Objective To investigate the dynamic changes of oral saliva flow， pH value， and bacterial flora before and after radiotherapy in patients with nasopharyngeal carcinoma （NPC） treated with intensity modulated radiotherapy， and to analyze the influencing factors of severe radioactive oral mucositis. Methods One hundred NPC patients who received radiotherapy for the first time in the Head and Neck Radiotherapy Ward of Shandong Cancer Hospital and Institute from June 2， 2021 to December 30， 2022 were selected. Oral saliva flow， pH value and bacterial flora were measured at 5 time points， namely before radiotherapy， 15 times of radiotherapy， 35 times of radiotherapy， 1 month and 3 months after radiotherapy in patients with NPC， and the dynamic changes of 3 indicators were analyzed at each time. The factors of the occurrence of severe radioactive oral mucositis in patients with NPC were analyzed by univariate and multivariate logistic regression 15 times of radiotherapy. Results The saliva flow of patients with NPC before radiotherapy， 15 times of radiotherapy， 35 times of radiotherapy， 1 month after and 3 months after radiotherapy were （16.51±1.29）， （8.64±1.31）， （5.15±1.14）， （4.78±1.36） and （5.67±1.27） ml， respectively， with a statistically significant difference （F=2 171.94， P<0.001）. Oral saliva flow decreased to the lowest level 1 month after radiotherapy and then increased （all P<0.05）. The pH values of patients with NPC before radiotherapy， 15 times of radiotherapy， 35 times of radiotherapy， 1 month after and 3 months after radiotherapy were 8.28±0.67， 5.87±0.53， 5.32±0.55， 6.04±0.83， 6.74±0.63， respectively， with a statistically significant difference （F=370.43， P<0.001）. The pH value decreased successively after 15 and 35 times of radiotherapy， and gradually increased 1 month and 3 months after radiotherapy （all P<0.05）. There was a statistically significant difference （χ²=18.24， P<0.001） in the detection rate of pathogenic bacteria in patients with NPC before radiotherapy （6%， 6/100）， 15 times of radiotherapy （62%， 62/100）， 35 times of radiotherapy （60%， 60/100）， 1 month after radiotherapy （40%， 40/100） and 3 months after radiotherapy （29%， 29/100）. Compared with before radiotherapy， there were statistically significant differences in the detection rates of pathogenic bacteria between 15 times of radiotherapy and 35 times of radiotherapy （χ²=1.90， P=0.001； χ²=1.63， P=0.005）. There was no statistically significant difference in the detection rate of pathogenic bacteria between 15 times of radiotherapy and 35 times of radiotherapy （χ²=0.27， P=0.644）. Compared with before radiotherapy， there was no statistically significant difference in the detection rate of pathogenic bacteria 1 month after radiotherapy and 3 months after radiotherapy （χ²=1.30， P=0.024； χ²=0.83， P=0.149）. Of 100 cases of NPC radiotherapy， 70 patients developed severe radiation oral mucositis （≥ grade 3）. There were statistically significant differences in severe radioactive oral mucositis among patients with different smoking history （χ²=8.84， P=0.003）， alcohol drinking （χ²=23.94， P<0.001）， chemotherapy （χ²=40.41， P<0.001）， oral hygiene （χ²=8.16， P=0.004）， oral pH （χ²=16.83， P<0.001） and oral pathogens （χ²=8.80， P=0.003）. Multivariate analysis showed that， alcohol drinking （OR=2.23， 95%CI： 1.98-6.04， P=0.006）， chemotherapy （OR=3.13， 95%CI： 2.62-6.87， P<0.001） and oral pathogens （OR=3.11， 95%CI： 1.04-9.31， P=0.043） were independent influencing factors for the occurrence of severe radioactive oral mucositis in NPC patients with radiotherapy. Conclusion The oral saliva flow of patients with NPC decreases gradually from the beginning of radiotherapy to the lowest 1 month after radiotherapy and then increases. The pH value gradually decreases from the beginning of radiotherapy to 35 times of radiotherapy， and gradually increases from 1 month to 3 months after radiotherapy. The detection rate of pathogenic bacteria increases rapidly from the beginning of radiotherapy to 15 times of radiotherapy， and the growth rate is stable from 15 times of radiotherapy to 35 times of radiotherapy， and tended to be normal 1 month after radiotherapy. Drinking history， chemotherapy history and oral pathogens are independent risk factors influencing the occurrence of severe radioactive oral mucositis.

