BMC Cancer最新文献

Background: Squamous cell carcinoma is the most common type of head and neck cancer. A subset of head and neck squamous cell carcinoma (HNSCC) is caused by human papillomavirus (HPV). Data regarding HPV-driven HNSCC from low- and middle-income countries is lacking.

Methods: A cross-sectional multicentre study of adult patients diagnosed with primary HNSCC between 2016 and 2020 was carried out. HPV status was determined through detection of high-risk HPV mRNA via in situ hybridization (RNAISH). Tumours were classified as being HPV-driven when there was positivity for RNAISH. p16^INK4a immunohistochemistry (IHC) and HPV DNA detection and genotyping (HPVDNA) via a PCR-based method were also carried out. The prognostic utility of RNAISH, p16^INK4a immunohistochemistry (IHC) alone and combined p16^INK4a & HPVDNA were assessed using survival analysis.

Results: A total of 356 cases of HNSCC were assessed for HPV status. Median age was 64 years (IQR: 15), and 69.9% of patients were males. The prevalence of HPV-driven HNSCC was approximately 11.0% (95% CI: 7.9-14.7%). The vast majority of HPV-driven HNSCC were from the oropharynx (94.9%). None of the tumours originating from the oral cavity, larynx and hypopharynx were HPV-driven. Seven different genotypes were found in HPV-driven HNSCC.

Conclusions: In our cohort, HPV-driven HNSCC was primarily encountered in tumours originating from the oropharynx. HPV16 was the most frequently encountered genotype in our cohort. It is recommended that determination of HPV status be based on single testing with RNAISH or dual testing of p16^INK4a IHC in conjunction with HPVDNA as an acceptable alternative in resource limited settings to improve patient stratification for treatment as well as clinical outcomes.

Objective: To construct and validate a predictive model to better differentiate the survival outcomes for locally advanced cervical cancer patients (LACC) receiving concurrent chemoradiotherapy (CCRT).

Methods: Patients were separately classified as vaginal invasion (VI), parametrial invasion (PI) or lymph node metastasis (LNM) based on disease extension. The survival outcomes were compared. Cox regression were performed to identify risk factors. Accordingly, the nomogram was constructed and validated. Moreover, the predictive performance between model and 2018 FIGO staging system was compared.

Results: LNM could significantly impair the survival for patients with VI (5-yr OS:77.7% VS 95.6%) (P = 0.018). Patients with bilateral extension to pelvic sidewall (BEP) and bilateral parametrial invasion (BPI) showed worse 5-yr OS (42.4% and 60.9%) compared to those with unilateral extension to pelvic sidewall (UEP) and unilateral parametrial invasion (UPI) (81.7% and 89.1%) (P < 0.001). Additionally, LNM didn't affect the prognosis of patients with PI. The nomogram incorporating pathology, PI and pelvic LNM was constructed and showed decent predictive performance both in the training and test cohort. Besides, nomogram possessed better predictive ability than 2018 FIGO staging system both in the training (5-yr AUC: 0.79 VS 0.59) and validation cohort (5-yr AUC: 0.77 VS 0.64).

Conclusions: LNM would impair the survival for patients with VI not those with PI and a nomogram was constructed and validated to better differentiate the prognosis for LACC patients than FIGO staging system.

Background: Prostate cancer (PCa) is a major health concern for men worldwide, and its incidence is rising rapidly, so identifying key molecules that regulate the malignant biological behavior of PCa is crucial for early diagnosis and targeted therapy. This study aimed to elucidate the regulatory role of chromosome 21 open reading frame 2 (C21orf2) in the biological functions of prostate cancer cells and further explore the molecular mechanisms by which C21orf2 regulates cell proliferation and apoptosis via the JAK2/STAT3 signaling pathway.

Materials and methods: In vitro, C21orf2's effects on cell proliferation, migration, invasion, and apoptosis were tested using CCK-8, scratch assays, Transwell assays, TUNEL staining, and electron microscopy. Western blotting assessed the impact of C21orf2 on JAK2/STAT3 proteins and related markers. Co-immunoprecipitation and immunofluorescence explored interactions with downstream targets. In vivo, a subcutaneous xenograft model in nude mice validated C21orf2's role in tumor growth and apoptosis.

Results: C21orf2 was upregulated in prostate cancer tissues and regulated the malignant biological behavior of prostate cancer cells, and C21orf2 showed a negative correlation with KCTD5. High expression of C21orf2 promoted the activation of the JAK2/STAT3 signaling pathway and upregulated its downstream target genes, including c-MYC, Cyclin A1, Bcl-2, and Cleaved caspase-3, thereby promoting cell proliferation and inhibiting apoptosis.

Conclusions: Our study indicates that C21orf2 is a potential biomarker for the diagnosis and treatment of prostate cancer and highlights its potential as a therapeutic target through the JAK2/STAT3 signaling pathway in prostate cancer.

Background: Hematopoietic stem cell transplantation (HSCT) is a cornerstone treatment for blood disorders and hematological malignancies, although its efficacy is limited by inefficient stem cell homing to the bone marrow. We previously demonstrated that fucosylated HSC ligands interact with endothelial E-selectin to facilitate homing. However, the downstream consequences of modulating fucosylation in HSCs remain unclear. Here, we systematically characterized how enhancing or inhibiting fucosylation-via recombinant human fucosyltransferase 6 (FTVI) or 2-fluoro-L-fucose (2FF), respectively-affects migration, signaling, and engraftment of human granulocyte-colony stimulating factor-mobilized peripheral blood CD34⁺ (mPB-CD34⁺) cells.

