American journal of cancer research最新文献

Gastrin-releasing peptide precursor (ProGRP) is a bioactive precursor of GRP and might play an important role as an emerging tumor marker in early cancer diagnosis. It might also be abnormal in the nonmalignant disease and renal function abnormalities. The present study was undertaken to investigate the changes of ProGRP levels in patients with kidney injuries, especially with chronic kidney disease (CKD), determine the upper reference intervals and clinical diagnostic value of ProGRP in CKD, and thus help oncologists in interpreting ProGRP levels and making clinical judgments of malignances. 676 individuals were enrolled in this cross-sectional study and divided into five groups: healthy control (n=194), CKD (n=272), nephrotic syndrome (NS) (n=137), antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) (n=41), and urinary tract infection (UTI) (n=32). A total of 27 features including age, gender, and 25 laboratory markers were analyzed. Machine learning algorithms were built for the diagnostic models of CKD. Statistical analysis was performed by R software. It was shown that serum ProGRP level in CKD was significantly higher than that in healthy controls, UTI and NS (P < 0.01). The upper reference limit of ProGRP was 188.42 pg/ml for CKD, 245.40 pg/ml for CKD IV-V, and 97.25 pg/ml for NS. Compared with the healthy control, the level of serum ProGRP in CKD stages II, III, IV-V was significantly increased and elevated progressively with CKD grade (P < 0.01). Random Forest (RF) model works best among 4 building machine learning algorithms. 5 vital indicators, ProGRP, estimated glomerular filtration rate (eGFR), urea, albumin (ALB), and direct bilirubin (DBIL), were selected to establish RF model for diagnosing CKD with an area under the curve (AUC) of 0.96 (95% confidence interval [CI]: 0.94-0.97) and high sensitivity (0.89) and specificity (0.92). This study demonstrates that the level of ProGRP in patients with CKD, nephrotic syndrome or AAV, was significantly higher than that in the healthy population. The machine learning model of ProGRP with DBIL, eGFR, ALB, and urea, could provide good clinical value for CKD evaluation.

Objectives: To evaluate the predictive value of combining CT radiomics features and inflammatory markers for the preoperative prediction of spread through air spaces (STAS) in pulmonary adenocarcinoma.

Methods: In this retrospective study, we analyzed data from 256 patients diagnosed with pulmonary adenocarcinoma between 2021 and 2023. Patients were categorized into two groups based on the presence (n = 115) or absence (n = 141) of STAS, as confirmed by histopathological examination. CT imaging data and routine blood test results, including inflammatory markers, were collected. A validation cohort of 233 patients was included for external validation. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify independent predictors of STAS. Model performance was assessed using Receiver Operating Characteristic curve analysis.

Results: Key CT radiomics features, such as density, satellite lesions, irregular shape, spiculation, vascular convergence, and the vacuole sign, were significantly associated with STAS. Among inflammatory markers, a lower lymphocyte-to-monocyte ratio (LMR) and higher neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios (PLR) were predictive of STAS. The combined predictive model, integrating CT radiomics and inflammatory markers, demonstrated a high discriminatory ability, achieving an area under the curve of 0.915, which was externally validated with an AUC of 0.847.

Conclusions: The combination of CT radiomics and inflammatory markers provides an effective, non-invasive preoperative tool for predicting STAS in pulmonary adenocarcinoma, aiding in early prognostication and treatment planning.

The term "metabolic dysfunction-associated steatotic liver disease" (MASLD) was introduced to replace the term "nonalcoholic fatty liver disease". The prevalence of MASLD is increasing worldwide. The prevalence of concomitant MASLD and hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is also increasing. This study explored the effect of the coexistence of MASLD and HBV on clinicopathological features and long-term clinical prognoses in patients with MASLD-related and/or HBV-related HCC after curative hepatectomy. The study retrospectively collected the data of 653 patients with HCC who had undergone curative hepatectomy between 2011 and 2022. We assessed the association of histologically confirmed MASLD with HCC recurrence and mortality. Of 653 patients, 320 (49.0%), 103 (15.8%), and 230 (35.2%) had concomitant MASLD and HBV, MASLD only, and HBV only, respectively. The median follow-up period was 5.1 years. Patients with concomitant MASLD and HBV were at a significantly increased risk of HCC recurrence (P = 0.013 and P = 0.041) and mortality (P = 0.044 and P = 0.026) than those with MASLD or HBV alone. In multivariable analyses, concomitant MASLD and HBV, male sex, body mass index < 23, absence of antiviral therapy, and tumor size ≥ 5 cm were significantly associated with increased HCC recurrence. Concomitant MASLD and HBV, male sex, type 2 diabetes mellitus, serum aspartate aminotransferase ≥ 40 U/L, tumor size ≥ 5 cm, tumor cell differentiation II-III, microvascular invasion, lymph node invasion, and tumor recurrence were significantly associated with increased mortality. In conclusion, patients with concomitant MASLD and HBV are at a significantly greater risk of HCC recurrence and mortality after curative hepatectomy than those with MASLD or HBV alone.

