Cancer Investigation最新文献

Breast cancer is a leading global health concern, while the endocrine resistance in breast cancer poses a critical challenge, directly undermining the long-term effectiveness of hormone therapies and significantly impacting patient survival and treatment outcomes. Hence, the present study aims to elucidate the non-genomic mechanism of ERK1/2 signalling pathway, in conjunction with ER and GPR30 receptors involved in regulation of breast cancer progression in MCF-7 and T47D cells. We assessed cell proliferation using MTT and Trypan blue assays, expression studies by reverse transcription quantitative PCR and western blot analysis, the migratory abilities of cells by scratch-wound healing assay. Our results revealed significant down (90%) regulation of E2-induced ERK phosphorylation, inturn suppression of proliferation rate by 30% and migration by 35% using small molecular inhibitors of ERK in MCF-7 and T47D cells confirming ERK as the central direct target for breast cancer proliferation and development. Collectively, our results suggest that E2-induced 1.5-fold upregulation of phospho ERK1/2 expression promotes breast cancer cell proliferation and migration via a Src/EGFR/ERK pathway. These findings provide a novel strategy of combining endocrine therapy with targeted agents (ERK inhibitors), a cornerstone in managing endocrine-resistant condition, delaying progression and improving outcomes in the treatment of breast cancer.

Background: A low absolute lymphocyte/monocyte ratio (LMR) in the peripheral blood is associated with poor prognosis in various cancers; however, its role as a predictive biomarker has not been well defined in the era of treatment with immune checkpoint inhibitors (ICIs).

Methods: We queried a database of advanced cancer patients treated with at least 1 dose of ICI from 2011 to 2019 to study the association of LMR with overall survival (OS). We calculated LMR at baseline and a median of 21 days after the first cycle of ICI (on-treatment LMR) and considered it low if < 2 and high if ≥ 2. OS was calculated from the initiation of ICI to the date of death or censored at the last follow-up.

Results: We identified 1077 patients treated with ICI, including 880 patients with both baseline and on-treatment assessment of LMR. Patients with low baseline LMR had shorter median OS of 8.8 months (95% CI 7.8-10.3) compared to patients with high baseline LMR (19.4 months [95% CI 16.1-21.7], P < 0.0001). Patients with low baseline LMR whose on-treatment LMR increased to high had longer median OS compared to those whose on-treatment LMR remained low (16.8 vs 7.8 months, P < 0.002). Patients with high baseline LMR whose on-treatment LMR remained high had longer median OS compared to patients with low on-treatment LMR (23.9 vs 9.2 months, P < 0.001). In multivariable analysis, high on-treatment LMR was most highly associated with fewer deaths compared to low on-treatment LMR, regardless of baseline LMR.

Conclusions: We observed that baseline LMR, as well as change in LMR from baseline after the first cycle of ICI were associated with OS in cancer patients treated with ICI.

Purpose: Paraneoplastic fever (PF) is an exclusion diagnosis that affects around 10% of patients in oncology, combining fever of unknown origin and the presence of cancer. There is no consensus or guidelines in the literature about the minimum criteria required for the diagnosis of (PF). The objective of this survey was to select clinical and paraclinical criteria to establish the diagnosis of PF.

Methods: After a review of the literature, 23 categories and 48 items were set up in an online survey. A two-round Delphi questionnaire survey was carried out from May to August 2021 with the participation of experts in several specialties in France and abroad.

Results: Thirty-seven and 33 experts responded in the first and second rounds respectively. Nine items obtained consensus. Among them, the need to rule out suspected infection by a directed bacteriological statement, an up-to-date imaging and doppler ultrasound of the lower limbs was highly consensual. No biological criteria were retained. Thirty-six propositions did not reach consensus and five were considered useless in this setting.

Conclusion: The 9 selected criteria confirm the importance to eliminating differential fever aetiologies whereas no specific clinical or biological markers were retained. This survey constitute the first consensus of experts in this field.

