Up and away with cervical cancer: IL-29 is a promising cytokine for immunotherapy of cervical cancer due to its powerful upregulation of p18, p27, and TRAILR1.

Cervical cancer is one of the most common types of female cancers worldwide. IL-29 is an interesting cytokine in the IFNλ family. Its role in the pathogenesis of neoplasia is complicated and has been studied in other cancers, such as lung cancer, gastric cancer, and colorectal cancer. IL-29 has been previously reported to promote the growth of pancreatic cancer. However, the direct role of IL-29 in cervical cancer has not been studied yet. This study was performed to investigate the direct effect on cervical cancer cell growth. Clonogenic survival assay, cell proliferation, and caspase-3 activity kits were used to evaluate the effects of IL-29 on cell survival, proliferation, and apoptosis of a well-studied cervical cancer cell line, SiHa. We further investigated the potential molecular mechanisms by using RT-PCR and IHC. We found that the percentage of colonies of SiHa cells was decreased in the presence of IL-29. This was consistent with a decreased OD value of cancer cells. Furthermore, the relative caspase-3 activity in cancer cells increased in the presence of IL-29. The anti-proliferative effect of IL-29 on cancer cells correlated with increased expression of the anti-proliferative molecules p18 and p27. The pro-apoptotic effect of IL-29 on cancer cells correlated with increased expression of the pro-apoptotic molecule TRAILR1. IL-29 inhibits cervical cancer cell growth by inhibiting cell proliferation and promoting cell apoptosis. Thus, IL-29 might be a promising cytokine for immunotherapy of cervical cancer.

Mast Cell-related Prognosis Signature Characterizes Immune Landscape and Predicts Prognosis of Ovarian Cancer.

BACKGROUND/AIM: Ovarian cancer (OVC) is a common, aggressive, and heterogeneous malignancy, with a widely variable prognosis. With the advances of modern immunology, mast cells (MCs) have been shown to play a significant role in the prognosis of some malignant tumors. However, the role of mast cells in the prognosis of OVC is unknown. MATERIALS AND METHODS: In this study, MC-associated prognostic genes (MRGs) were used to classify OVC from The Cancer Genome Atlas (TCGA)-OVC cohort. Genes were evaluated using univariate cox regression analysis. Twenty-nine prognostic gene signatures were identified using LASSO-COX analysis. COX regression models and principal component analysis (PCA) algorithms were used to construct MRG scores and individual MRGs patterns. External validation was performed in the TCGA-breast cancer (BRCA) and IMvigor210 cohorts. Immunity analysis based on MRGs was performed using CIBERSORT, and GSVA methods, and immunotherapy response was evaluated using the TIDE website. RESULTS: Using TCGA-OVC data, we established a model for constructing MRG scores based on the twenty-nine identified prognostic gene signatures using the PCA algorithm. MRG scores were found to be strongly correlated with immune cell infiltration and were excellent predictors of prognosis in patients with OVC. Low MRG scores were associated with better prognosis and better response to immunotherapy and chemotherapy. CONCLUSION: MC-related prognosis signature characterizes the immune landscape and predicts the prognosis of OVC. Understanding the correlation between MC-related gene signatures and immunotherapy and chemotherapy may improve the development of personalized clinical treatment strategies.

Determination of Ideal Factors for Early Adoption and Standardization of Metagenomic Next-Generation Sequencing for Respiratory System Infections.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) demonstrates great promise as a diagnostic tool for determining the cause of pathogenic infections. The standard diagnostic procedures (SDP) include smears and cultures and are typically viewed as less sensitive and more time-consuming when compared to mNGS. There are concerns about the logistics and ease of transition from SDP to mNGS. mNGS lacks standardization of collection processes, databases, and sequencing. Additionally, there is the burden of training clinicians on interpreting mNGS results. OBJECTIVE: Until now, few studies have explored factors that could be used as early adoption candidates to ease the transition between SDP and mNGS. This study evaluated 123 patients who had received both SDP and mNGS and compared several variables across a diagnostic test evaluation. METHODS: The diagnostic test evaluation observed metrics such as sensitivity, specificity, positive and negative likelihood ratios (PLR, NLR), positive and negative predictive values (PPV, NPV), and accuracy. Factors included various sample sources such as bronchoalveolar lavage fluid (BALF), lung tissue, and cerebral spinal fluid (CSF). An additional factor observed was the patient's immune status. RESULTS: Pathogen detection was found to be significantly greater for mNGS for total patients, BALF sample source, CSF sample source, and non-immunocompromised patients (p<0.05). Pathogen detection was found to be insignificant for lung tissue sample sources and immunocompromised patients. Sensitivity, PLR, NLR, PPV, NPV, and accuracy appeared to be higher with mNGS for the total patients, BALF sample source, and non-immunocompromised patients when compared with SDP (p<0.05). CONCLUSION: With higher metrics in sensitivity, specificity, PLR, NLR, PPV, NPV, and accuracy for overall patients, mNGS may prove a better diagnostic tool than SDP. When addressing sample sources, mNGS for BALF-collected samples appeared to have higher scores than SDP for the same metrics. When patients were in a non-immunocompromised state, mNGS also demonstrated greater diagnostic benefits to BALF and overall patients compared to SDP. This study demonstrates that using BALF as a sample source and selecting non-immunocompromised patients may prove beneficial as early adoption factors for mNGS standard protocol. Such a study may pave the road for mNGS as a routine clinical method for determining the exact pathogenic etiology of lung infections.