Tumor-derived mesenchymal progenitor cell-related genes in the regulation of breast cancer proliferation.

Background: Breast cancer (BC) is one of the most common malignancies worldwide, and its development is affected in various ways by the tumor microenvironment (TME). Tumor-derived mesenchymal progenitor cells (MPCs), as the most important components of the TME, participate in the proliferation and metastasis of BC in several ways. In this study, we aimed to characterize the genes associated with tumor-derived MPCs and determine their effects on BC cells. Methods: Tumor-derived MPCs and normal breast tissue-derived mesenchymal stem cells (MSCs) were isolated from tissues specimens of patients with BC. We conducted culture and passage, phenotype identification, proliferation and migration detection, inflammatory factor release detection, and other experiments on isolated MPCs from tumors and MSCs from normal breast tissues. Three paired tumor-derived MPCs and normal breast tissue-derived MSCs were then subjected to transcriptome analysis to determine the expression profiles of the relevant genes, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to further confirm gene expression. Subsequently, the overexpression plasmids were transfected into tumor-derived MPCs, and the expression of various inflammatory factors of tumor-derived MPCs and their proliferation were characterized with a cell viability test reagent (Cell Counting Kit 8). Subsequently, the transfected tumor-derived MPCs were cocultured with BC cells using a conditioned medium coculture method to clarify the role of tumor-derived MSCs in BC. Results: Tumor-derived MPCs expressed stem cell characteristics including CD105, CD90, and CD73 and exhibited adipogenic and osteogenic differentiation in vitro. The proliferation of tumor-derived MPCs was significantly lower than that of normal breast tissue-derived MSCs, and the invasive metastatic ability was comparable; however, MPCs were found to release inflammatory factors such as interleukin 6 (IL-6) and transforming growth factor ß (TGF-ß). Transcriptome analysis showed that stomatin (STOM), collagen and calcium binding EGF domains 1 (CCBE1), and laminin subunit alpha 5 (LAMA5) were significantly upregulated in tumor-derived MPCs. Among them, STOM was highly expressed in tumor-derived MPCs, which mediated the slow proliferation of MPCs and promoted the proliferation of BC cells. Conclusions: STOM, CCBE1, and LAMA5 were highly expressed in tumor-derived MPCs, with STOM being found to retard the proliferation of MPCs but promote the proliferation of BC cells. There findings present new possibilities in targeted microenvironmental therapy for BC.

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.

Background: Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. Methods: The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Results: Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. Conclusions: The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Diagnostic accuracy of cone-beam breast computed tomography and head-to-head comparison of digital mammography, magnetic resonance imaging and cone-beam breast computed tomography for breast cancer: a systematic review and meta-analysis.

Background: Cone-beam breast computed tomography (CBBCT) is a new breast imaging technique, however, CBBCT is not yet widely used, and its future application will depend on its diagnostic potential and application value. Therefore, it is of great clinical significance to systematically review and analyze the diagnostic accuracy of CBBCT for breast cancer detection in existing studies and compare it with other traditional imaging methods for the diagnosis of breast lesions. Methods: We searched PubMed, Embase, Web of Science, and Chinese databases until August 2022 for relevant papers. Studies evaluating the diagnostic accuracy of CBBCT in women with suspected breast cancer were included. Each study's quality was evaluated using the Quality Assessment of Diagnostic Performance Studies-2 (QUADAS-2) instrument. Results: Eighteen studies with a total of 1,792 patients were included in the analysis. The overall pooled sensitivity and specificity of CBBCT in diagnosing breast cancer were 0.95 [95% confidence interval (CI): 0.91-0.97] and 0.72 (95% CI: 0.62-0.80), respectively. The area under the curve (AUC) for CBBCT was 0.92 (95% CI: 0.90-0.94). In a head-to-head comparison of CBBCT and digital mammography (DM), eight trials with 992 patients were included in the study, and the AUCs for CBBCT and DM were 0.94 (95% CI: 0.92-0.96) and 0.83 (95% CI: 0.80-0.83), respectively. In a head-to-head comparison of CBBCT and magnetic resonance imaging (MRI), four trials with 203 patients were included in the analysis; the AUC for CBBCT and MRI were 0.88 (95% CI: 0.85-0.91) and 0.96 (95% CI: 0.94-0.97), respectively. Conclusions: This meta-analysis of CBBCT test accuracy indicated encouraging diagnostic performance. In the summary of head-to-head comparative studies, there is a tendency for CBBCT to have greater diagnostic accuracy than DM, although its diagnostic performance is marginally inferior to that of MRI. However, the meta-analysis results were derived from studies with limited sample sizes. There is a need for more extensive research in this setting.