Objective To investigate the value of conventional ultrasound combined with shear wave elastography （SWE） in the differential diagnosis of non-mass ductal carcinoma in situ （DCIS） and invasive breast cancer （IBC）. Methods A total of 102 patients with non-mass breast cancer admitted to Nanjing Drum Tower Hospital， Affiliated Hospital of Nanjing University Medical School from March 2019 to April 2022 were selected as the study objects， including 32 cases of DCIS and 70 cases of IBC. Conventional ultrasound parameters echo， microcalcification， location， posterior echo， blood flow， axillary lymph node， breast imaging reporting and data system （BI-RADS） score and SWE-related parameters maximum shear wave velocity （SWV_max）， minimum shear wave velocity （SWV_min）， mean shear wave velocity （SWV_mean） and median shear wave velocity （SWV_median） were compared between patients with non-mass DCIS and IBC. Binary logistic regression was used to analyze the independent factors for the differential diagnosis of non-mass DCIS and IBC. Based on the results of multivariate analysis， a nomogram prediction model was constructed and the predictive efficacy of the prediction model was evaluated by receiver operator characteristic （ROC） curve. Calibration curve and decision curve analysis （DCA） were used to evaluate the accuracy and practicability of the model. Results There were statistically significant differences in blood flow （χ²=8.47， P=0.004）， axillary lymph nodes （χ²=9.11， P=0.003）， SWV_max （Z=-3.32， P<0.001）， SWV_mean （t=3.00， P=0.003）， SWV_median （Z=-2.69， P=0.007） between patients with non-mass DCIS and IBC. Multivariate analysis showed that， blood flow （OR=3.56， 95%CI： 1.28-9.89， P=0.015）， axillary lymph nodes （OR=3.04， 95%CI： 1.10-8.42， P=0.032） and SWV_max （OR=1.40， 95%CI： 1.13-1.73， P=0.002） were independent factors for distinguishing non-mass DCIS from IBC. A nomogram prediction model was constructed based on blood flow， axillary lymph nodes and SWV_max. ROC curve analysis showed that， the area under the curve of blood flow， axillary lymph nodes， SWV_max， and prediction model for differential diagnosis of non-mass DCIS and IBC were 0.64 （95%CI： 0.52-0.76）， 0.66 （95%CI： 0.55-0.77）， 0.71 （95%CI： 0.60-0.81）， and 0.79 （95%CI： 0.70-0.88）， respectively， and the differential diagnostic value of prediction model was higher than that of blood flow （Z=2.92， P=0.004）， axillary lymph nodes （Z=2.94， P=0.003）， and SWV_max （Z=1.88， P=0.060） alone. The C-index of the prediction model for the differential diagnosis of non-mass DCIS and IBC was 0.77， and the calibration curve showed that the prediction probability of the prediction model was close to the actual probability. DCA showed that this prediction model could provide higher clinical net benefit and had certain clinical practicability. Conclusion Blood flow and axillary lymph nodes in conventional ultrasound parameters and SWV_max of SWE-related parameters are independent factors in the differential diagnosis of non-mass DCIS and IBC. The nomogram prediction model constructed by this method has a high value in the differential diagnosis of non-mass DCIS and IBC.

