Objective To investigate the expression of microRNA-4262 （miR-4262） and neuregulin 1 （NRG1） in non-small cell lung cancer （NSCLC） tissues and the relationship with prognosis. Methods A total of 102 NSCLC patients who underwent surgical resection from January 2017 to February 2021 in Tongren People's Hospital of Guizhou Province were selected. The expression levels of miR-4262 and NRG1 were detected using real-time fluorescence quantitative PCR. The expression levels of miR-4262 and NRG1 in NSCLC cancer tissues and adjacent tissues， as well as NSCLC cancer tissues with different clinicopathological characteristics were analyzed. TargetScan database was used to predict the binding sites of miR-4262 and NRG1， and Pearson correlation coefficient was used to analyze the correlation between miR-4262 and NRG1 expression in NSCLC cancer tissues. Based on the mean expression levels of miR-4262 and NRG1 in NSCLC cancer tissues， the patients were divided into high miR-4262 expression group （miR-4262≥1.52， n=54） and low miR-4262 expression group （miR-4262<1.52， n=48）， high NRG1 expression group （NRG1≥0.79， n=54） and low NRG1 expression group （NRG1<0.79， n=48）. Kaplan-Meier survival curves were plotted to compare the 3-year overall survival （OS） rates between groups. Cox proportional risk regression model was used to analyze the influencing factors for the prognosis of NSCLC patients. Results The expression level of miR-4262 was significantly higher in NSCLC tumor tissues compared to adjacent tissues （1.52±0.21 vs. 1.11±0.20）， while NRG1 expression level was lower （0.79±0.11 vs. 1.06±0.11）， there were statistically significant differences （t=14.22， P<0.001； t=-15.13， P<0.001）. The expression of miR-4262 was negatively correlated with NRG1 in cancer tissues of NSCLC patients （r=-0.74， P<0.001）. There were statistically significant differences in the expression levels of miR-4262 and NRG1 of NSCLC patients in tumor differentiation （t=2.80， P=0.006； t=-2.80， P=0.006）， TNM stage （F=24.36， P<0.001； F=17.66， P<0.001）， and lymph node metastasis （t=4.02， P<0.001； t=-3.98， P<0.001）. At the end of the follow-up period， 57 patients survived， and 45 died， with a 3-year OS rate of 55.88%. Patients with high miR-4262 expression had a significantly lower 3-year OS rate compared to those with low miR-4262 expression （35.19% vs. 79.17%）， patients with high NRG1 expression had a significantly higher 3-year OS rate than those with low NRG1 expression （77.78% vs. 31.25%）， there were statistically significant differences （χ²=22.58， P<0.001； χ²=27.26， P<0.001）. Univariate analysis showed that， age （HR=2.47， 95%CI：1.05-5.80， P=0.038）， maximum tumor diameter （HR=3.75， 95%CI：1.61-8.74， P=0.002）， differentiation degree （HR=3.03， 95%CI：1.32-6.96， P=0.009）， TNM stage （stage Ⅱ， HR=3.45， 95%CI：1.10-10.83， P=0.034； stage Ⅲ， HR=6.72， 95%CI：2.03-22.26， P=0.002）， lymph node metastasis （HR=3.00， 95%CI：1.29-6.96， P=0.010）， miR-4262 expression （HR=3.72， 95%CI：1.48-9.35， P=0.005）， and NRG1 expression （HR=0.30， 95%CI：0.13-0.73， P=0.008） were all influencing factors for OS in NSCLC patients. Multivariate analysis showed that， differentiation degree （HR=5.47， 95%CI：1.63-18.34， P=0.006）， TNM stage （stage Ⅲ， HR=5.56， 95%CI：1.23-25.14， P=0.026）， lymph node metastasis （HR=3.72， 95%CI：1.19-11.60， P=0.024）， miR-4262 expression （HR=8.56， 95%CI：2.26-32.41， P=0.002）， and NRG1 expression （HR=0.26， 95%CI：0.09-0.76， P=0.014） were all independent influencing factors for OS in NSCLC patients. Conclusions The expression of miR-4262 is high and the expression of NRG1 is low in cancer tissues of NSCLC patients. The 3-year OS rate of patients with high miR-4262 expression is lower than that of patients with low miR-4262 expression， and the 3-year OS rate of patients with high NRG1 expression is higher than that of patients with low NRG1 expression. Differentiation degree， TNM stage， lymph node metastasis， miR-4262 and NRG1 are all independent influencing factors for the prognosis of NSCLC patients.

