Objective To analyze the expression of H2A histone family, member X (H2AFX) gene in lung adenocarcinoma and its influence on prognosis. Methods We analyzed the expression level of H2AFX gene in the tumor tissues (497 cases) and normal adjacent tissues (54 cases) of lung adenocarcinoma patients via The Cancer Genome Atlas. The patients were divided into high expression group and low expression group according to the expression level of H2AFX gene in lung adenocarcinoma samples. The relationship between H2AFX and clinicopathological features of patients was analyzed through logistic regression. Kaplan-Meier survival curve and log-rank test were used to study the correlation between H2AFX expression and the prognosis of lung adenocarcinoma patients. Univariate and multiple Cox regression analyses were performed to determine the prognostic significance of H2AFX expression in lung adenocarcinoma patients. The research also covered H2AFX-related pathways of genes in the development of lung adenocarcinoma with gene set enrichment analysis (GSEA). Results The H2AFX expression was higher in lung adenocarcinoma tissues than that in normal adjacent tissues (P<0.001). Besides, it was significantly correlated with age (P<0.001), T staging (P=0.007), and N staging (P=0.010), but had little to do with M staging or gender (P>0.05). Kaplan-Meier survival curve and log-rank test showed that the survival rate of patients with high H2AFX expression was vastly lower than that of patients with low H2AFX expression (P<0.001). Multiple Cox regression analysis demonstrated that H2AFX could be an independent prognostic factor for lung adenocarcinoma [hazard ratio=1.41, 95% confidence interval (1.11, 1.78), P=0.004]. The results of GSEA displayed that H2AFX was involved in cell cycle, homologous recombination, DNA replication, base excision and repair, spliceosome, mismatch repair, p53 signaling pathway, nucleotide excision and repair, RNA degradation, RNA polymerase, and other pathways. Conclusions The expression of H2AFX gene is high in lung adenocarcinoma, and closely connected to the prognosis, occurrence, and evolution of lung adenocarcinoma. This gene can be one of the new molecular markers and therapeutic targets for lung adenocarcinoma.
Objective To analyze the relationship between the residence and oncological characteristics of colorectal patients served by Sichuan University West China Hospital as a regional center in the current version of the Database from Colorectal Cancer (DACCA). Methods The DACCA version selected for this data analysis was the updated version on January 5, 2022. The data items analyzed included: residence, precancerous lesions, family history of cancer, tumor location, tumor morphology, tumor orientation, tumor pathology, tumor differentiation and preoperative TNM staging. According to the regional distribution of colorectal cancer patients' residence in the database, they were divided into Sichuan group and non-Sichuan group, and the Sichuan group was further divided into Sichuan-Chengdu group and Sichuan-non-Chengdu group. Results The DACCA database was filtered by conditions to obtain 7 232 valid data. ① The composition ratio of precancerous lesions in different places of residence: The difference between the Sichuan group and the non-Sichuan group was statistically significant (χ2=14.462, P=0.003), and the difference between the Sichuan-Chengdu group and the Sichuan-non-Chengdu group was not statistically significant (χ2=7.591, P=0.101). ② Composition ratio of family history of cancer in different places of residence: In the family history of cancer in oneself, the difference between Sichuan group and non-Sichuan group as well as between Sichuan-Chengdu group and Sichuan-non-Chengdu group were not statistically significant (χ2=1.121, P=0.606; χ2=1.047, P=0.621). In the family history of cancer in relatives, the differences in the composition ratio of different tumor histories between the Sichuan group and the non-Sichuan group, and between the Sichuan-Chengdu group and the Sichuan-non-Chengdu group were not statistically significant (χ2=0.813, P=0.692; χ2=2.696, P=0.262). ③ Tumor site composition ratios in different places of residence: The difference between Sichuan group and non-Sichuan group was not statistically significant (χ2=0.476, P=0.490), and the difference between Sichuan-Chengdu group and Sichuan-non-Chengdu group was statistically significant (χ2=36.216, P<0.001). ④ Tumor morphology composition ratio in different places of residence: The difference between Sichuan group and non-Sichuan group was statistically significant (χ2=19.560, P<0.001), and the difference between Sichuan-Chengdu group and Sichuan-non-Chengdu group was not statistically significant (χ2=5.377, P=0.247). ⑤ Composition ratio of tumor orientation in different places of residence: The differences in composition ratio of tumor orientation between Sichuan group and non-Sichuan group and between Sichuan-Chengdu group and Sichuan-non-Chengdu group were statistically significant (χ2=17.484, P=0.005; χ2=26.820, P<0.001). ⑥ Composition ratio of tumor pathological properties under different residence: The differences in the comparison of pathological properties between Sichuan group and non-Sichuan group as well as between Sichuan-Chengdu group and Sichuan-non-Chengdu group of CRC patients were not statistically significant (χ2=8.136, P=0.408; χ2=7.278, P=0.506). ⑦ Composition ratio of tumor differentiation degree under different residence groupings: the differences in the composition ratio of tumors with different degrees of differentiation were not statistically significant between Sichuan group and non-Sichuan group, and between Sichuan-Chengdu group and Sichuan-non-Chengdu group (H=0.289, P=0.591; H=0.156, P=0.693). ⑧ The composition ratio of TNM staging of tumors before operation in different places of residence: between the Sichuan group and the non-Sichuan group, the difference in the composition ratio of preoperative TNM staging of CRC patients was statistically significant (H=8.023, P=0.005); between the Sichuan-Chengdu group and the Sichuan-non-Chengdu group, the difference in the composition ratio of preoperative TNM staging of CRC patients was not statistically significant (H=0.218, P=0.640). Conclusions Data analysis in DACCA reveal multiple associations between the place of residence and oncological characteristics of CRC patients. There are differences in the composition of the types of precancerous lesions among CRC patients in different places of residence. The proportion of CRC is higher in the family history of cancer. In terms of the site of tumor occurrence, the proportion of tumors located in the rectum is higher than that in the colon. In the composition of tumor morphology in all regions, the ulcerative type is the most frequent. The composition of tumor orientation is different in patients with CRC, and those who has involved a circle of the intestinal wall are the most frequent. Most CRC patients are already in middle or late stage when the tumor is discovered, and the proportion of middle or late stage patients in non-Sichuan provinces was even higher.
ObjectiveTo analyze the risk factors for early mortality in patients with stage Ⅳ colorectal cancer, and further construct and validate Nomogram prediction model for early mortality in stage Ⅳ colorectal cancer. MethodsA retrospective analysis was conducted on the clinical and pathological data of stage Ⅳ colorectal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 2018 to 2020. The study data was randomly divided into a training cohort and a validation cohort at a ratio of 8∶2. Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for early mortality in stage Ⅳ colorectal cancer patients, and Nomogram prediction model was further constructed. Receiver operating characteristic curve (ROC), calibration curve, and clinical decision curve analysis (DCA) were plotted. ResultsAge (50–70 group, OR=1.984, P=0.007; >70 group, OR=1.997, P=0.008), unmarried (OR=1.342, P=0.025), primary tumor differentiation of G3+G4 (OR=1.817, P<0.001), T4 stage (OR=1.434, P=0.009), N2 stage (OR=1.621, P<0.001), M1c stage (OR=1.439, P=0.036), no chemotherapy (OR=21.820, P<0.001), bone metastasis (OR=2.000, P=0.042), brain metastasis (OR=6.715, P=0.001) and liver metastasis (OR=1.886, P<0.001) were risk factors for all-cause early death in stage Ⅳ colorectal cancer patients. Age（50–70 group, OR=2.025, P=0.008; >70 group, OR=1.925, P=0.017), primary tumor differentiation grade of G3+G4 (OR=1.818, P<0.001), T4 stage (OR=1.424, P=0.013), N2 stage (OR=1.637, P<0.001), M1c stage (OR=1.541, P=0.016), no chemotherapy (OR=21.832, P<0.001), brain metastasis (OR=6.089, P=0.001), liver metastasis (OR=2.100, P<0.001) were factors for cancer-specific early death of stages Ⅳ colorectal cancer patients. Based on these variables, we constructed two Nomogram prediction models for all-cause early death and cancer-specific early death in stage Ⅳ colorectal cancer patients. The area under curve (AUC) value of the all-cause early death prediction model in the training queue was 0.874 [95% CI (0.855, 0.893)], and the AUC value of the cancer specific early death prediction model was 0.874 [95%CI (0.855, 0.894)]; the AUC value of the all-cause early death prediction model in the validation queue was 0.868 [95%CI (0.829, 0.907)], and the AUC value of the cancer specific early death prediction model was 0.867 [95%CI (0.827, 0.907)], indicating that the model had good predictive ability. The calibration curve showed that the predictive models had good consistency with the actual results for predicting early mortality in stage Ⅳ colorectal cancer, and the DCA curve showed that the models could provide patients with higher clinical benefits. ConclusionThe predictive models established in this study have good predictive performance for early mortality in stage Ⅳ colorectal cancer patients, which is helpful for clinical physicians to identify high-risk patients in the early stage and develop personalized treatment plans in clinical practice.
