Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures.By interacting with the microenvironment,tumor cells undergo dynamic changes in gene expression and metabolism,resulting in spatiotemporal variations in their capacity for proliferation and metastasis.In recent years,the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis.By preserving location information while obtaining a large number of gene and molecular data,spatially resolved metabolomics(SRM)and spatially resolved transcriptomics(SRT)approaches can offer new ideas and reliable tools for the in-depth study of tumors.This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques,as well as a review of their applications in cancer-related fields.

Objective:To explore the application value of histogram and texture feature analysis based on CT images in distinguishing long bone osteosarcoma (OS) from Ewing sarcoma (ES).Methods:A retrospective collection of 25 patients with long bone osteosarcoma and 25 patients with Ewing sarcoma confirmed by surgery and pathology in National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Qilu Hospital of Shandong University and Nanjing Drum Tower Hospital, Nanjing University Medical School, from March 2018 to May 2023 was conducted. All patients were randomly divided into a training set (21 cases of OS and 19 cases of ES) and a validation set (4 cases of OS and 6 cases of ES) in an 8∶2 ratio. The region of interest (ROI) on CT images to extract texture feature parameters was manually sketched. Random forest and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature screening. Logistic regression (LR), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were used to establish models respectively. Receiver operating characteristic (ROC)curve was drawn and area under the curve (AUC) was calculated to evaluate the diagnostic efficiency of the four models.Results:A total of 100 texture parameters were extracted from CT images, and 8 feature parameters (maximum 3D diameter, 10th percentile, kurtosis, maximum pixel intensity value, inverse normalization, grayscale level variance, long range high grayscale emphasis, and low grayscale area emphasis) were obtained through screening. Four classifiers were used to establish models, and the AUC values of the four models (LR, RF, SVM, KNN) in the validation group were 0.92, 0.79, 0.83, and 0.73, respectively. LR and SVM classifier algorithm trains models had high diagnostic efficiency, with an accuracy of 90%, sensitivity of 83%, specificity of 100%, and AUC of 92% for the LR classifier validation set; the accuracy of SVM classifier validation set was 80%, sensitivity was 67%, specificity was 100%, and AUC was 83%.Conclusions:LR and SVM models have high value in distinguishing OS and ES.

Objective:To explore the diagnostic value of magnetic resonance imaging (MRI) Q-Dixon fat quantification technique in differentiating vertebral metastases from hemangiomas in cancer patients.Methods:A retrospective analysis was conducted on 20 patients with vertebral metastases and 8 with vertebral hemangiomas who underwent vertebral MRI scans at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College from December 2021 to December 2023. Two radiologists independently measured the fat fractions (FF) in three areas (the lesion area, the normal area of the same vertebra, and the normal area of an adjacent vertebra) and evaluated the consistency of measurements. Group differences were tested using independent sample t-tests or Mann-Whitney U tests, and diagnostic performance was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Results:There was very high inter-observer consistency in the FF measurements across the three regions. The FF in the lesion areas of vertebral metastases group was significantly lower than that in the vertebral hemangioma group (13.8 ± 11.5 vs. 56.5 ± 22.1), there was statistical difference ( P<0.01). There were no significant differences in the FF of normal vertebral areas between the two groups ( P>0.05). ROC curve analysis showed that FF could differentiate vertebral metastases from hemangiomas with an AUC of 0.931, a specificity of 90%, and a sensitivity of 87.5%. Conclusions:The FF measured by the Q-Dixon quantitative fat technique can accurately differentiate between vertebral hemangiomas and vertebral metastases, providing more precise guidance for the diagnosis of vertebral lesions.

Pulmonary fibrosis(PF)is a chronic progressive end-stage lung disease.However,the mechanisms un-derlying the progression of this disease remain elusive.Presently,clinically employed drugs are scarce for the treatment of PF.Hence,there is an urgent need for developing novel drugs to address such diseases.Our study found for the first time that a natural source of Prismatomeris connata Y.Z.Ruan(Huang Gen,HG)ethyl acetate extract(HG-2)had a significant anti-PF effect by inhibiting the expression of the transforming growth factor beta 1/suppressor of mothers against decapentaplegic(TGF-β1/Smad)pathway.Network pharmacological analysis suggested that HG-2 had effects on tyrosine kinase phosphorylation,cellular response to reactive oxygen species,and extracellular matrix(ECM)disassembly.Moreover,mass spec-trometry imaging(MSI)was used to visualize the heterogeneous distribution of endogenous metabolites in lung tissue and reveal the anti-PF metabolic mechanism of HG-2,which was related to arginine biosyn-thesis and alanine,asparate and glutamate metabolism,the downregulation of arachidonic acid meta-bolism,and the upregulation of glycerophospholipid metabolism.In conclusion,we elaborated on the relationship between metabolite distribution and the progression of PF,constructed the regulatory metabolic network of HG-2,and discovered the multi-target therapeutic effect of HG-2,which might be conducive to the development of new drugs for PF.