Cancer Investigation最新文献

The direct and indirect effects of socioeconomic status (SES) on colorectal cancer (CRC) were examined using structural equation modeling in a case-control study with 488 CRCs and 651 controls. SES was measured by education, income and resident region. SES (odds ratio (OR)=0.89), age (OR = 1.03), processed meat intake (OR = 1.08), lack of CRC screening (OR = 2.67), smoking (OR = 1.85) and family history (OR = 1.06) were significantly associated with CRC risk. SES had a direct effect on CRC risk (β = -0.05). An indirect effect of SES on CRC also existed which was mediated by processed meat intake (β = -0.02), vegetable intake (β = -0.01), CRC screening uptake (β = -0.02), and smoking (β = -0.02).

Introduction: Colorectal cancer (CRC) ranks third globally in cancer incidence. Aurora Kinase A (AURKA) critically regulates tumor proliferation and microenvironment, yet its dual CRC roles remain unclear.

Methods: We integrated bulk RNA-seq, scRNA-seq, and 10x Visium spatial transcriptomics to profile AURKA. Immune infiltration was assessed via CIBERSORT/ssGSEA. Clinical validation used IHC/HE staining. Immunotherapy associations were tested in ICB cohorts and murine models.

Results: Pan-cancer analysis showed CRC-specific AURKA prognostic value (p < 0.05). High AURKA correlated with prolonged OS (median 68 vs 42 months; log-rank P = 0.034), conventional adenocarcinoma (p < 0.001), left-sided tumors (p < 0.001), and absent perineural invasion (p = 0.041). Pathway analyses linked AURKA to cell cycle (G2/M checkpoint) and immune pathways (IL-2/STAT5). Spatial transcriptomics identified peritumoral niches (clusters 6/7/12) co-expressing AURKA, CD4, MKI67, and immune-activation markers (HLA-DRB1, CXCL10). IHC confirmed AURKA-CD4 + T-cell correlation (R = 0.66, p < 0.05). scRNA-seq revealed AURKA dominance in proliferating T cells. High AURKA predicted anti-PD-1 response (HR = 0.44, p = 0.003) and CD4+ memory T-cell expansion in murine models.

Conclusion: AURKA dually regulates tumor proliferation and immune engagement. Its spatial enrichment in T-cell niches supports its use as an immunotherapy biomarker.

Given the limited diagnostic technologies and treatment options available for lung adenocarcinoma (LUAD) patients with liver metastases, it is crucial to identify potential genomic signatures associated with liver metastasis, which could significantly contribute to the development of improved diagnostic tools and treatment strategies for LUAD patients with liver metastases. In this study, we identified specific genetic alterations in tumor samples with liver metastases by targeted capture sequencing. The results showed that the significantly higher mutation frequencies of KRAS, STK11 and ERBB2 in LUAD patients with liver metastases and ERBB2 and STK11 mutations found in both tumor tissues and plasma samples from patients with liver metastases. In addition, the higher mutation frequencies of KRAS and STK11 in the group with early-stage liver metastasis suggested that mutations in KRAS and STK11 may play crucial roles in promoting liver metastases in LUAD patients at an early stage. Furthermore, the significantly higher TMB in the late-stage liver metastasis group indicated that patients with late-stage liver metastasis may have a better response to immunotherapy compared to those with early-stage liver metastasis. These findings provide valuable insights for developing detection tools and tailoring individualized treatments for such patients.

The SEER (Surveillance, Epidemiology, and End Results) database, a comprehensive public repository of clinical oncology data, has been increasingly used to construct clinical prediction models for predicting the prognosis of cancer. With the advances in machine learning, various algorithms including logistic regression (LR), support vector machines (SVM), decision trees (DT), random forest (RF), artificial neural networks (ANN), and extreme gradient boosting (XGBoost) have been successively employed in the development of lung cancer survival prediction models (LCSPMs). This study combs through the progress of these machine learning algorithms in constructing lung cancer survival prediction models, points out the problems of data imbalance, poor model interpretability, and lack of external validation, and clarifies the future development direction.