Objective To summarize primary clinical data from Xiao Tang Shan Hospital (XTSH) Information System, to provide evidence for clinical data of emerging diseases. Method The primary data were extracted from XTSH information system, which related to demographic and background information, case history, prescriptions, laboratory tests, physical examination, vital sign, surgery, diagnostics and expenditures. The software for data verification was developed by Delphi language program. The information of SARS management was developed by Oracle Developer. Results XTSH information system for SARS management collected 1.09 million pieces of information covering 680 SARS cases. The database was functionally divided into inquiry window, conditional case list window and case details spread window, which provided information of SARS management and shaped a platform for further investigation. Quality control of clinical data was done by the software of SARS Information Real Control.Conclusions XTSH information system collected complete data of SARS management, which made healthcare, research and policy-making on SARS accessible, and made it possible to share resources and train the professionals.
The hospital information structure, which is made up of various medical business systems, is suffering from the problems of the "information isolated island". Medical business systems in the hospital are mutually isomerous and difficult to become a whole. How to realize the internal barrier-free interaction of the patients effective medical information in the hospital and further to complete the area sharing of patients longitudinal diagnosis and treatment information has become a question having to be solved urgently in the process of healthcare informatization. Based on the HL7 standard, this paper refers to the IHE technical framework, expounds the overall structure of the interaction in the hospital internal and area sharing of medical information with the medical information exchange platform. The paper also gives the details of the whole process of the complete display of the discrete patient health information using Portal technology, which is saved in the business systems in different hospitals. It interacts internally through the information exchange platform and at last stores the information in the regional cinical data repository (CDR).
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer (bMBC). Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3. The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set, and the variables were screened and the prognostic prediction model was constructed. The concordance index (C-index), time-dependent clinical receiver operating characteristic curve and area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical applicability of the model in the training set and validation set, respectively. The model was used to calculate the patient risk score and classify the patients into low-, medium- and high-risk groups. Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups. Results A total of 2057 patients were enrolled with a median age of 45 years (interquartile range 47-62 years) and a median follow-up of 32 months (interquartile range 16-53 months). Totally 865 patients (42.1%) died. Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery [hazard ratio=0.51, 95% confidence interval (0.43, 0.60), P<0.001]. Chemotherapy, marital status, molecular subtype, age, pathological type and histological grade were independent prognostic factors for overall survival (P<0.05), and a prognostic prediction model was constructed based on the independent prognostic factors. The C-index was 0.702 in the training set and 0.703 in the validation set. The 1-, 3-, and 5-year AUCs of the training set and validation set were 0.734, 0.727, 0.731 and 0.755, 0.737, 0.708, respectively. The calibration curve showed that the predicted survival rates of 1, 3, and 5 years in the training set and the validation set were highly consistent with the actual survival rates. DCA showed that the prediction model had certain clinical applicability in the training set and the validation set. Patients were divided into low-, medium- and high-risk subgroups according to their risk scores. The results of log-rank test showed that local surgery improved overall survival in the low-risk group (training set: P=0.013; validation set: P=0.024), but local surgery did not improve overall survival in the medium-risk group (training set: P=0.45; validation set: P=0.77) or high-risk group (training set: P=0.56; validation set: P=0.94). Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC. The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.
Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.