Objective To explore the CT features and predictive value of radiomics nomogram of visceral pleural invasion （VPI） in stage Ⅰ lung adenocarcinoma with peripheral solid nodules. Methods One hundred and fifty patients with stage Ⅰ lung adenocarcinoma with peripheral solid nodules treated at the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine from August 2022 to November 2023 were selected as the study objects. Patients from August 2022 to March 2023 were defined as the training set （n=112）， and patients from April 2023 to November 2023 were defined as the validation set （n=38）. The training set was used to build the model， and the training set and validation set were used to evaluate the model performance respectively. In the training set， patients were divided into VPI positive group （n=35） and VPI negative group （n=77） based on the occurrence of VPI. Least absolute shrinkage and selection operator （LASSO） regression analysis was used to reduce the dimensionality of features. Multivariate logistic regression analysis was used to predict the influencing factors of VPI， and a radiomics nomogram prediction model was constructed based on the results of the multivariate analysis. Receiver operator characteristic （ROC） curves and calibration curves were used to evaluate the predictive efficacy of the prediction model. Results There were statistically significant differences in pathological types （χ²=11.49， P=0.003）， focal maximum diameter （t=5.83， P<0.001）， lobulation sign （χ²=9.29， P=0.002）， density （χ²=8.32， P=0.004）， intratumoral necrosis （χ²=5.86， P=0.015）， pleural traction （χ²=12.88， P<0.001）， pleural contact （χ²=4.82， P=0.028）， and adjacent pleural thickening （χ²=4.87， P=0.027） between the VPI positive group and negative group in the training set. LASSO regression analysis showed that 8 features were ultimately selected， and radiomics scores were constructed based on the corresponding coefficients of the features. Univariate analysis showed that， focal maximum diameter （OR=1.48， 95%CI：1.09-2.01， P=0.010）， lobulation sign （OR=5.09， 95%CI：2.31-6.00， P=0.001）， density （OR=4.25， 95%CI：1.47-7.18， P=0.004）， intratumoral necrosis （OR=2.27， 95%CI：1.01-5.17， P=0.049）， pleural traction （OR=6.75， 95%CI：1.92-13.68， P<0.001）， pleural contact （OR=3.58， 95%CI：1.18-5.65， P=0.018）， adjacent pleural thickening （OR=3.60， 95%CI：1.18-5.72， P=0.018）， and radiomics score （OR=19 418.06， 95%CI：394.18-957 161.04， P<0.001） were all influencing factors in the prediction of VPI in peripheral solid nodule stage Ⅰ lung adenocarcinoma patients. Multivariate analysis showed that， lobulation sign （OR=6.42， 95%CI：1.42-18.58， P=0.018）， intratumoral necrosis （OR=3.63， 95%CI：1.01-10.01， P=0.046）， pleural traction （OR=4.19， 95%CI：1.17-10.92， P=0.028）， and radiomics score （OR=179 711.20， 95%CI：525.13-61 552 573.59， P<0.001） were independent influencing factors in the prediction of VPI in patients with stage Ⅰ peripheral solid nodules of lung adenocarcinoma. A radiomics nomogram prediction model was established for indicators with statistical significance in multivariate analysis. ROC curve analysis showed that in the training set and validation set， the area under the curve （AUC） of the radiomics nomogram model predicting VPI of patients with peripheral solid nodules in stage Ⅰ lung adenocarcinoma was 0.88 （95%CI：0.82-0.94） and 0.87 （95%CI：0.78-0.97）， respectively， and the sensitivity was 93% and 82%， respectively. The specificity was 72% and 80%， respectively. C-indices of the training and validation set were 0.89 （95%CI：0.84-0.96） and 0.88 （95%CI：0.78-0.99）， respectively， and the calibration curves of both sets fitted well with the ideal curve. Conclusions The CT features of VPI in stage Ⅰ lung adenocarcinoma with peripheral solid nodules are lobular sign， intratumoral necrosis， and pleural traction. The radiomics nomogram model based on CT features of lobular sign， intratumoral necrosis， pleural traction， and radiomics score can predict VPI in patients with peripheral solid nodules in stage Ⅰ lung adenocarcinoma has high predictive efficacy.