Methods: Live-cell imaging under flow, phosphoproteomics, and transcriptomics were used to characterize rolling dynamics and intracellular signaling, and in vivo homing was assessed in immunodeficient xenograft mouse models.

Results: Fucosylation enhanced tether and sling formation, improved E-selectin binding, and increased homing to the bone marrow and spleen. FTVI-treated cells activated MAPK and PI3K/AKT/mTOR pathways and showed enriched Rho-GTPase signaling, associated with proliferation and migration. In contrast, 2FF-treated cells had impaired migration and reduced rolling efficiency. Long-term xenograft studies showed enhanced bone marrow engraftment/persistence of fucosylated cells without altering lineage output.

Conclusion: Fucosylation critically modulates E-selectin interactions, migration, and intracellular signaling in HSCs. These findings highlight glycoengineering as a promising strategy to enhance HSC transplantation outcomes in cancer therapy.

Background: Hepatocellular carcinoma (HCC) is a highly lethal form of cancer with a global mortality rate that is unparalleled among malignant tumours. The photothermal effect exhibited by nanoparticles offers a novel approach for the treatment of HCC. To perform a systematic review and meta-analysis on the therapeutic effects of nanoparticles on hepatocellular carcinoma (HCC) under the photothermal effect.

Methods: We conducted a comprehensive literature search across four major databases (PubMed, Embase, Web of Science, and Scopus) and performed bibliometric analysis to demonstrate the remarkable therapeutic efficacy of photothermal effect-driven nanoparticles in hepatocellular carcinoma (HCC) treatment within the near-infrared (NIR) biological tissue optical window (wavelength range: 650-1350 nm). The study selection, quality assessment, and data extraction were rigorously performed according to standardized protocols, with particular emphasis on key parameters including control group descriptions, sample sizes of control and experimental groups, tumor volume and weight, as well as therapeutic outcome measures. Sensitivity analysis and bias assessment were implemented, ultimately yielding research conclusions with high reliability.

Results: A total of 36 studies were included, all of which were assessed as high-quality studies. Based on the data of tumor volume and mass, Meta-analysis and sensitivity analysis revealed the significant therapeutic effect of photothermal effect-driven nanoparticles in the treatment of HCC, with a high degree of stability in general. In terms of tumor volume, 34 data sets were obtained from 31 studies, with a WMD of -0.95, a 95% confidence interval of (-1.10, -0.80), an I² value of 98.3%, and a p-value of less than 0.001, indicating that photothermal effect-driven nanoparticles have significant efficacy in reducing tumor volume. Regarding tumor mass, 25 studies provided 29 data sets, with a WMD of -0.90, a 95% confidence interval of (-1.03, -0.77), an I² value of 99.0%, and a p-value of less than 0.001. Sensitivity analysis revealed that excluding the data from Dun et al. (2022) significantly influenced the overall effect sizes for both tumor volume (WMD = -3.49) and tumor mass (WMD = -3.98). Funnel plot analysis indicated good symmetry for tumor volume data, suggesting no significant publication bias, whereas asymmetry was observed for tumor mass data, implying potential bias from small-sample studies.

Conclusion: Photothermal effect-driven nanoparticles exhibit positive therapeutic effects in HCC, primarily manifested in the reduction of tumor volume and mass, offering new ideas and strategies for the treatment of advanced hepatocellular carcinoma.

Background: To investigate the potential of apparent diffusion coefficient (ADC) map-based deep learning and dose distribution-based dosiomics in predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC).

Methods: This retrospective study included 3578 NPC patients from Jiangsu Cancer Hospital receiving intensity-modulated radiation therapy (IMRT). Ninety-four RTLI patients were recruited based on inclusion criteria and matched 1:1 with 97 control subjects using propensity scores. Patients were randomly assigned to the training cohort (n = 135) and the validation cohort (n = 59). Deep transfer learning (DTL) features and dosiomics features were extracted from ADC map and three-dimensional dose distribution, respectively. Pearson's correlation coefficient and the least absolute shrinkage and selection operator (LASSO) regression were employed to identify predictive features. Subsequently, eight machine learning classification models were trained to establish a prediction framework, encompassing Support Vector Machine, K-Nearest Neighbor, Random Forest, Extremely Randomized Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Adaptive Boosting and Multilayer Perceptron. The performance of clinical, DTL, dosiomics and feature fusion model was compared by the area under the curve (AUC).

Results: We constructed six pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that pre-trained WideResNet 101 exhibited superior performance with an AUC of 0.786 in the validation cohort. The clinical model based on D_1cc and induction chemotherapy demonstrated an AUC of 0.794 and the dosiomics model demonstrated an AUC of 0.903. Features fusion model demonstrated the highest AUC values in both the training (0.988) and validation (0.940) cohorts.

Conclusions: The fusion model based on pretreatment ADC map and dose distribution provided a promising way to predict RTLI in NPC patients receiving IMRT, which can support clinicians in making decisions to develop individualized treatment plans and implement preventive measures.