Direct Tumor Killing and Immunotherapy through Anti-SerpinB9 Therapy.

Cancer therapies kill tumors either directly or indirectly by evoking immune responses and have been combined with varying levels of success. Here, we describe a paradigm to control cancer growth that is based on both direct tumor killing and the triggering of protective immunity. Genetic ablation of serine protease inhibitor SerpinB9 (Sb9) results in the death of tumor cells in a granzyme B (GrB)-dependent manner. Sb9-deficient mice exhibited protective T cell-based host immunity to tumors in association with a decline in GrB-expressing immunosuppressive cells within the tumor microenvironment (TME). Maximal protection against tumor development was observed when the tumor and host were deficient in Sb9. The therapeutic utility of Sb9 inhibition was demonstrated by the control of tumor growth, resulting in increased survival times in mice. Our studies describe a molecular target that permits a combination of tumor ablation, interference within the TME, and immunotherapy in one potential modality.

IL-1ß-associated NNT acetylation orchestrates iron-sulfur cluster maintenance and cancer immunotherapy resistance.

Interleukin-1ß (IL-1ß) is a key protein in inflammation and contributes to tumor progression. However, the role of IL-1ß in cancer is ambiguous or even contradictory. Here, we found that upon IL-1ß stimulation, nicotinamide nucleotide transhydrogenase (NNT) in cancer cells is acetylated at lysine (K) 1042 (NNT K1042ac) and thereby induces the mitochondrial translocation of p300/CBP-associated factor (PCAF). This acetylation enhances NNT activity by increasing the binding affinity of NNT for NADP+ and therefore boosts NADPH production, which subsequently sustains sufficient iron-sulfur cluster maintenance and protects tumor cells from ferroptosis. Abrogating NNT K1042ac dramatically attenuates IL-1ß-promoted tumor immune evasion and synergizes with PD-1 blockade. In addition, NNT K1042ac is associated with IL-1ß expression and the prognosis of human gastric cancer. Our findings demonstrate a mechanism of IL-1ß-promoted tumor immune evasion, implicating the therapeutic potential of disrupting the link between IL-1ß and tumor cells by inhibiting NNT acetylation.

LncRNA CamK-A Regulates Ca2+-Signaling-Mediated Tumor Microenvironment Remodeling.

Cancer cells entail metabolic adaptation and microenvironmental remodeling to survive and progress. Both calcium (Ca2+) flux and Ca2+-dependent signaling play a crucial role in this process, although the underlying mechanism has yet to be elucidated. Through RNA screening, we identified one long noncoding RNA (lncRNA) named CamK-A (lncRNA for calcium-dependent kinase activation) in tumorigenesis. CamK-A is highly expressed in multiple human cancers and involved in cancer microenvironment remodeling via activation of Ca2+-triggered signaling. Mechanistically, CamK-A activates Ca2+/calmodulin-dependent kinase PNCK, which in turn phosphorylates IκBα and triggers calcium-dependent nuclear factor κB (NF-κB) activation. This regulation results in the tumor microenvironment remodeling, including macrophage recruitment, angiogenesis, and tumor progression. Notably, our human-patient-derived xenograft (PDX) model studies demonstrate that targeting CamK-A robustly impaired cancer development. Clinically, CamK-A expression coordinates with the activation of CaMK-NF-κB axis, and its high expression indicates poor patient survival rate, suggesting its role as a potential biomarker and therapeutic target.

dSCOPE: a software to detect sequences critical for liquid-liquid phase separation.

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast.

Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

qPTMplants: an integrative database of quantitative post-translational modifications in plants.

As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

iCysMod: an integrative database for protein cysteine modifications in eukaryotes.

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

gutMEGA: a database of the human gut MEtaGenome Atlas.

The gut microbiota plays important roles in human health through regulating both physiological homeostasis and disease emergence. The accumulation of metagenomic sequencing studies enables us to better understand the temporal and spatial variations of the gut microbiota under different physiological and pathological conditions. However, it is inconvenient for scientists to query and retrieve published data; thus, a comprehensive resource for the quantitative gut metagenome is urgently needed. In this study, we developed gut MEtaGenome Atlas (gutMEGA), a well-annotated comprehensive database, to curate and host published quantitative gut microbiota datasets from Homo sapiens. By carefully curating the gut microbiota composition, phenotypes and experimental information, gutMEGA finally integrated 59 132 quantification events for 6457 taxa at seven different levels (kingdom, phylum, class, order, family, genus and species) under 776 conditions. Moreover, with various browsing and search functions, gutMEGA provides a fast and simple way for users to obtain the relative abundances of intestinal microbes among phenotypes. Overall, gutMEGA is a convenient and comprehensive resource for gut metagenome research, which can be freely accessed at http://gutmega.omicsbio.info.