Chronic obstructive pulmonary disease (COPD) patients face an increased risk of developing various malignancies due to shared risk factors and underlying systemic inflammation. N-acetylcysteine (NAC) has shown potential anticancer properties in preclinical studies, but clinical evidence in COPD patients is limited. We conducted a nationwide propensity score-matched cohort study using data from Taiwan's National Health Insurance Research Database to evaluate the anticancer effects of NAC in COPD patients. Patients diagnosed with COPD between 2008 and 2019 were included, and those with pre-existing cancer were excluded. NAC use was defined as consistent administration for most days with an average dose exceeding 28 cumulative defined daily doses (cDDDs) annually. Cox regression models were adjusted for various covariates was employed. PSM yielded 91,546 patients, evenly distributed between NAC and non-NAC groups. Multivariate Cox regression analysis revealed a lower cancer risk in patients with long-term NAC use compared to non-users (adjusted hazard ratio [aHR] 0.69, 95% confidence interval [CI] 0.66-0.72; P<0.001). Dose-dependent relationships were observed, with higher daily NAC intake associated with reduced cancer risk. Time-varying Cox regression analysis demonstrated significant reductions in the risk of specific cancers, including hepatocellular carcinoma, colorectal cancer, and breast cancer, among NAC users compared to non-users. Our study provides clinical evidence supporting the potential anticancer effects of NAC in COPD patients. These findings highlight the importance of exploring NAC as a chemopreventive agent in high-risk populations and inform clinical practice and future research endeavors.

Objective: To construct a nomogram incorporating clinical-radiological and radiomics features from computed tomography (CT) for distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in ground-glass nodules (GGNs).

Methods: This retrospective study included 473 GGN patients with postoperative pathological confirmation of AIS, MIA, or IAC. The training set comprised 257 patients from Yantaishan Hospital, while the test set, used for external validation, included 216 patients from the Affiliated Hospital of Binzhou Medical College. Radiomics features were selected, and a radiomics model was constructed using least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) methods. A clinical-radiological model was developed using univariate and multivariate logistic regression. The nomogram was generated by combining the two models. Its performance was evaluated via receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).

Results: The radiomics model included 11 features, while the clinical-radiological model incorporated lobulation, age, and long diameter. The nomogram outperformed both individual models in terms of accuracy and area under the curve (AUC) in both the training and test sets. Calibration curve analysis confirmed good consistency between actual and predicted outcomes, and DCA indicated the nomogram's clinical utility.

Conclusion: The nomogram is a non-invasive, accurate tool for preoperative differentiation of GGN types, providing valuable guidance for clinicians in treatment planning.