Objective To explore the application value of serum melanoma-associated antigen A3 （MAGEA3） and human epididymis protein 4 （HE4） in the auxiliary diagnosis of lung cancer patients. Methods A total of 134 patients with lung cancer who underwent treatment at Qingdao Central Hospital， University of Health and Rehabilitation Sciences from January 2021 to January 2024 were selected as study subjects （study group）， and 158 healthy people who underwent physical examination during the same period were selected as control group. Enzyme linked immunosorbent assay was applied to measure the serum level of MAGEA3 and HE4. According to the median MAGEA3 and HE4 levels before treatment of the study group， lung cancer patients were divided into MAGEA3 high level group （≥58.69 pg/ml， n=66）， low level group （<58.69 pg/ml， n=68） and HE4 high level group （≥125.04 pmol/L， n=69） and low level group （<125.04 pmol/L， n=65）. Receiver operator characteristic （ROC） curves were plotted to evaluate the diagnostic performance of serum MAGEA3 and HE4 level for lung cancer before treatment. Results Serum MAGEA3 and HE4 levels before treatment in the study group were （58.69±16.14） pg/ml and （125.04±28.49） pmol/L， and those in the control group were （43.52±14.83） pg/ml and （96.85±22.33） pmol/L， respectively. The levels of MAGEA3 and HE4 in the study group were significantly higher than those in the control group， with statistically significant differences （t=8.36， P<0.001； t=9.47， P<0.001）. Serum MAGEA3 and HE4 levels after treatment in the study group were （46.73±15.42） pg/ml and （113.26±24.73） pmol/L， respectively， serum MAGEA3 and HE4 levels after treatment were significantly lower than those before treatment， with statistically significant differences （t=16.07， P<0.001； t=9.27， P<0.001）. There were significant differences in differentiation degree （χ²=6.20， P=0.013）， TNM staging （χ²=7.27， P=0.007） and lymph node metastasis （χ²=7.07， P=0.008） of lung cancer patients between the MAGEA3 high level group and the low level group before treatment. There were significant differences in differentiation degree （χ²=4.93， P=0.026）， TNM staging （χ²=7.31， P=0.007） and lymph node metastasis （χ²=9.85， P=0.002） of lung cancer patients between the HE4 high level group and the low level group before treatment. The ROC curve analysis showed that， the area under the curve （AUC） of serum MAGEA3 and HE4 alone for diagnosing lung cancer before treatment were 0.77 （95%CI： 0.72-0.83） and 0.76 （95%CI： 0.70-0.81）， and the AUC of the above two combined for diagnosing lung cancer was 0.87 （95%CI： 0.83-0.91）， and the combined diagnosis value of the two was higher than that of MAGEA3 （Z=2.92， P=0.003） and HE4 （Z=3.24， P=0.001） alone. Conclusion Compared with healthy people， serum MAGEA3 and HE4 levels before treatment of lung cancer patients are significantly increased， and serum MAGEA3 and HE4 levels are significantly lower after treatment than those before treatment. The combination of the two is more effective in diagnosis for lung cancer and can be used as an auxiliary diagnostic tool for lung cancer patients.

Objective To discusse the influencing factors of lung metastasis in elderly patients （≥60 years old） with clear cell renal cell carcinoma （ccRCC） based on Surveillance， Epidemiology， and End Results （SEER） database， and to construct and evaluate the nomogram prediction model. Methods The SEER database was used to retrieve the data of elderly ccRCC patients from 2017 to 2021. The screened 8 183 ccRCC patients were randomly assigned to the training set （n=5 728） and the validation set （n=2 455） at a ratio of 7∶3 by using the software R4.4.1. The incidence of lung metastasis in elderly patients with ccRCC was calculated， and the influencing factors of lung metastasis in elderly patients with ccRCC were analyzed by univariate and multivariate logistic regression. According to the results of multivariate analysis， the nomogram prediction model was constructed， and the prediction efficiency of the model was evaluated by using the receiver operator characteristic （ROC） curve， the clinical application value of the prediction model was evaluated by calibration curve and decision curve analysis （DCA）. Results A total of 8 183 elderly ccRCC patients were retrieved， including 620 patients with lung metastasis， and the incidence of lung metastasis was 7.58%. Univariate analysis showed that， race （white race： OR=1.58， 95%CI： 1.01-2.49， P=0.046； others： OR=1.85， 95%CI： 1.10-3.10， P=0.020）， sex （OR=1.32， 95%CI： 1.07-1.64， P=0.009）， maximum tumor diameter （55-95 mm： OR=8.22， 95%CI： 6.11-11.07， P<0.001；>95 mm： OR=28.12， 95%CI： 20.81-37.99， P<0.001）， T stage （T₂ stage： OR=15.62， 95%CI： 11.51-21.19， P<0.001； T₃ stage： OR=7.93， 95%CI： 6.06-10.36， P<0.001； T₄ stage： OR=28.65， 95%CI： 18.71-43.86， P<0.001）， N stage （OR=17.18， 95%CI： 13.36-22.10， P<0.001） and surgery situation （OR=0.12， 95%CI： 0.09-0.14， P<0.001） were all influencing factors for lung metastasis in elderly patients with ccRCC. Multivariate analysis showed that， race （white race： OR=1.82， 95%CI： 1.07-3.09， P=0.027； others： OR=2.18， 95%CI： 1.17-4.05， P=0.014）， maximum tumor diameter （55-95 mm， OR=4.63， 95%CI： 3.13-6.86， P<0.001； >95 mm， OR=8.29， 95%CI： 5.28-13.02， P<0.001）， T stage （T₂ stage： OR=2.26， 95%CI： 1.45-3.51， P<0.001； T₃ stage： OR=3.38， 95%CI： 2.28-5.01， P<0.001； T₄ stage： OR=2.45， 95%CI： 1.39-4.31， P=0.002）， N stage （OR=3.81， 95%CI： 2.81-5.17， P<0.001） and surgery situation （OR=0.10， 95%CI： 0.08-0.14， P<0.001） were independent influencing factors of lung metastasis in elderly patients with ccRCC. According to the results of multivariate analysis， a nomogram prediction model was constructed based on race， maximum tumor diameter， T stage， N stage and surgery situation. ROC curve analysis showed that the area under the curve （AUC） of the prediction model in the training set and the validation set for predicting lung metastasis in ccRCC patients was 0.91 （95%CI： 0.90-0.92） and 0.91 （95%CI： 0.89-0.93）， respectively， which indicated that the prediction model had excellent distinguishing ability. Calibration curve showed that the actual occurrence probability of the training set and the validation set was consistent with the predicted probability， which showed that the calibration degree of the prediction model was good. DCA curve showed that the predictive model had good discrimination ability in both training set and validation set， which indicated that the predictive model had potential clinical application value. Conclusion The incidence of lung metastasis in elderly patients with ccRCC is high. Race， maximum tumor diameter， T stage， N stage and surgery situation are all independent influencing factors of lung metastasis in elderly patients with ccRCC. The prediction model based on the above indexes has excellent prediction efficiency and clinical application value， and can be used to predict the risk of lung metastasis in elderly patients with ccRCC.

