Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

[Purpose]To analyze the trends in incidence,mortality and potential life loss of esophageal cancer in Linzhou City of Henan Province from 2010 to 2019.[Methods]The data of esophageal cancer incidence and mortality from 2010 to 2019 were collected from Linzhou cancer registries.The crude incidence and mortality rates,age-standardized rates(ASR)by sex and age group,the potential years of life lost(PYLL),average potential years of life lost(APYLL),and potential years of life lost rate(PYLLR)were calculated.The average annual percentage change(AAPC)from 2010 to 2019 were analyzed with Joinpoint software.[Results]From 2010 to 2019,there were a total of 8 447 newly diagnosed cases and 6 475 deaths of esophageal cancer in Linzhou.The ASR incidence and ASR mortality of esophageal cancer in the total population,males,females all showed significant downward trends,with AAPCs of-3.97％,-4.35％,-3.29％and-3.78％,-2.68％,-4.95％,respectively(all P＜0.05).The crude incidence and mortality rates in all age groups also showed significant downward trends.The AAPCs of incidence rate for the age groups of 0～49,50～59,60～69,and ≥70 years old were-9.92％,-8.27％,-1.41％,and-3.86％,respectively(all P＜0.05),and the AAPCs of mortality rate were-950％,-12.36％,-2.61％,and-2.98％,respectively(all P＜0.05).From 2010 to 2019,the total PYLL caused by esophageal cancer was 60 880 person years,APYLL was 13.73 person years,and PYLLR was 5.77‰.The PYLL,APYLL,and the PYLLR of the total population and those stratified by sex all showed a decreasing trend(all P＜0.05).[Con-clusion]From 2010 to 2019,the incidence,mortality and potential life loss of esophageal cancer in Linzhou City all decreased,and the long-term effect and screening programs is significant.How-ever,the risk of esophageal cancer among men and the elderly is still relatively high,indicating that more targeted prevention and control strategies should be developed.