Objective To evaluate the value of a combined model based on primary lesion ¹⁸F-fluorodeoxyglucose（¹⁸F-FDG） PET/CT radiomics for predicting lymph node metastasis in non-small cell lung cancer（NSCLC）. Methods A retrospective analysis was conducted on the clinical data of 203 NSCLC patients who underwent pre-treatment PET/CT imaging at Nanjing Drum Tower Hospital from June 2013 to July 2023. Patients were randomly assigned to the training set（n=142） and the validation set（n=61） at a ratio of 7∶3. A predictive model was developed in the training set， and its predictive performance and clinical application value were assessed in both the training and validation sets. Traditional PET/CT parameters and PET/CT radiomics features of the primary lesion were obtained by 3D-slicer software. Least absolute shrinkage and selection operator（LASSO）， random forest， and extreme gradient boosting were performed to extract features. Support vector machine was used to construct a radiomics score（Radscore）. Univariate and multivariate logistic regression analysis was used to predict the influencing factors of lymph node metastasis in NSCLC patients and to establish models. Predictive performance of the models was evaluated by receiver operator characteristic（ROC） curves and clinical application value was assessed by calibration curves and decision curve analysis（DCA）. Results Among 203 NSCLC patients， 116 had lymph node metastasis， with 64 cases in the training set and 52 cases in the validation set. Three complementary classical machine learning methods were used for feature screening， and finally 10 radiomics features were obtained. The optimal threshold for Radscore-PET was 0.43 and the optimal threshold for Radscore-CT was 0.39. Univariate analysis showed that， sex（OR=0.48， 95%CI：0.24-0.95， P=0.036）， tumor marker levels（OR=3.81， 95%CI：1.84-7.91， P<0.001）， long diameter of tumor（OR=2.56， 95%CI：1.27-5.16， P=0.009）， short diameter of tumor（OR=3.73， 95%CI：1.75-7.92， P=0.001）， vacuolar sign（OR=0.32， 95%CI：0.12-0.86， P=0.024）， ring-like metabolism（OR=3.67， 95%CI：1.33-10.13， P=0.012）， maximum standardized uptake value（SUV_max）（OR=6.57， 95%CI：3.03-14.25， P<0.001）， metabolic tumor volume（MTV）（OR=2.91， 95%CI：1.43-5.92， P=0.003）， total lesion glycolysis（TLG）（OR=4.23， 95%CI：2.08-8.59， P<0.001）， Radscore-PET（OR=21.93， 95%CI：9.04-53.20， P<0.001） and Radscore-CT（OR=13.72， 95%CI：6.12-30.76， P<0.001） were all influencing factors for predicting lymph node metastasis in NSCLC patients. Multivariate analysis showed that， tumor marker levels（OR=2.55， 95%CI：1.11-5.90， P=0.028）， vacuolar sign（OR=0.26， 95%CI：0.08-0.83， P=0.023）， SUV_max（OR=5.94， 95%CI：1.99-17.75， P=0.001）， Radscore-PET（OR=25.51， 95%CI：5.92-110.22， P<0.001）， and Radscore-CT（OR=8.68， 95%CI：2.73-27.61， P<0.001） were independent influencing factors for predicting lymph node metastasis in patients with NSCLC. Based on the above independent influencing factors， models were constructed：the traditional model（tumor marker levels， vacuolar sign， SUV_max）， the PET model（SUV_max， Radscore-PET）， the CT model（vacuolar sign， Radscore-CT）， and the combined model（tumor marker levels， vacuolar sign， SUV_max， Radscore-PET， Radscore-CT）. ROC curve analysis showed that， the area under curve（AUC） of the traditional， PET， CT， and combined models in the training set were 0.75（95%CI：0.67-0.82）， 0.90（95%CI：0.84-0.95）， 0.85（95%CI：0.78-0.90）， and 0.94（95%CI：0.88-0.97）， respectively. The predictive value of the combined model was higher than that of the traditional model（Z=5.01， P<0.001）， the PET model（Z=1.99， P=0.047）， and the CT model（Z=3.25， P=0.001）. In the validation set， the AUCs for the traditional model， PET model， CT model， and combined model were 0.65（95%CI：0.52-0.77）， 0.86（95%CI：0.74-0.93）， 0.85（95%CI：0.73-0.93）， and 0.90（95%CI：0.80-0.96）， respectively. The predictive value of the combined model was superior to that of the traditional model（Z=3.23， P=0.001）. The sensitivity and specificity of the combined model in the training set were 84.37% and 