2023年 · 第01卷第03期

Background: MicroRNA (miRNA) and mRNA levels in matching specimens were used to identify miRNA-mRNA interactions. We aimed to integrate transcriptome, immunophenotype, methylation, mutation, and survival data analyses to examine the profiles of miRNAs and target mRNAs and their associations with breast cancer (BC) diagnosis. Methods: Based on the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA), differentially expressed miRNAs and targeted mRNAs were screened from experimentally verified miRNA-target interaction databases using Pearson's correlation analysis. We used real-time quantitative reverse transcription polymerase chain reaction to verify BC and benign disease samples, and logistic regression analysis was used to establish a diagnostic model based on miRNAs and target mRNAs. Receiver operating characteristic curve analysis was performed to test the ability to recognize the miRNA-mRNA pairs. Next, we investigated the complex interactions between miRNA-mRNA regulatory pairs and phenotypic hallmarks. Results: We identified 27 and 359 dysregulated miRNAs and mRNAs, respectively, based on the GEO and TCGA databases. Using Pearson's correlation analysis, 10 negative miRNA-mRNA regulatory pairs were identified after screening both databases, and the related miRNA and target mRNA levels were assessed in 40 BC tissues and 40 benign breast disease tissues. Two key regulatory pairs (miR-205-5p/High mobility group box 3 (HMGB3) and miR-96-5p/Forkhead Box O1 (FOXO1)) were selected to establish the diagnostic model. They also had utility in survival and clinical analyses. Conclusions: A diagnostic model including two miRNAs and their respective target mRNAs was established to distinguish between BC and benign breast diseases. These markers play essential roles in BC pathogenesis.

Background: The number of lymph nodes examined (LNe) is often insufficient in patients with rectal cancer (RC) treated with neoadjuvant therapy; however, its prognostic value remains controversial. Thus, we retrospectively explored whether LNe had an influence on staging and prognosis and investigated whether there was a cut-off value for better prognosis in patients with RC treated with neoadjuvant therapy. Methods: Data were collected from seven prospective hospital databases in China from July 2002 to May 2018. Binary logistic regression models were used to predict lymph node metastasis. The cut-off value for LNe was determined using X-tile 3.6.1. Survival outcomes and risk factors were analyzed using the log-rank test and Cox regression model. Results: A total of 482 patients were included, of whom 459 had complete overall survival (OS) information. Using the percentile method, the total number of lymph nodes examined (TLNe) was 14-16 (40th-60th percentile), and the proportion of patients with lymph node metastasis reached a maximum of 48.1%. Cox multivariate analysis showed that the odds ratio (OR) remained the highest when TLNe was 14-16 (OR = 3.379, P= 0.003). The 3-year and 5-year OS were 85.4% and 77.8%, respectively. Negative lymph nodes examined (NLNe) of ≤6 was an independent risk factor for 3-year and 5-year OS (3-year OS 71.1% vs. 85.9%, P = 0.004; 5-year OS 66.3% vs. 74.3%, P = 0.035). Subgroup analysis for patients with ypN + showed that higher 3-year and 5-year OS were achieved when the TLNe was >10, 78.8% vs. 54.0% (P = 0.005), and 60.8% vs. 36.0% (P= 0.012), respectively. Patients with ypN0M0 had a higher 5-year OS when the TLNe was >19 (P = 0.055). Conclusion: The TLNe and NLNe influenced the staging accuracy and demonstrated prognostic value in patients with RC treated with neoadjuvant therapy.

Adult-onset brain cancers, such as glioblastomas, are particularly lethal. People with glioblastoma multiforme (GBM) do not anticipate living for more than 15 months if there is no cure. The results of conventional treatments over the past 20 years have been underwhelming. Tumor aggressiveness, location, and lack of systemic therapies that can penetrate the blood-brain barrier are all contributing factors. For GBM treatments that appear promising in preclinical studies, there is a considerable rate of failure in phase I and II clinical trials. Unfortunately, access becomes impossible due to the intricate architecture of tumors. In vitro, bioengineered cancer models are currently being used by researchers to study disease development, test novel therapies, and advance specialized medications. Many different techniques for creating in vitro systems have arisen over the past few decades due to developments in cellular and tissue engineering. Later-stage research may yield better results if in vitro models that resemble brain tissue and the blood-brain barrier are used. With the use of 3D preclinical models made available by biomaterials, researchers have discovered that it is possible to overcome these limitations. Innovative in vitro models for the treatment of GBM are possible using biomaterials and novel drug carriers. This review discusses the benefits and drawbacks of 3D in vitro glioblastoma modeling systems.