Objective To analyze the clinical efficacy of cetuximab combined with mFOLFOX6 chemotherapy regimen in the treatment of advanced colorectal cancer， to explore its effects on the distribution of T lymphocyte subsets， patients' physical status and tumor marker levels， and to analyze its safety. Methods A total of 90 patients with advanced colorectal cancer admitted to the Hainan Cancer Hospital from April 2020 to April 2022 were selected and randomly divided into observation group （n=45） and control group （n=45） according to random number table method. The control group was treated with the mFOLFOX6 chemotherapy regimen， while the observation group was treated with cetuximab combined with the mFOLFOX6 chemotherapy regimen. Objective response rate （ORR）， disease control rate （DCR）， T lymphocyte subsets levels （percentages of CD3⁺， CD4⁺， CD8⁺）， Karnofsky performance status （KPS） score， tumor marker vascular endothelial growth factor （VEGF）， endothelial cell-specific molecule 1 （ESM1）， carcino-embryonic antigen （CEA） levels and the incidence of adverse reactions were compared between the two groups after treatment. Results After 4 cycles of treatment， the ORR was 66.67% （30/45） in the observation group and 42.22% （19/45） in the control group， and there was a statistically significant difference （χ²=5.42， P=0.020）. The DCR of the observation group was 86.67% （39/45） and that of the control group was 66.67% （30/45）， and there was a statistically significant difference （χ²=5.03， P=0.025）. The percentages of T lymphocyte subsets CD3⁺， CD4⁺ and CD8⁺ in the observation group were （63.35±6.71） %， （35.67±3.96） % and （17.03±2.11） %， respectively， while those in the control group were （52.23±5.92） %， （30.55±3.51） % and （20.64±2.83） %， respectively. The percentages of CD3⁺ and CD4⁺ in the observation group were significantly higher than those in the control group （t=8.34， P<0.001； t=6.49， P<0.001）， while the percentage of CD8⁺ ratio was significantly lower （t=6.86， P<0.001）. The KPS score of the observation group was （95.55±9.74） points， and that of the control group was （85.03±8.92） points， and there was a statistically significant difference （t=5.34， P<0.001）. VEGF， ESM1 and CEA levels in the observation group were （303.45±33.21） ng/L， （75.66±8.36） pg/ml and （7.73±0.98） ng/ml， respectively， while those in the control group were （364.53±39.07） ng/L， （92.53±9.91） pg/ml and （9.95±1.13） ng/ml， respectively. VEGF， ESM1 and CEA levels in observation group were significantly lower compared with control group （t=7.99， P<0.001； t=8.73， P<0.001； t=9.96， P<0.001）. The total incidence of adverse reactions was 75.56% （34/45） in the observation group and 66.67% （30/45） in the control group， and there was no statistically significant difference （χ²=0.87， P=0.352）. Conclusion Cetuximab combined with mFOLFOX6 chemotherapy regimen in the treatment of advanced colorectal cancer patients has significant clinical efficacy， can improve the immune function of patients， alleviate clinical symptoms， regulate the level of serum tumor markers in patients without increasing adverse reactions， and has good safety.

