Identification and validation of tumor microenvironment-related genes of prognostic value in lung adenocarcinoma

Construction of a genomic instability-derived predictive prognostic signature for non-small cell lung cancer patients

BACKGROUND

Genomic instability (GI) is an effective prognostic marker of cancer. Thus, in this work, we aimed to explore the impact of GI derived signature on prognosis in non-small cell lung cancer (NSCLC) patients using bioinformatics methods.

METHODS

The data of NSCLC patients were collected from The Cancer Genome Atlas. Totally 1794 immune related genes were downloaded from immport database. The optimal prognosis related genes were identified by univariate and LASSO Cox analyses. The risk score model was built to predict the NSCLC patients' prognosis. The immune cell infiltration was analyzed in CIBERSORT.

RESULTS

The 951 differentially expressed genes (DEGs) between the genomic stability (GS) and GI groups were enriched in 862 Gene ontology terms and 32 Kyoto Encyclopedia of Genes and Genomes pathways. Based on the 13 optimal genes, a prognostic risk score mode for NSCLC was established, and the high-risk patients exhibited worse overall survival. Moreover, the nomogram could reliably predict the clinical outcomes. The immune cell infiltration and checkpoints were significantly differential between the two groups (high-risk and low-risk).

CONCLUSION

The GI related 13-gene signature (TMPRSS11E, TNNC2, HLF, FOXM1, PKMYT1, TCN1, RGS20, SYT8, CD1B, LY6K, MFSD4A, KLRG2 APCDD1L) could reliably predict the prognosis of NSCLC patients.

Knockdown of ANLN inhibits the progression of lung adenocarcinoma via pyroptosis activation

Significant advancements have been achieved in the area of molecular targeted therapy for lung adenocarcinoma (LUAD). However, the complex molecular patterns and high heterogeneity of LUAD confine the efficacy of these therapies to a specific subset of patients; therefore, it is necessary to explore novel targets for LUAD treatment. The expression levels of anillin (ANLN) in LUAD were analyzed using the Gene Expression Profiling Interactive Analysis database. Furthermore, the association between ANLN gene expression and patient survival outcomes was evaluated using the Kaplan‑Meier Plotter. Subsequently, small interfering RNA (siRNA) transfection was performed to knock down ANLN in A549 and H1299 cell lines, after which, TUNEL, colony formation and Transwell assays were conducted to assess cell death, colony formation and migration, respectively. Additionally, western blot analysis was performed to analyze the expression levels of caspase‑1, interleukin (IL)‑18 (IL‑18), IL‑1β, NLR family pyrin domain‑containing 3 (NLRP3), apoptosis‑associated speck‑like protein containing a CARD domain (ASC) and cleaved gasdermin D (GSDMD) following ANLN knockdown. The results revealed that ANLN mRNA expression was significantly increased in LUAD tissues compared with adjacent normal samples. Furthermore, the expression levels of ANLN displayed an increasing trend with advancing clinical stage. Furthermore, patients with high ANLN expression levels exhibited poor overall survival rates compared with those with low ANLN expression levels. Subsequent ANLN knockdown experiments indicated elevated cell death rate, and reduced colony formation and migration in both A549 and H1299 cells. Additionally, ANLN knockdown resulted in increased protein expression levels of pyroptosis‑associated molecules, including caspase‑1, NLRP3, cleaved‑GSDMD, IL‑1β, ASC and IL‑18 in both A549 and H1299 cells. In conclusion, ANLN represents an important gene and a promising therapeutic target for LUAD. Its potential as a therapeutic target makes it an interesting candidate for further exploration in the development of novel treatment strategies for LUAD.

Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Immune checkpoints expression patterns in early-stage triple-negative breast cancer predict prognosis and remodel the tumor immune microenvironment

Background

Currently, targeting immune checkpoint molecules holds great promise for triple-negative breast cancer (TNBC). However, the expression landscape of immune checkpoint genes (ICGs) in TNBC remains largely unknown.

Method

Herein, we systematically investigated the ICGs expression patterns in 422 TNBC samples. We evaluated the ICGs molecular typing based on the ICGs expression profile and explored the associations between ICGs molecular subtypes and tumor immune characteristics, clinical significance, and response to immune checkpoint inhibitors (ICIs).

Results

Two ICGs clusters and two ICGs-related gene clusters were determined, which were involved in different survival outcomes, biological roles and infiltration levels of immune cells. We established a quantification system ICGs riskscore (named IRS) to assess the ICGs expression patterns for individuals. TNBC patients with lower IRS were characterized by increased immune cell infiltration, favorable clinical outcomes and high sensitivity to ICIs therapy. We also developed a nomogram model combining clinicopathological variables to predict overall survival in TNBC. Genomic feature analysis revealed that high IRS group presented an increased tumor mutation burden compared with the low IRS group.