ObjectiveTo investigate the impact of surgical treatment on the prognosis of patients with gastric signet-ring cell carcinoma (GSRC). MethodsThe clinicopathologic and prognosis data of patients pathologically diagnosed with GSRC from 2000 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The Cox proportional hazards regression model was used to analyze the impact of surgery on overall survival (OS) and cancer-specific survival (CSS) of patients with GSRC. ResultsA total of 3 457 patients with GSRC were included, including 2 048 cases in the operation group and 1 409 cases in the non-operation group. The propensity-score matching by a 1∶1 nearest neighbour algorithm was conducted to control for confounding baseline differences. There were 802 cases in the operation group and 802 cases in the non-operation group after matching. The OS and CSS curves drawn by Kaplan-Meier method of the operation group were better than those of the non-operation group (χ2=434.3 P<0.001; χ2=412.4, P<0.001). The multivariate Cox proportional hazards regression analysis showed that the elderly (≥ 60 years old), late AJCC tumor stage (stage Ⅰ as reference), and patients with bone metastasis of GSRC increased the risk of shortening OS and CSS (P<0.05), while patients treated with surgery and chemotherapy decreased the risk of shortening OS and CSS (P<0.05). ConclusionAccording to the analysis results of SEER database in this study, surgical treatment is beneficial to improve the prognosis for patients with GSRC.
ObjectiveTo analyze the relation between the place of residence of patients with colorectal cancer (CRC) and patient compliance or regimen decision-making or outcomes for neoadjuvant therapy (NAT) in the current version of the Database from Colorectal Cancer (DACCA). MethodsThe version of DACCA selected for this analysis was updated on June 29, 2022. The patients were enrolled according to the established screening criteria and then assigned into inside and outside of Sichuan Province groups as well as inside and outside of Chengdu City groups. The differences in the patient compliance or regimen decision-making or outcomes (changes of symptom and imaging, and cancer marker carcinoembryonic antigen) for NAT were analyzed. ResultsA total of 3 574 data that met the screened criteria were enrolled, 3 142 (87.91%) and 432 (12.09%) were inside of Sichuan Province group and outside of Sichuan Province group, respectively; 1 340 (42.65%) and 1 802 (57.35%) were inside of Chengdu City group and outside of Chengdu City group in Sichuan Province, respectively. ① The constituent ratios of the patient compliance for NAT had no statistical differences between the inside and outside of Sichuan Province groups (χ2=0.299, P=0.585) as well as between the inside and outside of Chengdu City groups (χ2=3.109, P=0.078). ② In terms of the impact of the place of residence on the decision-making of NAT: For the patients with targeted therapy or not, there was a statistical difference between the inside and outside of Sichuan Province groups (χ2=5.047, P=0.025), but which had no statistical difference between the inside and outside of Chengdu City groups (χ2=0.091, P=0.762); For the patients with radiotherapy or not, there were no statistical differences in the constituent ratios of patients between the inside and outside of Sichuan Province groups as well as between the inside and outside of Chengdu City groups (χ2=2.215, P=0.137; χ2=2.964, P=0.085); For the neoadjuvant intensity, there was a statistical difference between the inside and outside of Sichuan Province groups (χ2=12.472, P=0.002), but which had no statistical difference between the inside and outside of Chengdu City groups (χ2=2.488, P=0.288). ③ The outcomes for NAT: The changes of carcinoembryonic antigen had no statistical differences between the inside and outside of Sichuan Province groups as well as between the inside and outside of Chengdu City groups (H=1.762, P=0.184; H=3.531, P=0.060); In the symptom changes, there was a statistical difference between the inside and outside of Sichuan Province groups (χ2=3.896, P=0.048), which had no statistical difference between the inside and outside of Chengdu City groups (χ2=0.016, P=0.900); In the image changes, the difference was statistically significant between the inside and outside of Chengdu City groups (χ2=7.975, P=0.005), but which had no statistical difference between the inside and outside of Sichuan Province groups (χ2=0.063, P=0.802). ConclusionsThrough data analysis in DACCA in this study, it is found that there are no statistical differences in compliance and carcinoembryonic antigen changes. However, decision-making of NAT for patients of inside and outside of Sichuan Province has different choices on whether to assist targeted therapy and chemotherapy intensity for NAT; Symptom changes of NAT in patients of inside of Sichuan Province has a better effect than in patients of outside of Sichuan Province; Imaging change of NAT in patients of inside of Chengdu City has a better effect than in patients of outside of Chengdu City.