Protein S-palmitoylation is a reversible post-translational modification of lipids that regulates protein localization， stability and protein-protein interactions. The thioesterification of palmitate to internal cysteine residues is catalyzed by palmitoyltransferase， while the removal of palmitate is mainly catalyzed by acyl-protein thioesterase. The S-palmitoylation of some tumor-related proteins is abnormally altered in tumor， which is closely related to the biological processes such as tumor cell proliferation， invasion， metastasis， drug resistance and immune response. Furtherly， exploring the characteristics of protein S-palmitoy-lation and its role in tumor progression could deliver new ideas in targeting protein S-palmitoylation for tumor therapy.

Circular RNA （circRNA） has shown great application potential in triple-negative breast cancer （TNBC）. The aberrantly expressed circRNAs participate in the occurrence and development of TNBC by acting as microRNA sponges， regulating gene expression， interacting with functional proteins， and encoding short peptides or proteins to regulate the biological processes of TNBC cell proliferation， apoptosis， invasion， metastasis， and drug resistance. circRNAs are emerging biomarkers for early diagnosis and prognosis of TNBC， as well as new therapeutic targets with potential clinical value. Further elucidation of the complex mechanism of circRNAs in TNBC may provide an important theoretical basis for the development of precise therapeutic strategies for TNBC.

Breast cancer， as a malignant tumor with a high incidence rate in women， seriously endangers the life health and safety of women. Its pathogenesis and treatment strategies are still the hot and difficult points in current research. More and more studies have shown that the occurrence and development of breast cancer is closely related to the microbial community in intestinal tissue and breast tissue， and the microbial community in human tissue may promote or inhibit the occurrence of breast cancer through various ways and mechanisms. Defining the relationship between microbial communities and breast cancer will provide new directions for the prevention and comprehensive treatment of breast cancer.

Immunotherapy has become the first-line standard treatment option for driver gene-negative advanced non-small cell lung cancer （NSCLC）. But not all patients can benefit from immunotherapy， and can even have serious adverse reactions. It is crucial to identify the predictors of clinical response to immunotherapy. Several studies have shown that elevated baseline C-reactive protein （CRP） or persistent elevation of CRP during the treatment process may indicate a poorer prognosis for patients，and high CRP may be correlated with adverse reactions. Attention to the dynamic changes of CRP during immunotherapy for NSCLC may become an important predictor of prognosis.

Bone metastasis of lung cancer is relatively common in patients with advanced lung cancer， the treatment research of which has been made remarkable progress. In terms of systemic therapy， chemotherapy is the traditional treatment plan； targeted therapy shows strong efficacy in treating lung cancer patients with specific gene mutations； immunotherapy can bring new treatment options for lung cancer patients with bone metastasis by activating the body's immune system， which can be used alone or in combination with other therapies. Anti bone metastasis drugs such as bisphosphonates and denosumab play an important role in reducing bone destruction and bone-related adverse events. In terms of local therapy， radiotherapy can accurately irradiate tumor lesions and relieve pain symptoms； surgical treatment is suitable for specific bone metastasis to restore bone stability and function. In addition， analgesic treatment and psychological intervention can alleviate patients' pain and anxiety. These therapeutic advances bring new hope to patients with lung cancer bone metastasis.

Bone is the main metastatic site of prostate cancer， and bone metastasis of prostate cancer seriously affects the prognosis and quality of life of patients. Exosomes are lipid bilayer vesicles secreted by cells， which play a significant role in intercellular communication. Exosomes can regulate the specific colonization of prostate cancer cells in bone tissue， reshape the pre-metastatic niche conducive to the survival of tumor cells， and modulate the immune microenvironment of bone. At the same time， exosomes can also regulate the differentiation and maturation of osteoblasts and osteoclasts， resulting in bone remodeling. Further understanding of the role of exosomes in bone metastasis of prostate cancer will help to develop new targets for delaying the progression of bone metastasis.