Mycoplasma contamination of cell line cultures is a common, yet often undetected problem in research laboratories. Many of the existing techniques to detect mycoplasma contamination of cultured cells are time-consuming, expensive, and have significant drawbacks. Here, we describe a mycoplasma detection system that is useful for detecting multiple species of mycoplasma in infected cell lines. The system contains three dye-labeled detection aptamers that can specifically bind to mycoplasma-infected cells and a dye-labeled control aptamer that minimally binds to cells. With this system, mycoplasma-contaminated cells can be detected within 30 min by using a flow cytometer, fluorescence microscope, or microplate reader. Further, this system may be used to detect mycoplasma-contaminated culture medium. This study presents an novel mycoplasma detection model that is simple, rapid, inexpensive, and sensitive.

In this research, we conducted an in‐depth analysis of differentially expressed genes associated with mitochondrial depolarisation in non‐small cell lung cancer (NSCLC) using single‐cell sequencing. By combining our findings with cuproptosis‐related genes, we identified 10 significant risk genes: DCN, PTHLH, CRYAB, HMGCS1, DSG3, ZFP36L2, SCAND1, NUDT4, NDUFA4L2 and RPL36A, using univariate Cox regression analysis and machine learning methods. These genes form the core of our prognosis risk prediction model, which demonstrated high specificity and accuracy in predicting patient outcomes, as evidenced by ROC curve analysis. Kaplan–Meier curves further confirmed that patients in the low‐risk group had significantly better survival rates compared to those in the high‐risk group. Our models also provided valuable insights into the tumour microenvironment, immunotherapy sensitivity and chemotherapy response. To facilitate the quantification of the probability of patient survival, we incorporated clinical data into a nomogram. We comprehensively analysed the mutation status and expression patterns of the 10 risk genes using bulk transcriptomic, single‐cell and spatial transcriptomic datasets. Drug target predictions highlighted DSG3, PTHLH, ZFP36L2, DCN and NDUFA4L2 as promising therapeutic targets. Notably, RPL36A emerged as a potential tumour marker for NSCLC, with its expression validated in lung cancer cell lines through qPCR. This study has established a predictive models based on mitochondrial depolarisation genes associated with cuproptosis, aiding clinicians in forecasting overall survival and guiding personalised treatment strategies. The identification of novel tumour markers has paved the way for targeted therapies, and therapeutic targets are critical for advancing the treatment of NSCLC.

Evidence indicates that the long noncoding RNAs are involved in the metformin-mediated anti-cancer processes. However, the potential effects of the long noncoding RNAs in metformin-mediated anti-tumor processes in esophageal squamous cell carcinomas (ESCC) are still elusive. This study uncovered that metformin decreases the level of long noncoding RNAs CCAT1 and SPRY4-IT1 thereby contributing to the down-regulation of c-Myc and vimentin. Also, the RNA level test of human ESCC tissue confirmed the positive correlation between CCAT1 and c-Myc. These findings demonstrated that metformin facilitated anti-cancer effects by targeting the 2 long noncoding RNAs (CCAT1 and SPRY4-IT1) and their consequential targets c-Myc and vimentin. Therefore, the CCAT1 and SPRY4-IT1 might act as novel molecular targets that mediate the anti-tumor effects in esophageal squamous cell carcinoma. This helps in predicting the treatment response of metformin in patients diagnosed with esophageal squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a major challenge in oncology due to its high recurrence rate, highlighting the need to identify recurrence drivers. Based on the data provided by Zhang Zemin’s team in their research, we selected survival metadata from 119 patients with lung squamous cell carcinoma and data on the proportions of 51 immune cell types. We then combined machine learning methods to analyze the intrinsic tumor factors and tumor microenvironment (TME) in the recurrence of lung squamous cell carcinoma (LUSC). Chi-square tests identified immune cell subsets enriched in recurrent patients, such as high-MKI67 Tregs, low-FGFBP2 NK cells, low-FOXP3 Tregs, and low-KLRB1 CD8 + T cells. A random forest algorithm further pinpointed pathological response rate (PRR) as the primary predictive factor, with high-MKI67 Tregs as a key secondary contributor. Univariate and multivariate Cox regression analyses and Kaplan-Meier survival curves confirmed that pathological complete response (pCR) and low Treg_MKI67 expression were associated with better survival ( p = 0.0055 and p = 0.011, respectively). Patients achieving pCR and with low Treg_MKI67 expression had superior recurrence-free survival (RFS; 2-year RFS: 96.7% vs. 57.9% for non-pCR and high Treg_MKI67, p = 0.00066). These findings underscore the prognostic value of PRR and TME markers. They also highlight the potential of using single-cell RNA sequencing and machine learning to guide personalized therapies and reduce recurrence risk by targeting Treg activity, laying the groundwork for integrating immune markers into clinical practice to improve LUSC prognosis.