Background: The burden of multiple myeloma (MM) in middle-aged and older adults is rising with global aging. This study aimed to assess the global burden and trends of MM from 1990 to 2021, focusing on morbidity, mortality, and disability-adjusted life years (DALYs).

Methods: Data from the Global Burden of Disease (GBD) 2021 were used, stratified by region, age, and sex. The study focused on individuals over 55 years of age. Trends were analyzed using age-standardized annual percentage change (EAPC) and Bayesian age-partitioned cohort (BAPC) models.

Results: From 1990 to 2021, MM cases, deaths, and DALYs doubled in middle-aged and older adults. Age-standardized incidence, mortality, and DALYs showed significant upward trends. Intermediate socio demographic index (SDI) regions had the highest increase in incidence (2.27 per year). The USA accounted for 20% of the global burden. The disease burden peaked in those aged 65-74. Projections suggest a potential decline in global MM burden by 2050.

Conclusions: MM has become a significant global health burden, with notable regional, gender, and age variations. Targeted strategies are crucial for improving prognosis, particularly in elderly populations.

Gastric cancer (GC) prognosis remains suboptimally defined by conventional clinicopathological parameters, necessitating integrative multi-omics approaches to unravel molecular heterogeneity. This study established a robust multi-omics prognostic framework through synergistic analysis of transcriptomic, epigenomic, and clinical data from 108 GC patients. Genome-wide expression profiling and methylation array analysis identified 1,243 survival-associated transcripts and 8,742 prognostic CpG sites, with cross-omics integration via similarity network fusion revealing three molecular subtypes exhibiting distinct clinical trajectories. The aggressive Subtype 3 demonstrated a 2.87-fold increased mortality risk compared to the favorable Subtype 1, independent of age and tumor stage. A LASSO-derived prognostic signature integrating eight gene expression markers, nine methylation loci, and three clinical parameters achieved superior discrimination (C-index: 0.786 [95% CI: 0.748-0.824], compared to 0.687-0.752 in unimodal models) and 19-28% improvement in time-dependent AUC metrics. The multi-optimized nomogram incorporating molecular risk scores with conventional predictors demonstrated strong calibration (slope 0.967) and clinical utility across validation cohorts (C-index 0.742), significantly outperforming existing stratification systems. Functional characterization revealed subtype-specific enrichment in cell cycle dysregulation and immune evasion pathways, obtaining CDK/PI3K inhibitors as potential therapeutic targets. These findings establish multi-omics integration as a novel strategy for prognostic refinement and precision therapy guidance in GC.

Background: Bone morphogenetic protein 2 (BMP2) is essential for bone development and repair in vertebrates. Its role in tumorigenesis and progression remains incompletely characterized.

Method: Using the Cancer Genome Atlas (TCGA) and bioinformatic tools, we analyzed BMP2 expression, prognostic relevance, genetic alterations, immune infiltration, and signaling pathways across 33 tumor types.

Results: BMP2 exhibited elevated expression in tumor tissues of cholangiocarcinoma (CHOL), glioblastoma (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC) patients, but reduced expression in 10 other cancers. High BMP2 expression correlated with reduced overall survival (OS) in esophageal carcinoma (ESCA), LIHC, lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), and thyroid carcinoma (THCA) patients, and shorter disease-free survival (DFS) in uveal melanoma (UVM) patients. BMP2 mutations and amplifications were frequent in diffuse large B-cell lymphoma (DLBC), skin cutaneous melanoma (SKCM), and uterine corpus endometrial carcinoma (UCEC). BMP2 expression positively correlated with cancer-associated fibroblast (CAF) infiltration and interacts physically with ACVR2A, BMP4, BMPR1A/B, BMPR2, CALR, and HSPA5. Pathway analysis implicated transforming growth factor-beta (TGF-β) signaling pathway.

Conclusions: BMP2 expressions and alterations have tissue-specific prognostic implications. BMP2 may serve as a biomarker and therapeutic target in specific tumors via TGF-β signaling modulation.

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.