91.03%， while in the validation set， the sensitivity and specificity were 82.61% and 94.74%， respectively. Calibration curves showed a good agreement between the predicted and actual probabilities in both the training and validation sets. DCA showed that the combined models had good discriminative ability in both the training and validation sets. Conclusions Tumor marker levels， vacuolar sign， SUV_max， Radscore-PET， and Radscore-CT are all independent influencing factors for predicting lymph node metastasis in patients with NSCLC. The combined model based on these factors demonstrates excellent predictive performance and clinical application value for predicting lymph node metastasis in NSCLC.

Objective To explore the relationship between the expression levels of succinate receptor 1 （SUCNR1） and Y-box binding protein 1 （YBX1） in colorectal cancer tissues of patients with colorectal cancer liver metastases （CRLM） and their clinicopathological characteristics and prognosis. Methods One hundred and five CRLM patients who underwent surgical treatment in Suzhou Kowloon Hospital of Shanghai Jiao Tong University School of Medicine from January 2016 to May 2020 were taken as the study subjects. The high expression rates of SUCNR1 and YBX1 in cancer tissues and adjacent tissues were compared. Clinicopathological characteristics and prognosis of patients with high and low SUCNR1 and YBX1 expression were compared. Univariate and multivariate Cox proportional risk regression models were applied to analyze prognostic influencing factors. Results SUCNR1 staining was mainly located on the cell membrane in colorectal cancer tissues， and positive staining showed yellow or brownish yellow； YBX1 was mainly located in the cytoplasm of colorectal cancer tissues， and positive staining showed yellow or brownish yellow. The high expression rate of SUCNR1 in cancer tissues （74.29%， 78/105） of CRLM patients was obviously higher than that in adjacent tissues （27.62%， 29/105）， and the high expression rate of YBX1 in cancer tissues （84.76%， 89/105） was obviously higher than that in adjacent tissues （32.38%， 34/105）， with statistically significant differences （χ²=45.75， P<0.001； χ²=59.36， P<0.001）. There were statistically significant differences in histological grade （χ²=7.43， P=0.006） and the time from colorectal cancer diagnosis to liver metastasis （χ²=9.19， P=0.002） between patients with high and low expression of SUCNR1； there was a statistically significant difference in time from colorectal cancer diagnosis to liver metastasis （χ²=13.08， P<0.001） between patients with high and low expression of YBX1. The 3-year overall survival （OS） rates of patients with high and low expression of SUCNR1 were 52.56% and 77.78%， respectively， with a statistically significant difference （χ²=6.10， P=0.014）； the 3-year OS rates of patients with high and low expression of YBX1 were 53.93% and 87.50%， respectively， with a statistically significant difference （χ²=6.02， P=0.014）. Univariate analysis showed that， histological grade （HR=4.69， 95%CI：1.14-19.36， P=0.033）， time from colorectal cancer diagnosis to liver metastasis （HR=4.05， 95%CI：1.02-16.62， P=0.048）， cancer tissues SUCNR1 （HR=5.12， 95%CI：1.17-22.34， P=0.030）， and YBX1 expression （HR=6.29， 95%CI：1.55-25.47， P=0.010） were all influencing factors for OS in CRLM patients. Multivariate analysis showed that， histological grade （HR=4.16， 95%CI：1.12-15.54， P=0.034）， time from colorectal cancer diagnosis to liver metastasis （HR=5.59， 95%CI：1.25-24.99， P=0.024）， expression of SUCNR1 in cancer tissues （HR=3.68， 95%CI：1.28-10.54， P=0.015）， and expression of YBX1 in cancer tissues （HR=3.42， 95%CI：1.56-7.52， P=0.002） were all independent influencing factors for OS in CRLM patients. Conclusions The high expression rates of SUCNR1 and YBX1 in cancer tissues of CRLM patients are higher than those in adjacent tissues. Patients with high and low SUCNR1 expression have differences in tumor histological grade， time from colorectal cancer diagnosis to liver metastasis， patients with high and low YBX1 expression has a difference in time from colorectal cancer diagnosis to liver metastasis. The 3-year OS rates of patients with low expression of SUCNR1 and YBX1 are higher than those of patients with high expression. The histological grade， the time from colorectal cancer diagnosis to liver metastasis， and the expression of SUCNR1 and YBX1 in cancer tissues are all independent influencing factors for OS in CRLM patients.