Identification of Hub Genes and Biological Mechanisms Associated with Non-Alcoholic Fatty Liver Disease and Triple-Negative Breast Cancer.

The relationship between non-alcoholic fatty liver disease (NAFLD) and triple-negative breast cancer (TNBC) has been widely recognized, but the underlying mechanisms are still unknown. The objective of this study was to identify the hub genes associated with NAFLD and TNBC, and to explore the potential co-pathogenesis and prognostic linkage of these two diseases. We used GEO, TCGA, STRING, ssGSEA, and Rstudio to investigate the common differentially expressed genes (DEGs), conduct functional and signaling pathway enrichment analyses, and determine prognostic value between TNBC and NAFLD. GO and KEGG enrichment analyses of the common DEGs showed that they were enriched in leukocyte aggregation, migration and adhesion, apoptosis regulation, and the PPAR signaling pathway. Fourteen candidate hub genes most likely to mediate NAFLD and TNBC occurrence were identified and validation results in a new cohort showed that ITGB2, RAC2, ITGAM, and CYBA were upregulated in both diseases. A univariate Cox analysis suggested that high expression levels of ITGB2, RAC2, ITGAM, and CXCL10 were associated with a good prognosis in TNBC. Immune infiltration analysis of TNBC samples showed that NCF2, ICAM1, and CXCL10 were significantly associated with activated CD8 T cells and activated CD4 T cells. NCF2, CXCL10, and CYBB were correlated with regulatory T cells and myeloid-derived suppressor cells. This study demonstrated that the redox reactions regulated by the NADPH oxidase (NOX) subunit genes and the transport and activation of immune cells regulated by integrins may play a central role in the co-occurrence trend of NAFLD and TNBC. Additionally, ITGB2, RAC2, and ITGAM were upregulated in both diseases and were prognostic protective factors of TNBC; they may be potential therapeutic targets for treatment of TNBC patients with NAFLD, but further experimental studies are still needed.

Neoadjuvant therapy with vs. without anthracyclines for HER2-positive breast cancer: a systematic review and meta-analysis.

Background: Neoadjuvant therapy has become the standard treatment for early human epidermal growth factor receptor 2 (HER2)-positive breast cancer, with most regimens using a combination of anti-HER2-targeted drugs and chemotherapy. However, the combination of anthracyclines and trastuzumab has high cardiac toxicity, and the efficacy evaluation of targeted therapy with or without anthracyclines is not unified. The purpose of this meta-analysis was to evaluate the relative efficacy and safety of anti-HER2-targeted therapy combined with vs. without anthracyclines neoadjuvant treatment. Methods: The following databases: PubMed, Medline, Embase, and Cochrane Library were systematically searched. Study inclusion was determined according to PICOS principles. PICOS: Patients, HER2-positive breast cancer; Intervention, anti-HER2-targeted therapy combined with anthracyclines; Control, without anthracyclines; Outcomes, the percentage of pathologic complete response (pCR), breast-conserving surgery (BCS), and grade 3 or worse adverse events according to CTCAE version 4.03; Studies, randomized controlled trials (RCTs) and retrospective studies. The meta-analysis was performed using RevMan5.3 software, and the odds ratio (OR) with 95% confidence intervals (CIs) was performed. Results: In total, 11 articles involving 1,998 patients were included with 1,155 patients in the anthracycline-containing group and 843 patients in the anthracycline-free group. For efficacy, there was no statistically significant difference in the percentage of pCR (OR 0.95; 95% CI: 0.61-1.48; P=0.83) and BCS (OR 1.18; 95% CI: 0.93-1.49; P=0.17) on anthracycline-free regimens compared with anthracycline-containing regimens. For safety, the combined effect values showed a significantly lower incidence of left ventricular ejection fraction decreases with the anthracycline-free regimen than with the anthracycline-containing regimen (OR 0.50; 95% CI: 0.35-0.71; P=0.0001). Other adverse effects and survival events were generally not statistically different in incidence between the two groups. The subgroup analysis suggested that hormone receptor status might be the source of heterogeneity in this study. Conclusions: Our study demonstrated that the targeted therapy combined with anthracyclines was associated with an increased risk of cardiac adverse events compared with the anthracycline-free group, with no significant difference in the percentage of pCR and BCS. Due to the high heterogeneity of this meta-analysis, more studies with longer follow-up are needed to validate the current findings and to further explore the removal and retention of anthracyclines.