DrugCVar: a platform for evidence-based drug annotation for genetic variants in cancer.

MOTIVATION: Targeted therapy for cancer-related genetic variants is critical for precision medicine. Although several databases including The Clinical Interpretation of Variants in Cancer (CIViC), The Oncology Knowledge Base (OncoKB), The Cancer Genome Interpreter (CGI) and My Cancer Genome (MCG) provide clinical interpretations of variants in cancer, the clinical evidence was limited and miscellaneous. In this study, we developed the DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidence from literature and the interpretations from the public resources. RESULTS: In total, 7830 clinical evidences for cancer variant-drug targeting were integrated and classified into 10 evidence tiers. Searching and browsing functions were provided for quick queries of cancer variant-drug targeting evidence. Also, batch annotation module was developed for user-provided massive genetic variants in various formats. Details, such as the mutation function, location of the variants in gene and protein structures and mutation statistics of queried genes in various tumor types, were also provided for further investigations. Thus, DrugCVar could serve as a comprehensive annotation tool to interpret potential drugs for cancer variants especially the massive ones from clinical cancer genomics studies. AVAILABILITY AND IMPLEMENTATION: The database is available at http://drugcvar.omicsbio.info. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CrossICC: iterative consensus clustering of cross-platform gene expression data without adjusting batch effect.

Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License.

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation.

Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein-protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

CDK1/2/5 inhibition overcomes IFNG-mediated adaptive immune resistance in pancreatic cancer.

OBJECTIVE: Adaptive immune resistance mediated by the cytokine interferon gamma (IFNG) still constitutes a major problem in cancer immunotherapy. We develop strategies for overcoming IFNG-mediated adaptive immune resistance in pancreatic ductal adenocarcinoma cancer (PDAC). DESIGN: We screened 429 kinase inhibitors for blocking IFNG-induced immune checkpoint (indoleamine 2,3-dioxygenase 1 (IDO1) and CD274) expression in a human PDAC cell line. We evaluated the ability of the cyclin-dependent kinase (CDK) inhibitor dinaciclib to block IFNG-induced IDO1 and CD274 expression in 24 human and mouse cancer cell lines as well as in primary cancer cells from patients with PDAC or ovarian carcinoma. We tested the effects of dinaciclib on IFNG-induced signal transducer and activator of transcription 1 activation and immunological cell death, and investigated the potential utility of dinaciclib in combination with IFNG for pancreatic cancer therapy in vivo, and compared gene expression levels between human cancer tissues with patient survival times using the Cancer Genome Atlas datasets. RESULTS: Pharmacological (using dinaciclib) or genetic (using shRNA or siRNA) inactivation of CDK1/2/5 not only blocks JUN-dependent immune checkpoint expression, but also triggers histone-dependent immunogenic cell death in immortalised or primary cancer cells in response to IFNG. This dual mechanism turns an immunologically 'cold' tumour microenvironment into a 'hot' one, dramatically improving overall survival rates in mouse pancreatic tumour models (subcutaneous, orthotopic and transgenic models). The abnormal expression of CDK1/2/5 and IDO1 was associated with poor patient survival in several cancer types, including PDAC. CONCLUSION: CDK1/2/5 kinase activity is essential for IFNG-mediated cancer immunoevasion. CDK1/2/5 inhibition by dinaciclib provides a novel strategy to overcome IFNG-triggered acquired resistance in pancreatic tumour immunity.

qPhos: a database of protein phosphorylation dynamics in humans.