Prostate cancer (PCa) is the most common malignancy in men. Despite aggressive therapy involving surgery and hormonal treatments, the recurrence and emergence of metastatic castration-resistant prostate cancer (CRPCa) remain a major challenge. Dysregulation of the transforming growth factor-β(TGF-β) signaling pathway is crucial to PCa development and progression. This also contributes to androgen receptor activation and the emergence of CRPC. In addition, TGF-β signaling regulates long non-coding RNA (lncRNA) expression in multiple cancers, including PCa. Here, we discuss the complex regulatory network of lncRNAs and TGF-β signaling in PCa and their potential applications in diagnosing, prognosis, and treating PCa. Further investigations on the role of lncRNAs in the TGF-β pathway will help to better understand PCa pathogenesis.

Background: A high body mass index (BMI) can indicate overweight or obesity and is a crucial risk factor for breast cancer survivors. However, the association between high BMI and prognosis in early-stage breast cancer (EBC) remains unclear. We aimed to assess the effects of high BMI on the prognosis of patients with EBC. Methods: The PubMed, Embase, and Cochrane Library databases and proceedings of major oncological conferences related to the effects of BMI on the prognosis of breast cancer were searched up to November 2021. Fixed- and random-effects models were used for meta-analyses. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for disease-free survival (DFS) and overall survival (OS) were extracted from the included literature. Results: Twenty retrospective cohort studies with 33,836 patients with EBC were included. Overweight patients had worse DFS (HR: 1.16, 95% CI: 1.05-1.27, P = 0.002) and OS (HR: 1.20; 95% CI: 1.09-1.33, P < 0.001). Obesity also had adverse effects on DFS (HR: 1.17, 95% CI: 1.07-1.29, P = 0.001) and OS (HR: 1.30, 95% CI: 1.17-1.45, P < 0.001). Likewise, patients with high BMI had worse DFS (HR: 1.16, 95% CI: 1.08-1.26, P < 0.001) and OS (HR: 1.25, 95% CI: 1.14-1.39, P < 0.001). In subgroup analyses, overweight had adverse effects on DFS (HR: 1.11, 95% CI: 1.04-1.18, P = 0.001) and OS (HR: 1.18, 95% CI: 1.11-1.26, P < 0.001) in multivariate analyses, whereas the relationship that overweight had negative effects on DFS (HR: 1.21, 95% CI: 0.99-1.48, P = 0.058) and OS (HR: 1.39, 95% CI: 0.92-2.10, P = 0.123) was not statistically significant in univariate analysis. By contrast, obesity had adverse effects on DFS (HR: 1.21, 95% CI: 1.06-1.38, P = 0.004 and HR: 1.14, 95% CI: 1.08-1.22, P < 0.001) and OS (HR: 1.33, 95% CI: 1.15-1.54, P < 0.001 and HR: 1.23, 95% CI: 1.15-1.31, P < 0.001) in univariate and multivariate analyses, respectively. Conclusions: Compared with normal weight, increased body weight (overweight, obesity, and high BMI) led to worse DFS and OS in patients with EBC. Once validated, these results should be considered in the development of prevention programs.

This report involves a 54-year-old female patient diagnosed with diffuse large B-cell lymphoma who developed interstitial pneumonia (IP) during treatment. The patient presented to the ward with enlarged lymph nodes in the neck and was treated with the standard regimen, which included rituximab, cyclophosphamide, doxorubicin liposomes, vincristine, and prednisone (R-CDOP regimen). After 3 cycles, the treatment was assessed as effective. However, following the 4th cycle, the patient experience shortness of breath after physical activity. A repeat lung computer tomography indicated IP, which completely recovered after receiving "full coverage" treatment. Subsequently, the patient underwent 2 cycles of cyclophosphamide, doxorubicin liposomes, vincristine, and prednisone (CDOP), followed by local radiotherapy. Currently, the patient is now being followed up with regular reviews.