Breast cancer (BC) is the second most prevalent cause of death for women and the most frequently diagnosed malignancy. Early identification of this deadly illness lowers treatment costs while significantly improving survival rates. In contrast, skilled radiologists and pathologists analyze radiographic and histopathological images, respectively. In addition to being expensive, the procedure is prone to errors. The paper offers a solution to these challenges by presenting an innovative approach that combines a Modified U-Net architecture with sophisticated self-supervised learning methods to the accuracy and efficiency of breast cancer detection in WSIs. The proposed model improves the accuracy of tumor detection by integrating a multi-stage process: starting with Gaussian filtering for image preprocessing to remove noise, followed by the Modified U-Net for precise tumor segmentation including multi-scale processing and attention mechanisms. Feature extraction is achieved through the Bag of Visual Words (BoW), Improved Local Gradient and Intensity Pattern (LGIP), and Pyramidal Histogram of Oriented Gradients (PHOG) techniques to capture diverse image characteristics. The classification phase employs an Improved Self-Supervised Learning (ISSL) method, which improves feature representation via a novel loss function and an improved Multiple Instance Pooling (IMIP) mechanism. This method is designed to overcome the limitations of conventional techniques by offering clearer tumor boundaries and more accurate classifications, thereby improving the overall reliability and efficacy of breast cancer detection in clinical practice. Moreover, the ISSL strategy yielded the highest performance metrics, including an accuracy of 0.924, a sensitivity of 0.886, and a negative predictive value (NPV) of 0.943.

Background: Breast cancer (BC) is one of the most prevalent malignant tumors among women globally. The incidence and mortality rates of female BC exhibit significant variation across different countries and regions.

Objective: This study analyzed the trends of BC among Chinese women from 1997 to 2021 to support evidence-based for the prevention, screening and treatment strategies of female BC in China.

Methods: We extracted data on BC incidence, mortality, prevalence, disability-adjusted life years (DALYs), years lived with disability (YLDs) and years of life lost (YLLs) among Chinese women from 1997 to 2021 from the Global Burden of Disease (GBD)database. Join point regression analysis was used to identify the major turning points of disease burden trends, and to calculate the annual percentage change (APC) and average annual percentage change (AAPC). We applied age-period-cohort (A-P-C) models to separately evaluate the effects of age, period, and cohort on trends in female BC in China.

Results: In 2021, the age standardized incidence rate (ASIR) and DALYs of female BC in China were 37.12 (95% CI: 28.23,46.95) and 281.54(95% CI: 216.87,358.11) per 100,000 women respectively. The AAPC values of the incidence and mortality of female BC were 2.42% (95% CI 2.04-2.80) and -0.49% (95% CI -0.70--0.28) respectively (p < 0.05). A-P-C model indicated that both the rates of incidence, prevalence and deaths increased with age from 1997 to 2021. The period effect analysis revealed that the prevalence and incidence risk of BC peaked between 2015 and 2020, with the highest rate ratio (RR) value 1.28 (95% CI 1.25-1.31) and 1.22 (95% CI 1.19-1.25). The cohort born in 2002 exhibited the lowest risk of mortality and the highest risk of incidence and prevalence.

Conclusions: Over the past 25 years, the large population size and aging population structure in China have led to female BC becoming an important public health issue. Effective preventive strategies and individualized treatment approaches are urgently required to enhance the control of BC in China.

Breast cancer's heterogeneity demands innovative therapies. Co-delivery of therapeutics using nanocarriers, especially siRNA combined with other chemotherapeutic drugs, presents a promising avenue. These systems safeguard siRNA, enhance its cellular uptake, and facilitate simultaneous targeting of multiple oncogenic pathways. This multifaceted approach holds potential for superior efficacy and reduced toxicity, addressing the limitations of conventional treatments and paving the way for improved breast cancer therapy.

This review analyzed 12 studies to evaluate the safety, immunogenicity, and therapeutic efficacy of peptide-based cancer vaccines across various tumor types, including breast, gynecological, head and neck, and gastrointestinal cancers. The included studies involved a total of 520 patients and preclinical models. The findings indicated that peptide vaccines are generally safe, with no serious adverse events reported in clinical trials, and demonstrated robust immunogenicity, eliciting specific T-cell responses in up to 85.7% of patients. Importantly, the durability of T-cell responses varied across studies, with some demonstrating sustained immune memory that could enhance long-term protection against tumor recurrence.