Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Multiple programmed cell death patterns predict the prognosis and drug sensitivity in gastric cancer

Background

Gastric cancer (GC) is a malignant tumor with poor prognosis. The diverse patterns of programmed cell death (PCD) are significantly associated with the pathogenesis and progression of GC, and it has the potential to serve as prognostic and drug sensitivity indicators for GC.

Method

The sequencing data and clinical characteristics of GC patients were downloaded from The Cancer Genome Atlas and GEO databases. LASSO cox regression method was used to screen feature genes and develop the PCD score (PCDS). Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore immunotherapy response. By integrating PCDS with clinical characteristics, we constructed and validated a nomogram that demonstrated robust predictive performance.

Results

We screened nine PCD-related genes (SERPINE1, PLPPR4, CDO1, MID2, NOX4, DYNC1I1, PDK4, MYB, TUBB2A) to create the PCDS. We found that GC patients with high PCDS experienced significantly poorer prognoses, and PCDS was identified as an independent prognostic factor. Furthermore, there was a significant difference in immune profile between high PCDS and low PCDS groups. Additionally, drug sensitivity analysis indicated that patients with a high PCDS may exhibit resistance to immunotherapy and standard adjuvant chemotherapy regimens; however, they may benefit from the FDA-approved drug Dasatinib.

Conclusion

Overall, we confirmed that the PCDS is a prognostic risk factor and a valuable predictor of immunotherapy response in GC patients, which provides new evidence for the potential application of GC.

Identification and Validation of a m6A-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer

Accumulating research indicates that N6-methyladenosine (m6A) modification plays a pivotal role in colorectal cancer (CRC). Hence, investigating the m6A-related long noncoding RNAs (lncRNAs) significantly improves therapeutic strategies and prognostic assessments. This study aimed to develop and validate a prognostic model based on m6A-related lncRNAs to improve the prediction of clinical outcomes and identify potential immunological mechanisms in CRC. We obtained high-throughput CRC data from The Cancer Genome Atlas to identify a prognostic model based on m6A-related lncRNAs. Then, the model was constructed and validated through LASSO analysis and Cox regression using R software. The clinical applicability was enhanced by developing a nomogram. We further conducted experiments to reveal the biological function of LINC00543. The prognostic model based on eight m6A-related lncRNAs exhibited impressive accuracy, achieving an area under the receiver-operating curve value of 0.753, 0.682 and 0.706 for predictions after 1, 3 and 5 years, respectively. The Kaplan-Meier analysis confirmed the consistency of the model across different pathological characteristics, with a high-risk group showing a poorer prognosis. Furthermore, the model was linked to immune function, particularly the type I interferon response, through gene set enrichment analysis and experimental validation. Our study presented a m6A-related lncRNA prognostic model for CRC with potential clinical utility. The model not only provided improved accuracy over traditional staging but also offered insights into the immunological mechanisms of CRC, facilitating personalised medicine approaches.

Crosstalk between lncRNAs and Wnt/β-catenin signaling pathways in lung cancers: From cancer progression to therapeutic response

Lung cancer (LC) is considered to have the highest mortality rate around the world. Because there are no early diagnostic signs or efficient clinical alternatives, distal metastasis and increasing numbers of recurrences are a challenge in the clinical management of LC. Long non-coding RNAs (lncRNAs) have recently been recognized as a critical regulator involved in the progression and treatment response to LC. The Wnt/β-catenin pathway has been shown to influence LC occurrence and progress. Therefore, discovering connections between Wnt signaling pathway and lncRNAs may offer new therapeutic targets for improving LC treatment and management. In this review, the purpose of this article is to present possible therapeutic approaches by reviewing particular relationships, key processes, and molecules associated to the beginning and development of LC.

Combination of pemetrexed with bevacizumab for non-small-cell lung cancer: a meta-analysis study

BACKGROUND

Combining pemetrexed with bevacizumab may have some potential in improving the efficacy in patients with non-small-cell lung cancer (NSCLC), and this meta-analysis aims to explore the impact of pemetrexed addition to bevacizumab on treatment efficacy for NSCLC.

METHODS

PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases were systematically searched, and we included randomized controlled trials (RCTs) assessing the effect of pemetrexed addition to bevacizumab on treatment efficacy in patients with NSCLC. Overall survival and progression-free survival were included in this meta-analysis.

RESULTS

Four RCTs were finally included in the meta-analysis. Overall, compared with bevacizumab for NSCLC, pemetrexed addition showed significantly improved overall survival (hazard ratio [HR] = 0.87; 95% confidence interval [CI] = 0.76 to 0.99; P = 0.03), survival rate (odd ratio [OR] = 1.41; 95% CI = 1.06 to 1.86; P = 0.02), progression-free survival (HR = 0.63; 95% CI = 0.55 to 0.72; P < 0.00001) and progression-free survival rate (OR = 1.92; 95% CI = 1.38 to 2.67; P < 0.00001), but led to the increase in grade ≥ 3 adverse events (OR = 2.15; 95% CI = 1.62 to 2.84; P < 0.00001).

CONCLUSIONS

Pemetrexed addition may be effective to improve treatment efficacy for NSCLC compared to bevacizumab treatment.

Novel Long Non-Coding RNA (lncRNA) Transcript AL137782.1 Promotes the Migration of Normal Lung Epithelial Cells through Positively Regulating LMO7

The role of long non-coding RNA (lncRNAs) in biological processes remains poorly understood, despite their significant impact. Our previous research discovered that the expression of AL137782.1, a long transcript of the novel lncRNA ENSG00000261553, is upregulated in lung epithelial cells upon exposure to microbes. Furthermore, the expression of AL137782.1 exhibits variability between para-cancerous and lung adenocarcinoma samples. These findings imply that this lncRNA may play a role in both normal lung epithelial cellular processes and pathophysiology. To elucidate the function of AL137782.1 in lung epithelial cells, we utilized bioinformatics retrieval and analysis to examine its expression. We then analyzed its subcellular localization using fluorescence in situ hybridization (FISH) and subcellular fractionation. Through rapid amplification of cDNA ends (RACE), we confirmed the presence of a 4401 nt lncRNA AL137782.1 in lung epithelial cells. Moreover, we discovered that this lncRNA positively regulates both mRNA and the protein expression of LMO7, a protein that may regulate the cell migration of normal lung epithelial cells. Although the overexpression of AL137782.1 has been shown to enhance the migration of both normal lung epithelial cells and lung adenocarcinoma cells in vitro, our study revealed that the expression of this lncRNA was significantly decreased in lung cancers compared to adjacent tissues. This suggests that the cell migration pattern regulated by the AL137782.1-LMO7 axis is more likely to occur in normal lung epithelial cells, rather than being a pathway that promotes lung cancer cell migration. Therefore, our study provides new insights into the mechanism underlying cell migration in human lung epithelial cells. This finding may offer a potential strategy to enhance normal lung epithelial cell migration after lung injury.