Contralateral prophylactic mastectomy for unilateral breast cancer in Chinese female population: a retrospective cohort study.

Background: Due to differences in socioeconomic and cultural backgrounds, the characteristics and prognosis of Asian female patients choosing contralateral prophylactic mastectomy (CPM) are likely to be different from Western patients. To fill the research gap of CPM in Asian populations, this study aims to explore the application trend, survival benefits, decision-making factors, and satisfaction of CPM based on the Chinese patients undergoing CPM. Methods: The 0-III stage unilateral breast cancer (UBC) patients who received breast surgery in the Chinese PLA General Hospital from 2005 to 2017 were selected. The surgical procedures included simple mastectomy (SM), nipple-sparing mastectomy (NSM), breast conserving surgery (BCS), and CPM. Cox proportional regression analyses and Kaplan-Meier (KM) curve were performed to compare the overall survival (OS) and disease-free survival (DFS) rates between CPM group and unilateral mastectomy (UM) group. Proportional propensity score matching (PSM) with a 1:1 ratio was used to match the two groups and secondary survival analysis was performed. Logistic regression models were used to test predictive factors related to patients' CPM surgical decision-making. Results: Four thousand two hundred and seventy-six patients were included in the study, with 73 patients receiving CPM, 3,567 receiving SM, 151 receiving NSM, and 485 receiving BCS. CPM surgery was first used in 2007, with a peak application rate of 3.02% in 2016. Three thousand seven hundred and ninety-one patients were included in the survival analysis, with a median follow-up time of 66.60 months. Compared to UM patients, neither the KM survival curve nor Cox regression hazard analyses of CPM showed better OS (P=0.963; P=0.834). After PSM, CPM also did not exhibit significant survival benefits in OS (P=0.335) and DFS (P=0.409). The logistic regression analyses showed that NSM surgery and lower tumor-node-metastasis (TNM) stage were independent factors to promote the CPM decision-making of patients. The CPM group showed high overall satisfaction (84.9%) and relatively low appearance satisfaction (69.9%). Conclusions: CPM was practiced for the first time since 2007 in our hospital. CPM does not provide any OS and DFS benefits compared to UM and the appearance satisfaction procedure was relatively low. Therefore, clinicians should fully communicate with patients before surgery and be more cautious in giving CPM recommendations.

Advancement of secretory breast carcinoma: a narrative review.

Background and Objective: Secretory breast carcinoma (SBC) is a rare breast malignancy. Most available studies on SBC are case reports or small case series, and the few large-sample studies available lack critical information due to database limitations. To improve the understanding of SBC and provide a reference for clinical practice, we systematically reviewed the demographic, clinical, pathologic, and genetic characteristics of SBC, as well as its treatment and prognosis. Methods: We conducted a PubMed search with the keywords "secretory breast carcinoma" or "juvenile breast carcinoma". Relevant English-language publications published from January 1966 to February 2022 were screened manually at 3 levels-title, abstract, and full text-to identify the articles that presented the demographic, clinical, pathologic, and genetic characteristics of SBC, as well as its treatment and prognosis. Key Content and Findings: SBC lacks specific clinical manifestations and has typical pathological and molecular characteristics, including intracellular and extracellular eosinophilic secretions, immune spectrum similar to hormone receptor-positive tumors, and the ETV6-NTRK3 fusion gene. Surgery remains the primary treatment for SBC. Postoperative radiotherapy is recommended by most researchers for adult SBC but not for pediatric patients. The evidence of chemotherapy and endocrine therapy is insufficient, and targeted therapy of the ETV6-NTRK3 fusion gene shows a good response. Most patients with SBC have a good prognosis except for a few patients who experience distant metastases. Future studies will be focused on the molecular characteristics of those patients with SBC who have a poor prognosis. Conclusions: The development of histopathology and molecular genetics has promoted the progress of the clinical diagnosis of SBC. The purpose of this review is to serve as a guide for the better clinical treatment of SBC, particularly in the areas of disease identification and prognosis classification for patients.