Objective To investigate the differences in the levels of inactive rhomboid protein （iRhom） 1， iRhom2， and tumor necrosis factor-α （TNF-α） in cervical cancer patients with different prognoses， and to analyze the predictive value of each index for patient's prognosis. Methods A total of 90 cervical cancer patients who underwent extensive hysterectomy at the People's Hospital of Xinjiang Uygur Autonomous Region from June 2021 to June 2023 were selected as the study objects. Using a propensity score matching method with a caliper value of 0.02， 30 cases with normal cervical biopsy results and matched general clinical data were selected as the controls. Cervical cancer patients were divided into the good prognosis group （n=69） and the poor prognosis group （n=21） according to the prognosis. Western blotting was used to detect the protein levels of iRhom1， iRhom2， and TNF-α in tissue samples. The differences in iRhom1， iRhom2， and TNF-α protein levels between cervical cancer tissues， adjacent tissues， normal cervical tissues， and between the good and poor prognosis groups were compared. Multivariate logistic regression was used to analyze the influencing factors of prognosis in cervical cancer patients， and the receiver operator characteristic （ROC） curve was used to evaluate the predictive efficacy of each indicator for prognosis in cervical cancer patients. Results The levels of iRhom1 protein in cervical cancer tissues， adjacent tissues， and normal cervical tissues were 0.80±0.11， 0.41±0.10， 0.40±0.07， respectively； those of iRhom2 were 0.81±0.12， 0.47±0.10， 0.46±0.05， respectively； and those of TNF-α were 1.15±0.12， 0.58±0.10， 0.56±0.07， respectively. There were statistically significant differences in the levels of iRhom1， iRhom2， and TNF-α protein among the three groups （F=64.93， P<0.001； F=56.14， P<0.001； F=191.61， P<0.001）. There were statistically significant differences in the levels of iRhom1， iRhom2 and TNF-α between cervical cancer tissues and adjacent tissues and normal cervical tissues （all P<0.05）. In the poor prognosis group and the good prognosis group， the levels of iRhom1 protein in cervical cancer tissues were 0.90±0.12 and 0.77±0.10， respectively； those of iRhom2 were 0.90±0.10 and 0.79±0.09， respectively； and those of TNF-α were 1.29±0.13 and 1.06±0.10， respectively. There were statistically significant differences in iRhom1， iRhom2， and TNF-α protein levels between the two groups （t=7.31， P<0.001； t=5.35， P<0.001； t=10.30， P<0.001）. Multivariate logistic regression analysis showed that， iRhom1 （OR=2.29， 95%CI：1.77-3.71， P<0.001）， iRhom2 （OR=1.51， 95%CI：1.10-2.71， P<0.001）， and TNF-α （OR=2.10， 95%CI：1.90-4.44， P<0.001） were all independent influencing factors of the prognosis of cervical cancer patients. The ROC curve indicated that iRhom1 （AUC=0.88， 95%CI：0.80-0.97）， iRhom2 （AUC=0.83， 95%CI：0.73-0.94）， and TNF-α （AUC=0.80， 95%CI：0.65-0.94） alone and in combination （AUC=0.97， 95%CI：0.93-1.00） could predict prognosis of cervical cancer patients. Conclusions The levels of iRhom1， iRhom2 and TNF-α proteins in cervical cancer tissues are higher than those in adjacent tissues and normal cervical tissues， and the levels of these three indexes in cervical cancer tissues with poor prognosis are significantly higher than those in cervical cancer tissues with good prognosis. The levels of iRhom1， iRhom2 and TNF-α protein are all independent factors influencing the prognosis of cervical cancer patients， and the three indicators alone or in combination can predict the prognosis of cervical cancer patients.

