed that”
BackgroundMethodEngel, A. J., Samuel, K., Bass, I., Lin, S., Antoniou, I., Yagnik, R., Port, E., Feldman, S., Friedman, N. B., Boolbol, S., et al. (2026) · Journal of the Endocrine Society · doi:10.1210/jendso/bvag041 · Cited by 3 · see citing works
“It is important to note that the tumors used for this analysis were not from our cohort and were not all TNBC but were from protein analysis performed by liquid chromatography-mass spectrometry rather than IHC, as described by Tang et al [17].”
InfluentialPV-1: a novel molecular prognostic marker of distant metastases in various solid tumors
Pozzi, C., Sarti, R., Cascio, A. L., Bonovas, S., Tiraboschi, L., Lizier, M., Bertocchi, A., Torrisi, R., Fernandes, B., Colombo, P., et al. (2025) · EMBO Molecular Medicine · doi:10.1038/s44321-025-00277-5 · Cited by 3
“Only one dataset was available for this analysis (Tang et al, 2018), comprising 65 tumor samples.”
Stiphout, C. M. V., Kelly, G., Pallegar, N. K., Elbakry, E., Vilchis-Celis, A. V., Christian, S., & Viloria-Petit, A. (2023) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2024.00201 · Cited by 3 · see citing works
Zeki, Ö., Nenni, M., Çelebi̇er, M., Öncül, S., Ercan, A., Süslü, İ., & Haznedaroglu, I. (2021) · doi:10.1016/j.hermed.2021.100449 · Cited by 3
The RNA and protein landscape that could bring precision medicine to more people
Makin, S. (2020) · Nature · doi:10.1038/d41586-020-02676-9 · Cited by 3
An AGO2 adaptor expands the functional and evolutionary reach of microRNA targetingPreprint
Crozier, A. F. F., Shah, K. M., Grevitt, P., Thind, A., Maniati, E., Wang, J., Shen, K., Cox, D. C., Rajeeve, V., Cutillas, P. R., et al. (2025) · bioRxiv · doi:10.1101/2025.10.07.680889 · Cited by 2 · see citing works
“J ), consistent with loss of translational control ( 40 , 41 ).”
Multi-modal for the diagnosis and management of ocular adnexal lymphoma
Zhou, M., Tan, H., & Zhou, Y.-X. (2025) · Holistic Integrative Oncology · doi:10.1007/s44178-025-00148-6 · Cited by 2
“Pro-teotranscriptomics, which combines proteomic and transcriptomic data, has provided new insights into the development and progression of malignancies [36].”
Self-citationTakeda, M., Ito, H., Kitahata, K., Sato, S., Nishide, A., Gamo, K., Managi, S., Tezuka, T., Yoshizawa, A., & Kim, M. (2024) · Cells · doi:10.3390/cells13181572 · Cited by 2
“…contribute to poor prognosis in BC. 13 , 1572 Similarly, the high α -parvin expression group showed a trend toward worse overall survival in both datasets, with the data of Tang et al. [27] being significant (Figure 1e). curves for two groups according to high and low PARVA/ α -parvin expression.”
MethodResultIntegrative analysis of transcriptome and proteome profiles in primary and recurrent glioblastoma
Zhang, J., Wang, G., Yan, B., Yang, G., Yang, Q., Hu, Y.-Q., Guo, J.-R., Zhao, N., Wang, L., & Wang, H.-J. (2023) · PROTEOMICS - Clinical Applications · doi:10.1002/prca.202200085 · Cited by 2
“Thelowcorrelationbetweentranscriptionand proteome was reported in multiple tumors, such as breast cancer and lung cancer [19, 20].”
BackgroundKe, S., Dang, F., Wang, L., Chen, J.-Y., Naik, M., Thavamani, A., Liu, Y.-S., Li, W.-X., Kim, N., Naik, N., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2447544/v1 · Cited by 2 · see citing works
Self-citationHuang, A.-Q., Huang, S., Shah, P., Ku, W.-C., Huang, K.-T., Liu, Y., Su, C.-L., & Huang, R. (2022) · Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2022.109000 · Cited by 2 · see citing works
Paranthaman, P., Karuppasamy, R., & Veerappapillai, S. (2026) · BioMedicine · doi:10.37796/2211-8039.1708 · Cited by 1
“Four microarray datasets (GSE36295, GSE37751, GSE61724, GSE38959) and one RNA-seq dataset (GSE58135) were downloaded from the publicly available GEO database and analyzed using the GEO2R script [12 — 17].”
Savoie, J. J., Hebert, K., King, C., McConnell, E. C., Hoang, V. T., Monroe, W. T., Burow, M. E., Collins-Burow, B., Belgodere, J. A., & Martin, E. C. (2025) · Bioengineering · doi:10.3390/bioengineering13010047 · Cited by 1 · see citing works
“Further, when compared to prior work on non-metaplastic TNBC [6], the decellularized samples identified proteins not detected in non-metaplastic TNBC tumors, suggesting potential subtype-specific ECM features.”
Sun, T., Zhang, X., Liu, X., Li, X., Li, S., Yu, S., Xiao, Z., & Li, N. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00087 · Cited by 1
A proteotranscriptomic approach to dissect the molecular landscape of human retinoblastoma
Wolf, J., Hajdú, R. I., Boneva, S., Godbole, I., Stürzbecher, L., Auw-Haedrich, C., Lagrèze, W., Agostini, H., Reinhard, T., Tholen, S., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1571702 · Cited by 1
Liu, W., Chen, W., Tang, M., Liu, S., Gao, H., & Miao, C. (2025) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-025-01733-2 · Cited by 1 · see citing works
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- Authors
- Tang, W., Zhou, M., Dorsey, T. H., Prieto, D. A., Wang, X. W., Ruppin, E., Veenstra, T. D., & Ambs, S.
- Affiliations
- National Institutes of Health (US)
- Frederick National Laboratory for Cancer Research (US)
- National Cancer Institute (US)
- Corresponding author
- Ambs, Stefan
- Venue
- Genome Medicine
- Published
- 2018-12-03
- Volume
- 10
- Issue
- 1
- Pages
- 94-94
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC6276229
- OpenAlex
- W2902072367
- Cited by
- 162
- References
- 37
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 20 · 2023: 44 · 2022: 25 · 2021: 35 · 2020: 9 · 2019: 6
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-06-24)
- Open access
- gold
- doi.org
- Locations
- Genome Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DigitalCommons-Cedarville (Cedarville University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Cancer Institute (ZIA BC 010887)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- breast cancer, proteotranscriptomics, breast cancer subtypes, basal-like breast cancer, triple-negative breast cancer, tumor cell proliferation, patient survival
- MeSH terms
- Breast Neoplasms, Chromatography, Liquid, Female, Humans, Mass Spectrometry, Signal Transduction, Gene Expression Regulation, Neoplastic, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Proteomics
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