Smoke analysis of a new surgical system that applies low-temperature plasma.

Background: The high-frequency electrotome (ES), which is widely used in surgical procedures, generates surgical smoke that is potentially hazardous to operating personnel. Previous research shows that the PlasmaBlade (PB) may be able to overcome this problem. The present study set out to analyze potentially hazardous surgical smoke generated during electrosurgery by the ES, the PB, and. a new surgical system that applies low-temperature plasma, the NTS-100. Methods: In vitro and in vivo healthy porcine models were used to compare volatile organic compounds (VOCs) and particulate matter (PM) in smoke generated by the NTS-100, the PB, and the conventional ES when cutting liver, muscle, and skin and subcutaneous tissues. The detected indexes included the VOCs in surgical smoke, the concentration and percentage of each part, the PM2.5 concentration, the mass of particles, and the diameter distribution of particles. Results: The smoke generated by the NTS-100 contained fewer hazardous components than that generated by the ES (P<0.05) and a comparable amount to that generated by the PB (P>0.05). The PM2.5 concentration and mass of particles in the smoke generated by the NTS-100 were lower than those with the ES (P<0.05 and P<0.01, respectively) and similar to those with the PB (P>0.05). The NTS-100 generated larger particles than did the ES and the PB (P<0.05). Conclusions: Surgical smoke contains harmful VOCs and PM, but the NTS-100 generated less hazardous surgical smoke than did the conventional ES and performed comparably to the PB. Therefore, using the NTS-100 may reduce the potential hazard of surgical smoke to operating room personnel.

Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI.

Importance: The utilization of artificial intelligence for the differentiation of benign and malignant breast lesions in multiparametric MRI (mpMRI) assists radiologists to improve diagnostic performance. Objectives: To develop an automated deep learning model for breast lesion segmentation and characterization and to evaluate the characterization performance of AI models and radiologists. Materials and methods: For lesion segmentation, 2,823 patients were used for the training, validation, and testing of the VNet-based segmentation models, and the average Dice similarity coefficient (DSC) between the manual segmentation by radiologists and the mask generated by VNet was calculated. For lesion characterization, 3,303 female patients with 3,607 pathologically confirmed lesions (2,213 malignant and 1,394 benign lesions) were used for the three ResNet-based characterization models (two single-input and one multi-input models). Histopathology was used as the diagnostic criterion standard to assess the characterization performance of the AI models and the BI-RADS categorized by the radiologists, in terms of sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC). An additional 123 patients with 136 lesions (81 malignant and 55 benign lesions) from another institution were available for external testing. Results: Of the 5,811 patients included in the study, the mean age was 46.14 (range 11-89) years. In the segmentation task, a DSC of 0.860 was obtained between the VNet-generated mask and manual segmentation by radiologists. In the characterization task, the AUCs of the multi-input and the other two single-input models were 0.927, 0.821, and 0.795, respectively. Compared to the single-input DWI or DCE model, the multi-input DCE and DWI model obtained a significant increase in sensitivity, specificity, and accuracy (0.831 vs. 0.772/0.776, 0.874 vs. 0.630/0.709, 0.846 vs. 0.721/0.752). Furthermore, the specificity of the multi-input model was higher than that of the radiologists, whether using BI-RADS category 3 or 4 as a cutoff point (0.874 vs. 0.404/0.841), and the accuracy was intermediate between the two assessment methods (0.846 vs. 0.773/0.882). For the external testing, the performance of the three models remained robust with AUCs of 0.812, 0.831, and 0.885, respectively. Conclusions: Combining DCE with DWI was superior to applying a single sequence for breast lesion characterization. The deep learning computer-aided diagnosis (CADx) model we developed significantly improved specificity and achieved comparable accuracy to the radiologists with promise for clinical application to provide preliminary diagnoses.

A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging.