Behavioral clinical trials among cancer patients often fail to meet recruitment goals - especially for underrepresented groups. Comparing recruitment strategies on participant accrual and cost can inform the use of cost-effective recruitment strategies for enrollment of diverse populations of cancer patients. In this study, we compared social media, internet sites, and clinic-based recruitment on accrual, cost, and characteristics of cancer patients (i.e., sociodemographic, cancer type/stage, and smoking habits) enrolled in a randomized trial of app-based smoking cessation interventions. Fisher's exact tests for categorical variables and analysis of variance for continuous variables were used to compared data between recruitment strategies. In 35 months, 427 cancer patients from 45 US states enrolled in the trial out of 3,936 screened (rate of participation, 10.8%). Social media recruited over eight times the number of enrolled participants (n=340, 79.6%) compared with Internet sites (n=43, 10.1%) and clinics (n=42, 9.8%). Most (80.1%) participants were women, with mean age 52.3 years. About 20.4% of participants were from underrepresented racial/ethnic backgrounds, 23.0% were rural residents, and 23.7% were uninsured. Over 32 cancer types and all cancer stages were represented. Breast cancer was the most common diagnosis (n=129/427, 30.2%), followed by lung cancer (n=96/427, 23.8%). Internet recruitment generated a higher proportion of men (30.2% vs. 26.2% clinics vs. 17.4% social media, P=.005). Clinics generated a higher proportion of Hispanic participants (9.5% vs. 7.0% Internet vs. 2.6% social media, P=.04) and cancer patients aged 65 and older (28.6% vs. 11.5% social media vs. 4.7% Internet, P=.01). Social media recruited a higher proportion of participants with low income (<$20,000: 39.1% vs. 23.3% Internet vs. 19.0% clinics, P<.001), who tended to have later stage cancers (stage IV: 17.4% vs. 14.0% Internet vs. 7.1% clinics, P=.05). Cost per randomized participant ranged from $270 via social media to $454 via Internet sites to $2,240 via clinic-based recruitment. In conclusion, social media was the most efficient and cost-effective method for recruiting a quality sample of racially/ethnically, geographically, socioeconomically, and clinically diverse sample of cancer patients into a smoking cessation clinical trial. Social media has solid potential for recruiting cancer patients into behavioral clinical trials.

[This corrects the article on p. 2064 in vol. 8, PMID: 30416856.].

Tumor metastasis leads to circulating tumor cells (CTCs) that separate from primary malignant tumors and enter blood circulation. CTCs survive and engage with other cells to cope with obstacles, including shear stress, disease, immune attacks, and drugs. Platelets are the best partners for CTCs. Platelets provide a good protective layer for CTCs to ensure that are not monitored and cleared by the native immune system, and protected from shear stress and survive better. Here, we review current reports on platelet-CTC interaction and the clinical relevance of their combination and summarize new techniques for CTC capture and treatment based on platelet-CTC interaction. We discuss current data, identify its shortcomings, and suggest future developments.

Objective: To develop an accurate predictive model for identifying patients at high risk of pulmonary infection during radiochemotherapy.

Methods: We retrospectively analyzed data from 544 lung cancer patients treated at Hubei Cancer Hospital between May 2019 and October 2022. The patients were divided into training and validation groups (7:3 ratio). An external validation cohort of 100 patients treated from November 2022 to January 2024 was also included. Feature selection and model development were performed using machine learning algorithms, including Lasso regression, Random Forest, XGBoost, and Support Vector Machine (SVM). Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis.

Results: Key predictive factors for pulmonary infection risk were identified, including diabetes, chronic obstructive pulmonary disease, chemotherapy intensity, chemotherapy cycles, antibiotic use, age, Karnofsky Performance Status score, systemic inflammation index, prognostic nutritional index, and C-reactive protein. A nomogram-based prediction model was constructed, achieving ROC curve Area Under the Curve values of 0.889 in the training set, 0.897 in the validation set, and 0.875 in the external validation set, demonstrating strong classification ability and stability.

Conclusion: We developed a robust nomogram-based model incorporating eight key factors to predict the risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy. This model can assist clinicians in early identification of high-risk patients, enabling timely interventions to improve patient outcomes and quality of life.

The current investigation sought to explore the effects and its possible mechanisms of Scavenger receptor class A member 5 (SCARA5) in model of colon cancer (CC). Patients diagnosed with CC were recruited from our hospital. SCARA5 mRNA expression levels in patients with CC was reduced, and the expression of SCARA5 mRNA in patients with I-II was higher than that of patients with III-IV. The overall survival (OS) and disease-free survival (DFS) of SCARA5 high expression were higher those of SCARA5 low expression in patients with CC. Sh-SCARA5 promoted CC in mice model. SCARA5 up-regulation reduced cell growth of CC. SCARA5 up-regulation promoted Ferroptosis of CC by the inhibition of mitochondrial damage. SCARA5 up-regulation induced ferritin light chain (FTL) protein expression. Si-FTL attenuated the effects of SCARA5 on Ferroptosis in CC. The SCARA5 protein interlinked with the FTL protein. SCARA5 up-regulation reduced FTL protein ubiquitination. Up-regulation of SCARA5 suppressed cell growth in CC. SCARA5 up-regulation enhanced ferroptosis in CC by inhibiting mitochondrial damage. We conclude that SCARA5 promoted ferroptosis in CC cells by inhibiting FTL ubiquitination-induced mitochondrial damage, which may contribute to the treatment of CC.