Noncoding RNAs-mediated overexpression of KIF14 is associated with tumor immune infiltration and unfavorable prognosis in lung adenocarcinoma

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients

Background

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD.

Methods

A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets.

Results

The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group.

Conclusions

This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

Identification and validation of non-coding RNA-mediated high expression of IQGAP3 in poor prognosis of lung adenocarcinoma

BACKGROUND

The primary reason for tumor-related deaths worldwide is lung adenocarcinoma (LUAD). The oncogene IQ motif-containing GTPase activating protein 3 (IQGAP3) is crucial for contributing to tumor initiation and progression. However, the precise function and molecular mechanism of IQGAP3 in LUAD remain unknown. The present study aimed to investigate the expression, prognosis, mechanism and tumor immunity associated with IQGAP3 in LUAD.

METHODS

The relationship between IQGAP3 and the poor prognosis of LUAD was analyzed using The Cancer Genome Atlas (TCGA) database. This analysis was further validated on lung cancer tissues and cell lines. The function of IQGAP3 was investigated by silencing it in LUAD cell lines. To predict microRNA (miRNA) and long non-coding RNA associated with IQGAP3, the starBase database was utilized, and the predictions were verified by enhancing the function of miRNA. Finally, the relationship between IQGAP3 and tumor immunity was evaluated using Spearman's correlation analysis.

RESULTS

TCGA database revealed that higher levels of IQGAP3 were associated with advanced tumor stage, N stage and poor prognosis in LUAD patients. To confirm that, we conducted experiments on lung cancer tissues and cell lines and found that silencing IQGAP3 significantly inhibited tumor cell proliferation and migration. The expression of IQGAP3 showed a negative correlation with has-miR-101-3p and has-miR-135a-5p, whereas it showed a positive correlation with GSEC, AC005034.3 and TYMSOS. Furthermore, the introduction of miRNA-mimics into lung cancer cell resulted in a significant inhibition of cancer cell growth and migration. Following that, the level of IQGAP3 showed a positive correlation with the infiltration of immune cells in tumors.

CONCLUSIONS

These results reveal that IQGAP3 significantly promotes LUAD progression and could serve as a prognostic biomarker for LUAD. Furthermore, IQGAP3 is most likely regulated by the GSEC/TYMSOS-hsa-miR-101-3p axis and the AC005034.3-hsa-miR-135a-5p axis in LUAD.

MiR-101: An Important Regulator of Gene Expression and Tumor Ecosystem

MiRNAs are small single-stranded non-coding RNAs. MiRNA contributes to the transcriptional and post-transcriptional regulation of mRNA in different cell types, including mRNA transcription inhibition and mRNA decay and phenotypes via the effect of several essential oncogenic processes and tumor microenvironment. MiR-101 is a highly conserved miRNA that was found to alter the expression in various human cancers. MiR-101 has been reported to have tumor oncogenic and suppressive effects to regulate tumorigenesis and tumor progression. In this review, we summarize the new findings about the roles of miR-101 in cancers and the underlying mechanisms of targeting genes degradation and microenvironment regulation, which will improve biological understanding and design of novel therapeutics.