[Purpose]To analyze the trends of incidence and mortality of malignant tumors in Linzhou City of Henan Province from 2010 to 2019.[Methods]The incidence and mortality data of malignant tumors of Linzhou cancer registration areas from 2010 to 2019 were collected and evaluated for data quality.The crude incidence/mortality rates and age-standardized incidence/mortality rates by Chinese standard population(ASIRC/ASMRC)were calculated by sex,age and can-cer type.Joinpoint software was used to calculate the average annual percentage change(AAPC)to analyze the trends from 2010 to 2019.[Results]From 2010 to 2019,the crude incidence of malig-nant tumors in Linzhou City showed an upward trend,with an AAPC of 2.09％(95％CI:0.58％～3.63％),while the ASIRC tended to be stable.The incidence of malignant tumors showed a signifi-cant upward trend in the 15～29 and 60～69 age groups,and a significant downward trend in the 70～79 age group.From 2010 to 2019,the ASIRC of esophageal cancer and stomach cancer in both men and women showed a significant downward trend,while that of lung cancer and prostate cancer increased in men,and the incidences of thyroid cancer,uterus cancer,cervical cancer,lung cancer and breast cancer increased significantly in women.From 2010 to 2019,the crude mortality of malignant tumors in Linzhou showed a significant upward trend,with an AAPC of 1.18％(95％CI:0.88％～1.48％),while ASMRC showed a significant downward trend,with an AAPC of-1.63％(95％CI:-1.86％～-1.40％).The mortality increased in the group aged 80 and above,while the other age groups remained in a downward or stable state.From 2010 to 2019,the ASMRC of stomach cancer and esophageal cancer in both men and women showed a down-ward trend,while those of prostate cancer,and malignant tumors of the lip,oral cavity and pha-ryngeal in men increased,and that of ovarian cancer in women increased significantly.[Conclu-sion]The disease burden of malignant tumors in Linzhou City is still heavy.The incidence of common cancer types such as thyroid cancer,prostate cancer and lung cancer shows a significant-ly increasing trends from 2010 to 2019.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective:To investigate the application and clinical efficacy of customized, 3D-printed femoral bone marrow stems in the revision of tumor prostheses.Methods:A retrospective analysis was performed for the data of 11 patients (7 males and 4 females) aged 53.1±11.7 years (range, 38-75 years), who underwent 3D-printed customized revision of femoral intramedullary stems due to loosening of femoral tumor prostheses at the 960th Hospital of the Joint Support Force of the PLA from June 2021 to June 2023. The pathological types of tumors associated with the initial surgeries included 4 cases of giant cell tumor of bone, 5 cases of osteosarcoma, 1 case of chondrosarcoma, and 1 case of plasma cell tumor. The tumor was located at the distal femur in 8 cases and the proximal femur in 3 cases. The procedures included 3 initial revisions, 7 secondary revisions, and 1 tertiary revision. The average limb shortening measured 4.6±2.2 cm (range, 2.5-9.0 cm). Prior to revision, all prostheses were fixed with bone cement, revealing enlargement of the femoral medullary cavity and cortical bone thinning. Among them, 5 cases had intramedullary stems permeabilizing the femoral cortex, and 1 case had femoral cleavage fractures. All 11 patients received personalized data for the design and 3D printing of femoral bone marrow stems.Results:The lengths and diameters of the 3D-printed porous femoral bone marrow stems ranged from 80 to 160 mm and 20 to 22 mm, respectively. Ten patients were fitted with cylindrical intramedullary handles, while one received a conical intramedullary handle. A successful revision with the 3D-printed stems was achieved in 10 patients; however, 1 case failed to accommodate the conical handle and was instead revised with a bone cement prosthesis. During the implantation of the intramedullary stems, three patients experienced minor cortical splitting, which was managed with bundling and fixation during the procedure. Immediate stability was attained for all prostheses during surgery, yet postoperative limb shortening did not undergo significant correction. All patients exhibited normal healing of their postoperative incisions. The visual analog scale for limb pain decreased significantly from 8.0±0.8 points before surgery to 1.0±0.4 points three months postoperatively ( t=25.957, P<0.001). By six months after the surgery, none of the patients reported any limb pain. Follow-up data for all 11 patients indicated an average follow-up duration of 25.2±7.5 months (range, 16-36 months), during which limb function improved satisfactorily. The Musculoskeletal Tumor Society (MSTS) score increased from 7.9±1.4 points preoperatively to 20.9±2.7 points at the last follow-up, with this change also being statistically significant ( t=14.229, P<0.001). Imaging evaluations revealed normal lower limb force lines, no rotation or longitudinal displacement of the prosthetic stem, and successful integration with the femur. Conclusion:Personalized intramedullary stems produced through 3D printing demonstrate significant clinical effectiveness in femoral tumor prosthesis revision surgery, making them a viable option for such procedures.

[Purpose]To analyze the trends in incidence,mortality and potential life loss of esophageal cancer in Linzhou City of Henan Province from 2010 to 2019.[Methods]The data of esophageal cancer incidence and mortality from 2010 to 2019 were collected from Linzhou cancer registries.The crude incidence and mortality rates,age-standardized rates(ASR)by sex and age group,the potential years of life lost(PYLL),average potential years of life lost(APYLL),and potential years of life lost rate(PYLLR)were calculated.The average annual percentage change(AAPC)from 2010 to 2019 were analyzed with Joinpoint software.[Results]From 2010 to 2019,there were a total of 8 447 newly diagnosed cases and 6 475 deaths of esophageal cancer in Linzhou.The ASR incidence and ASR mortality of esophageal cancer in the total population,males,females all showed significant downward trends,with AAPCs of-3.97％,-4.35％,-3.29％and-3.78％,-2.68％,-4.95％,respectively(all P＜0.05).The crude incidence and mortality rates in all age groups also showed significant downward trends.The AAPCs of incidence rate for the age groups of 0～49,50～59,60～69,and ≥70 years old were-9.92％,-8.27％,-1.41％,and-3.86％,respectively(all P＜0.05),and the AAPCs of mortality rate were-950％,-12.36％,-2.61％,and-2.98％,respectively(all P＜0.05).From 2010 to 2019,the total PYLL caused by esophageal cancer was 60 880 person years,APYLL was 13.73 person years,and PYLLR was 5.77‰.The PYLL,APYLL,and the PYLLR of the total population and those stratified by sex all showed a decreasing trend(all P＜0.05).[Con-clusion]From 2010 to 2019,the incidence,mortality and potential life loss of esophageal cancer in Linzhou City all decreased,and the long-term effect and screening programs is significant.How-ever,the risk of esophageal cancer among men and the elderly is still relatively high,indicating that more targeted prevention and control strategies should be developed.