Copper death is a newly discovered copper-dependent cell death mode that causes protein-toxic stress and triggers cell death by affecting mitochondrial tricarboxylic acid cycling and iron-sulfur tuftin loss. In recent years，with the in-depth study of the mechanism of copper death，it has been found that the genes related to copper death may be related to the clinical characteristics and prognosis of tumors，which can be used as potential biological targets for the diagnosis or treatment of tumors. At the same time，drugs targeting copper ions such as copper ionophores，copper chelators and copper containing complexes are widely studied. Further study on the mechanism of copper death in the development of tumor can provide new ideas for tumor diagnosis and treatment.

In recent years，intratumoral immune injection，as an emerging drug delivery modality in the treatment of advanced malignant tumors，has not only improved drug bioavailability，but also reduced systemic toxicity by injecting bacteria and toxins，oncolytic viruses，cytokines，monoclonal antibodies，immune cells，pattern recognition receptor agonists，chemotherapeutic agents，mRNA，and antibody-drug conjugates into solid tumors. In addition，the development of drug delivery carriers such as iodized oil，hydrogel，nanoparticles and drug-carrying microspheres has solved the problem that drugs injected intratumorally are prone to diffuse through the vascular system and are difficult to remain locally for a long period of time. An in-depth exploration of the research progress of intratumoral immune injection of drugs and drug delivery carriers can provide a reference for further research on intratumoral immune injection，and improve the clinical benefits for patients with solid tumors.

Drug delivery system （DDS） holds promise in reducing off-target effects of antitumor drugs. The carriers of antitumor drug DDS mainly include nanoparticles，small molecules，nucleic acids，peptides，antibodies and cells，which carry antitumor drugs through linker to deliver the drugs to specific tumor tissues and cells. DDS will propel progress in the field of cancer precision medicine.

Immunotherapy has achieved relatively satisfactory results in the treatment of non-small cell lung cancer （NSCLC），but not all patients can benefit from immunotherapy. Peripheral blood-based markers are easily accessible and can be monitored dynamically. Peripheral blood tumor cell-related markers （circulating tumor DNA，circulating tumor cells，peripheral blood tumor mutation load，exosomes，etc.），as well as immune and inflammatory markers （T-lymphocyte subpopulations，hematology-associated ratios，C-reactive protein，etc.） have demonstrated great potential in immunotherapy efficacy prediction，prognosis evaluation，and dynamic monitoring.

Osteosarcoma is a kind of mesenchymal tissue tumor，which is highly invasive and metastatic. Pro-angiogenic factors and vascular endothelial cells play key roles in the angiogenesis and metastasis of osteosarcoma，and the interaction between vascular endothelial cells and osteosarcoma cells can affect the growth and metastasis of osteosarcoma. Anti-vascular therapeutics agents for osteosarcoma，including monoclonal antibodies，tyrosine kinase inhibitors and traditional Chinese medicines，can inhibit the progression of osteosarcoma. An in-depth study of the correlation between osteosarcoma and angiogenesis can provide new ideas for the study of vascularization of osteosarcoma and anti-vascular treatment of osteosarcoma.