Temporal and spatial protein phosphorylation dynamically orchestrates a broad spectrum of biological processes and plays various physiological and pathological roles in diseases and cancers. Recent advancements in high-throughput proteomics techniques greatly promoted the profiling and quantification of phosphoproteome. However, although several comprehensive databases have reserved the phosphorylated proteins and sites, a resource for phosphorylation quantification still remains to be constructed. In this study, we developed the qPhos (http://qphos.cancerbio.info) database to integrate and host the data on phosphorylation dynamics. A total of 3 537 533 quantification events for 199 071 non-redundant phosphorylation sites on 18 402 proteins under 484 conditions were collected through exhaustive curation of published literature. The experimental details, including sample materials, conditions and methods, were recorded. Various annotations, such as protein sequence and structure properties, potential upstream kinases and their inhibitors, were systematically integrated and carefully organized to present details about the quantified phosphorylation sites. Various browse and search functions were implemented for the user-defined filtering of samples, conditions and proteins. Furthermore, the qKinAct service was developed to dissect the kinase activity profile from user-submitted quantitative phosphoproteome data through annotating the kinase activity-related phosphorylation sites. Taken together, the qPhos database provides a comprehensive resource for protein phosphorylation dynamics to facilitate related investigations.

ZIC2 is downregulated and represses tumor growth via the regulation of STAT3 in breast cancer.

Although early detection and systemic therapies have improved the diagnosis and clinical cure rate of breast cancer, breast cancer remains the most frequently occurring malignant cancer in women due to a lack of sufficiently effective treatments. Thus, to develop potential targeted therapies and thus benefit more patients, it is helpful to understand how cancer cells work. ZIC family members have been shown to play important roles in neural development and carcinogenesis. In our study, we found that ZIC2 is downregulated in breast cancer tissues at both the mRNA and protein levels. Low expression of ZIC2 was correlated with poor outcome in breast cancer patients and serves as an independent prognostic marker. Furthermore, overexpression of ZIC2 repressed, whereas knockdown of ZIC2 promoted, cell proliferation and colony formation ability in vitro and tumor growth in vivo. Using ChIP-seq and RNA-seq analysis, we screened and identified STAT3 as a potential target for ZIC2. ZIC2 bound to the STAT3 promoter and repressed the promoter activities of STAT3. ZIC2 knockdown induced the expression of STAT3, increasing the level of phosphorylated STAT3. These results suggest that ZIC2 regulates the transcription of STAT3 by directly binding to the STAT3 promoter. Additionally, interfering STAT3 with siRNAs or inhibitors abrogated the oncogenic effects induced by decreased ZIC2. Taken together, our results indicate that ZIC2 serves as a useful prognostic marker in breast cancer and acts as a tumor suppressor by regulating STAT3, implying that STAT3 inhibitors might provide an alternative treatment option for breast cancer patients with ZIC2 downregulation.

Liquid biopsies to track trastuzumab resistance in metastatic HER2-positive gastric cancer.

OBJECTIVE: To monitor trastuzumab resistance and determine the underlying mechanisms for the limited response rate and rapid emergence of resistance of HER2+ metastatic gastric cancer (mGC). DESIGN: Targeted sequencing of 416 clinically relevant genes was performed in 78 paired plasma and tissue biopsy samples to determine plasma-tissue concordance. Then, we performed longitudinal analyses of 97 serial plasma samples collected from 24 patients who were HER2+ to track the resistance during trastuzumab treatment and validated the identified candidate resistance genes. RESULTS: The results from targeted sequencing-based detection of somatic copy number alterations (SCNA) of HER2 gene were highly consistent with fluorescence in situ hybridisation data, and the detected HER2 SCNA was better than plasma carcinoembryonic antigen levels at predicting tumour shrinkage and progression. Furthermore, most patients with innate trastuzumab resistance presented high HER2 SCNA during progression compared with baseline, while HER2 SCNA decreased in patients with acquired resistance. PIK3CA mutations were significantly enriched in patients with innate resistance, and ERBB2/4 genes were the most mutated genes, accounting for trastuzumab resistance in six (35.3%) and five (29.4%) patients in baseline and progression plasma, respectively. Patients with PIK3CA/R1/C3 or ERBB2/4 mutations in the baseline plasma had significantly worse progression-free survival. Additionally, mutations in NF1 contributed to trastuzumab resistance, which was further confirmed through in vitro and in vivo studies, while combined HER2 and MEK/ERK blockade overcame trastuzumab resistance. CONCLUSION: Longitudinal circulating tumour DNA sequencing provides novel insights into gene alterations underlying trastuzumab resistance in HER2+mGC.

