Distal metastasis is the main cause of clinical treatment failure in patients with colon cancer. It is now known that the invasion and metastasis of cancer cells is precisely regulated by chemical and physical factors in vivo. However, the role of extracellular matrix (ECM) stiffness in colon cancer cell (CCCs) invasion and metastasis remains unclear. Here, bioinformatical analysis suggested that a high expression level of yes associated protein 1 (YAP1) was significantly associated with metastasis and poor prognosis in colon cancer patients. We further investigated the effects of polyacrylamide hydrogels with different stiffnesses (3, 20, and 38 ​kPa), which were simulated as ECM, on the mechanophenotype (F-actin cytoskeleton organization, electrophoretic rate, membrane fluidity, and Young's modulus) of CCCs. The results showed that a stiffer ECM could induce the maturation of focal adhesions and formation of stress fibers in CCCs, regulate their mechanophenotypes, and promote cell motility. We also demonstrated that the expression levels of YAP1 and paxillin were positively correlated in patients with colon cancer. YAP1 knockdown reduces paxillin clustering and cell motility and alters the cellular mechanophenotypes of CCCs. This is of great significance for an in-depth understanding of the invasion and metastatic mechanisms of colon cancer and for the optimization of clinical therapy from the perspective of mechanobiology.

Objective: Successful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC.Methods: Data on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME).Results: A total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients.Conclusions: After validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.

Identification of effective biomarkers is crucial to improve the efficacy of immunotherapy in patients with osteosarcoma. Tumor-associated M2 macrophages, an important immune cell type in the tumor immune microenvironment, are closely related to the formation and progression of tumors. However, the relationships of M2 macrophages and prognosis and the immunotherapy response to osteosarcoma remain unclear. In this study, we obtained single-cell RNA sequencing (scRNA-seq) data of osteosarcoma from the gene expression omnibus (GEO) database and performed trajectory analysis and cell communication analysis. We then identified M2 macrophage marker genes based on scRNA-seq data of osteosarcoma, and constructed a risk-score model using these genes. Next, we compared the survival status and immune features of patients with high and low risk scores. Based on scRNA-seq data, we found that macrophages were the major immune cell type in the osteosarcoma microenvironment, and the high proportion of M2 macrophages might result from the transition of macrophages M1 to M2. M2 macrophages communicated with osteoblastic cells via the APP, MIF, and SPP1 signaling pathways, facilitating osteosarcoma development. Moreover, we identified 189 osteosarcoma-related M2 macrophage marker genes and screened out 10 key genes used for model constrcution. These 10 genes consisted of two known M2 macrophage markers and eight novel M2 macrophage marker genes. Low-risk patients have a statistically significant survival advantage, which was verified in the four GEO datasets. Low-risk patients also displayed a high abundance of tumor-infiltrating immune cells, indicative of an “hot” immune phenotype, while high-risk patients displayed an opposite immunologic feature. Notably, our analysis of two independent immunotherapy cohorts revealed that low-risk patients had good immunotherapy responses and outcomes. Additionally, we determined 32 evidently correlated pairs between risk score and drug sensitivity. This study reveals a new prognostic signature based on M2 macrophage marker genes that can help optimize personalized prognosis and improve immunotherapy outcomes in patients with osteosarcoma and also provides a method for identifying effective biomarkers based on integrated analysis of single-cell and bulk RNA sequencing.

Background: Complete resection of the tumor and the ipsilateral thyroid lobe at the primary surgery is the “gold standard” for the treatment of parathyroid carcinoma (PC). However, differences in the overall survival (OS) of patients with PC who underwent partial and total surgical resection remain to be determined.Methods: Data on patients with PC who underwent partial and total surgical resection were extracted from the Surveillance, Epidemiology and End Results (SEER) database (2000–2018). The X-tile software (https://medicine.yale.edu/lab/rimm/research/software/) was used to define the optimal cut-off values for continuous variables. The inverse probability of treatment weighting (IPTW) method was used to reduce the selection bias. IPTW-adjusted Kaplan–Meier curves and Cox proportional hazards models were used to compare the OS of patients with PC in the partial and total surgical resection groups.Results: A total of 334 patients with PC were included in this study (183 and 151 in the partial and total surgical resection groups, respectively). The optimal cut-off values for age at diagnosis were 53 and 73 years, respectively, while that for tumor size was 34 mm. In both the Kaplan–Meier analysis and univariable Cox proportional hazards regression analysis before IPTW, the difference in OS between the partial and total surgical resection groups was not statistically significant (p>0.05). These findings were confirmed in the IPTW-adjusted Kaplan–Meier analysis and multivariate Cox proportional hazards regression analysis (p>0.05). Subgroup analysis revealed that total surgical resection was beneficial for OS only in the subgroup with unknown tumor size.Conclusion: There was no significant difference in the prognosis of patients who underwent partial and total surgical resection. This finding may provide a useful reference for the treatment of PC.

Background: Cardiovascular–kidney–metabolic syndrome (CKM) contributes substantially to death, but the extent of malnutrition documentation among CKM–attributed deaths is unclear. We quantified temporal trends and disparities in US CKM‐related deaths involving malnutrition from 1999 to 2023. Methods: Using Centers for Disease Control and Prevention Wide‐Ranging Online Data for Epidemiologic Research Multiple Cause of Death data, we identified deaths with CKM as the underlying cause and malnutrition as a contributing cause. We calculated age‐adjusted mortality rates (AAMRs) per 100 000 using the 2000 US standard population, assessed trends with joinpoint regression, quantified malnutrition involvement using an AAMR‐based share, and modeled the death‐count–based share with weighted binomial regression. Results: National AAMRs followed a U‐shaped pattern, declining from 2.31 in 1999 to about 0.9 in the early 2010s, then increasing to 3.78 (95% CI, 3.71–3.85) in 2023 (average annual percentage change, 2.1%). Despite declining overall CKM death, the AAMR‐based share increased from 0.67% to 1.40%. Adults aged ≥65 years accounted for most deaths and had higher AAMRs than younger adults, although younger decedents showed faster adjusted annual increases in malnutrition documentation. Black adults had the highest AAMRs throughout, whereas relative increases in malnutrition involvement were steeper among White adults. The West had higher baseline odds and the most rapid acceleration. Conclusions: Malnutrition involvement in CKM‐attributed deaths increased over time despite declining overall CKM deaths, highlighting malnutrition as an underrecognized component of end‐of‐life vulnerability in CKM and supporting integration of nutritional assessment and supportive care in high‐burden populations and regions.