Overexpression of SH2D1A promotes cancer progression and is associated with immune cell infiltration in hepatocellular carcinoma via bioinformatics and in vitro study

Downregulation of DIP2B as a prognostic marker inhibited cancer proliferation and migration and was associated with immune infiltration in lung adenocarcinoma via CCND1 and MMP2

Background

DIP2B is related to cancer progression. This study investigated the roles and pathways of DIP2B in lung adenocarcinoma (LUAD).

Methods

DIP2B expression and the relationship between survival time of cancer patients and DIP2B expression were analyzed. The relationship between DIP2B expression and survival time in LUAD patients was evaluated by a meta-analysis. Cox and survival analyses were used to evaluate the prognostic factors and construct a prognostic nomogram. The mechanisms and effects of DIP2B and the relationship between DIP2B expression and the immune microenvironment were investigated using bioinformatics, CCK-8, western blotting, and transwell experiments.

Results

DIP2B was overexpressed in LUAD tissues. DIP2B overexpression was associated with shorter prognosis and was an unfavorable risk factor for prognosis in LUAD patients. DIP2B co-expressed genes were involved in cell division, DNA repair, cell cycle, and others. Inhibition of DIP2B expression could downregulate the proliferation, migration, and invasion of LUAD A549 and H1299 cells, which was related to the decrease in CCND1 and MMP2 protein expression. BRCA1 overexpression was associated with short prognosis, and the nomogram formed by DIP2B and BRCA1 was associated with a poor prognosis in LUAD patients. DIP2B expression correlated with immune cells (such as CD8 T cells, Tcm, and iDCs) and cell markers.

Conclusion

DIP2B is a potential biomarker of poor prognosis and the immune microenvironment in LUAD. Inhibition of DIP2B expression downregulated cancer cell proliferation, migration, and invasion, which might be related to the decrease in CCND1 and MMP2 protein expression. DIP2B-related nomograms might be useful tools for predicting the prognosis of LUAD patients.

A novel prognostic model for predicting patient survival and immunotherapy responsiveness in hepatocellular carcinoma: insights into the involvement of T-cell proliferation

BACKGROUND

The cancer-associated biological mechanisms and the implementation of immunotherapy are heavily impacted by the activities of T cells, consequently influencing the effectiveness of therapeutic interventions. Nevertheless, the mechanistic actions of T-cell proliferation in response to immunotherapy and the overall prognosis of individuals diagnosed with hepatocellular carcinoma (HCC) remains insufficiently understood. The present work seeks to present a comprehensive analysis immune landscape in the context of HCC.

METHODS

To achieve this objective, both clinical data and RNA sequencing data were acquired from authoritative databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).

RESULTS

Through the utilization of consensus clustering techniques, distinct molecular subtypes associated with T-cell proliferation were delineated. Following this, seven genes of prognostic significance were identified via a combination of Cox and Lasso regression analyses. By integrating these genes into a prognostic signature, the predictive capability of the model was verified through an examination of internal and external datasets. Moreover, immunohistochemistry and qRT-PCR tests have verified the reliability of prognostic markers. Notably, the high-risk group exhibited elevated expression of immune checkpoint genes as well as higher benefit in terms of drug sensitivity testing, as determined by the Chi-square test (P < 0.001). The risk score derived from the prognostic signature depicted considerable efficacy in predicting the survival outcomes of HCC cases.

CONCLUSIONS

Overall, prognostic markers may become valuable predictive tool for individuals diagnosed with HCC, allowing for the prediction of their prognosis as well as the assessment of their immunological condition and response to immunotherapy.

Overexpression of MTFR1 promotes cancer progression and drug-resistance on cisplatin and is related to the immune microenvironment in lung adenocarcinoma

OBJECTIVE

The roles of MTFR1 in the drug resistance of lung adenocarcinoma (LAC) to cisplatin remain unexplored. In this study, the expression, clinical values and mechanisms of MTFR1 were explored, and the relationship between MTFR1 expression and immune microenvironment was investigated in LAC using bioinformatics analysis, cell experiments, and meta-analysis.

METHODS

MTFR1 expression and clinical values, and the relationship between MTFR1 expression and immunity were explored, through bioinformatics analysis. The effects of MTFR1 on the growth, migration and cisplatin sensitivity of LAC cells were identified using cell counting kit-8, wound healing and Transwell experiments. Additionally, the mechanisms of drug resistance of LAC cells involving MTFR1 were investigated using western blotting.

RESULTS

MTFR1 was elevated in LAC tissues. MTFR1 overexpression was associated with sex, age, primary therapy outcome, smoking, T stage, unfavourable prognosis and diagnostic value and considered an independent risk factor for an unfavourable prognosis in patients with LAC. MTFR1 co-expressed genes involved in the cell cycle, oocyte meiosis, DNA replication and others. Moreover, interfering with MTFR1 expression inhibited the proliferation, migration and invasion of A549 and A549/DDP cells and promoted cell sensitivity to cisplatin, which was related to the inhibition of p-AKT, p-P38 and p-ERK protein expression. MTFR1 overexpression was associated with stromal, immune and estimate scores along with natural killer cells, pDC, iDC and others in LAC.

CONCLUSIONS

MTFR1 overexpression was related to the unfavourable prognosis, diagnostic value and immunity in LAC. MTFR1 also participated in cell growth and migration and promoted the drug resistance of LAC cells to cisplatin via the p-AKT and p-ERK/P38 signalling pathways.