Background: The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS. Methods: This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology. Results: There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant. Conclusions: Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.

Progress on deep learning in digital pathology of breast cancer: a narrative review.

Background and Objective: Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology. Methods: A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. Key Content and Findings: DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches. Conclusions: Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

Serotonin/HTR1E signaling blocks chronic stress-promoted progression of ovarian cancer.

Rationale: Psychological stress has been linked to cancer development and resistance to therapy by many epidemiological and clinical studies. Stress-induced immunosuppressive microenvironment by stress hormones, in particular glucocorticoids, has been extensively studied. However, the impacts of other stress-related neurotransmitters, such as serotonin (5-hydroxytryptamine, 5-HT), on cancer development just start to be revealed. Here, we aimed to identify novel neurotransmitters involved in stress-induced growth and dissemination of ovarian cancer (OC) and reveal the major underlying signaling pathway and the therapeutic significance. Methods: Through a genome-wide CRISPR/Cas9 knockout screen in the murine orthotopic model of ovarian carcinoma (OC), we identified candidate genes regulating the peritoneal dissemination of OC. Among them, we picked out HTR1E, one member of 5-HT receptor family specifically expressed in the ovary and endometrium in addition to brain. The correlation of HTR1E expression with OC progression was analyzed in OC patient specimen by quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC). Gain-of-function and loss-of-function analyses were performed to explore the functions of 5-HT/HTR1E signaling in OC growth and dissemination in vitro and in vivo. In addition, we investigated the therapeutic values of HTR1E specific agonist and small molecular inhibitors against HTR1E downstream factor SRC in a stressed murine OC xenograft model. Results: In OC patients, the HTR1E expression is dramatically decreased in peritoneal disseminated OC cells, which correlates with poor clinical outcome. Silence of HTR1E in OC cells greatly promotes cell proliferation and epithelial mesenchymal transition (EMT) by the activation of SRC-mediated downstream signaling pathways. Furthermore, chronic stress results in significantly decreased serotonin in the ovary and the enhanced OC growth and peritoneal dissemination in mice, which can be strongly inhibited by specific HTR1E agonist or the SRC inhibitor. Conclusions: We discovered the essential role of serotonin/HTR1E signaling in preventing the chronic psychological stress-promoted progression of OC, suggesting the potential therapeutic value of the HTR1E specific agonist and the SRC inhibitor for OC patients who are suffering from psychological stress.

Characteristics of HIV-1 molecular transmission networks and drug resistance among men who have sex with men in Tianjin, China (2014-2018).

BACKGROUND: In Tianjin, China, there is a relatively high prevalence of HIV in men who have sex with men (MSM). The number of HIV cases in Tianjin is also increasing. We investigated the HIV molecular transmission network, genetic tropisms, and drug resistance mutations in Tianjin. METHODS: Blood samples were collected from 510 newly diagnosed antiretroviral therapy (ART)-naïve HIV-1-infected subjects among MSM in Tianjin. Partial pol and env genes were sequenced and used for phylogenetic, genetic tropism, and genotypic drug resistance analyses. Molecular clusters were identified with 1.5% genetic distance and 90% bootstrap support. RESULTS: Among the 436 HIV-1 pol sequences obtained from the study participants, various genotypes were identified, including CRF01_AE (56.9%), CRF07_BC (27.8%), B (7.3%), CRF55_01B (4.1%), unique recombinant forms (URFs) (3.7%), and CRF59_01B (0.2%). A higher prevalence of X4 viruses was observed in individuals infected with CRF55_01B (56.3%) and CRF01_AE (46.2%) than with other subtypes. Of all 110 sequences in the 36 clusters, 62 (56.4%) were observed in 23 CRF01_AE clusters and 18 (16.4%) in four CRF07_BC clusters. Eight sequences clustered with at least one other shared the same drug resistance mutation (DRM). In different cluster sizes, the distributions of individuals by age, presence of sexually transmitted disease, and presence of DRMs, were significantly different. CONCLUSION: We revealed the characteristics of HIV molecular transmission, tropism, and DRMs of ART-naïve HIV-infected individuals among the MSM population in Tianjin. Identifying infected persons at risk of transmission is necessary for proposing counseling and treating these patients to reduce the risk of HIV transmission.