LncRNA LINRIS stabilizes IGF2BP2 and promotes the aerobic glycolysis in colorectal cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) play nonnegligible roles in the epigenetic regulation of cancer cells. This study aimed to identify a specific lncRNA that promotes the colorectal cancer (CRC) progression and could be a potential therapeutic target. METHODS: We screened highly expressed lncRNAs in human CRC samples compared with their matched adjacent normal tissues. The proteins that interact with LINRIS (Long Intergenic Noncoding RNA for IGF2BP2 Stability) were confirmed by RNA pull-down and RNA immunoprecipitation (RIP) assays. The proliferation and metabolic alteration of CRC cells with LINRIS inhibited were tested in vitro and in vivo. RESULTS: LINRIS was upregulated in CRC tissues from patients with poor overall survival (OS), and LINRIS inhibition led to the impaired CRC cell line growth. Moreover, knockdown of LINRIS resulted in a decreased level of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), a newly found N6-methyladenosine (m6A) 'reader'. LINRIS blocked K139 ubiquitination of IGF2BP2, maintaining its stability. This process prevented the degradation of IGF2BP2 through the autophagy-lysosome pathway (ALP). Therefore, knockdown of LINRIS attenuated the downstream effects of IGF2BP2, especially MYC-mediated glycolysis in CRC cells. In addition, the transcription of LINRIS could be inhibited by GATA3 in CRC cells. In vivo experiments showed that the inhibition of LINRIS suppressed the proliferation of tumors in orthotopic models and in patient-derived xenograft (PDX) models. CONCLUSION: LINRIS is an independent prognostic biomarker for CRC. The LINRIS-IGF2BP2-MYC axis promotes the progression of CRC and is a promising therapeutic target.

[Post-translational modification (PTM) bioinformatics in China: progresses and perspectives].

Post-translational modifications (PTMs) are essential for regulating conformational changes, activities and functions of proteins, and are involved in almost all cellular pathways and processes. Identification of protein PTMs is the basis for understanding cellular and molecular mechanisms. In contrast with labor-intensive and time-consuming experiments, the PTM prediction using various bioinformatics approaches can provide accurate, convenient, and efficient strategies and generate valuable information for further experimental consideration. In this review, we summarize the current progresses made by Chineses bioinformaticians in the field of PTM Bioinformatics, including the design and improvement of computational algorithms for predicting PTM substrates and sites, design and maintenance of online and offline tools, establishment of PTM-related databases and resources, and bioinformatics analysis of PTM proteomics data. Through comparing similar studies in China and other countries, we demonstrate both advantages and limitations of current PTM bioinformatics as well as perspectives for future studies in China.

Breast-conserving surgery is associated with a lower incidence of suicide among females with breast cancer in the United States: a population-based retrospective cohort study.

BACKGROUND: With comparable overall survival and local recurrence rates with mastectomy, breast-conserving surgery (BCS) has become the cornerstone of therapy for breast cancer; however, the difference in the incidence of suicide between BCS and mastectomy among breast cancer survivors remains unclear. This study evaluated the mortality risk from suicide among breast cancer survivors and compared suicide risk between BCS and mastectomy using a population-based cohort. MATERIALS AND METHODS: Female patients newly diagnosed with first primary breast cancer, recorded in the Surveillance, Epidemiology and End Results database, were included. Standardized mortality ratio (SMR) and cumulative mortality rate from suicide among those who underwent BCS and mastectomy were compared. RESULTS: A total of 1 190 991 patients with newly diagnosed first primary breast cancer were included in the study, of whom 56.5% underwent BCS and 36.1% underwent mastectomy. During the follow-up period, 667 suicides were recorded. Patients who underwent mastectomy exhibited significantly higher suicide mortality than the general population [mortality rate, 8.16 per 100 000 person-years; SMR 1.18 (95% CI 1.05-1.33)], while there was no significant difference in suicide rate between patients who underwent BCS and the general population [SMR 0.92 (95% CI 0.83-1.02)]. Multivariate Cox analysis revealed that BCS, compared with mastectomy, was associated with a significantly decreased risk of suicide among females with breast cancer [hazard ratio 0.80 (95% CI 0.68-0.95); P = 0.009]. CONCLUSION: BCS was associated with a significantly lower incidence of suicide among females with breast cancer. BCS offers a compelling option for improving the quality of life and self-esteem of patients with cancer and provides a novel perspective on cancer management.