Conclusion

Collectively, dissecting the ICGs expression patterns not only provides a new insight into TNBC subtypes but also deepens the understanding of ICGs in the tumor immune microenvironment.

Combination of Tumor Mutational Burden and DNA Damage Repair Gene Mutations with Stromal/Immune Scores Improved Prognosis Stratification in Patients with Lung Adenocarcinoma

Background

Both the tumor environment and the genomic landscape of lung cancer may shape patient responses to treatments, including immunotherapy, but their joint impacts on lung adenocarcinoma (LUAD) prognosis are underexplored.

Methods

RNA sequencing data and whole-exome sequencing results were downloaded from the TCGA database, and only LUAD-related data were included in this study. Based on gene expression data, the ESTIMATE algorithm was used to estimate stromal and immune scores, and CIBERSORT analysis was used for quantification of the relative abundances of immune cells. Somatic mutations were used for calculating tumor mutation burden (TMB). Specific mutations in genes involved in DNA damage repair (DDR) pathways were identified. The individual and joint associations of stromal and immune score, TMB, and DDR gene mutations with 5-year survival were analyzed by the Kaplan–Meier method and multivariate Cox model.

Results

LUAD patients with a high (>highest 25%) stromal or immune score had prolonged survival as compared to those with a low (<lowest 25%) score (log-rank P=0.05 and 0.035, respectively). Patients with both high stromal and immune scores had the most favorable survival. Although the survival differences between patients with high (>highest 25%) and low (<lowest 25%) TMB, or between patients with mutant- and wild-type DDR genes were not statistically significant, a survival benefit from high TMB or DDR gene mutations was observed in patients with high stromal or immune scores.

Conclusion

A comprehensive evaluation of transcriptomic signatures and genomic biomarkers may provide a novel avenue for improving prognosis stratification in LUAD.

A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.

Identification of the GTPase IMAP family as an immune-related prognostic biomarker in the breast cancer tumor microenvironment

INTRODUCTION

Breast cancer is the most common malignancy threatening women's health worldwide. The GTPase IMAP family genes are proteins belonging to the immune-associated nucleotide subfamily of the GTP-binding superfamily and nucleotide-binding proteins. However, little is known about the role of different GTPase IMAP family genes in breast cancer.

METHODS

We obtained differential genes from the GEPIA and UALCAN databases and then used the Kaplan-Meier plotter, The Human Protein Atlas, NetworkAnalyst, STRING, and TIMER to analyze the prognostic value, protein expression, and immune cell infiltration of the GTPase IMAP family in patients with breast cancer.

RESULTS

Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly low in breast tumor tissues. In the overall population, patients with high expression of all genes of the GIMAP family had a significantly higher overall survival (OS), with the most significant increase correlated with the GIMAP2 gene (hazard ratio [HR] = 0.45, 95% confidence interval [CI], 0.34-0.59, P = 3.1e-09). However, patients with high expression of the GIMAP family genes in triple-negative breast cancer compared to those with low expression had a significant OS benefit, with the most pronounced benefit correlated with the GIMAP2 gene (HR = 0.37, 95% CI, 0.23-0.59, P = 1.4e-05). GIMAP7 and GIMAP8 were significantly upregulated in breast tumor tissues. The expression of genes in different GIMAP families was positively correlated with the infiltration and expression of six immune cell types (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells).

CONCLUSION

This study may provide novel insights into the selection of GIMAP family prognostic biomarkers for breast cancer.

An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

Background

A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients.

Methods

Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores.

Results

Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival.

Conclusions

Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.

Combination of tumor mutation burden and immune infiltrates for the prognosis of lung adenocarcinoma

BACKGROUND

Tumor mutation burden (TMB) levels are associated with immune infiltrates in the tumor microenvironment and can modulate the responses to immune checkpoint inhibitors (ICIs) in lung adenocarcinoma (LUAD) patients. This study aimed at exploring the potential role of a signature of genes associated with TMB and immune infiltrates and the relevant nomogram in the prognosis of LUAD.

MATERIALS AND METHODS

The TMB levels in LUAD patients in the Cancer Genome Atlas (TCGA) were analyzed. The differentially expressed genes (DEGs) between the higher- and lower-TMB subgroups were functionally analyzed. The immune-related DEGs and their relationship with immune infiltrates in the tumor environment between two subgroups were analyzed. Nine immune-related DEGs were used to generate a TMB-related immune signature. The sensitivity to immunotherapy in TCGA-LUAD patients was analyzed by immunophenotypic scores (IPS). Subsequently, a nomogram was generated using tumor-related parameters and the signature score. The signature or nomogram values in predicting overall survival (OS) were evaluated and validated in LUAD patients in the GSE30219 and GSE72094.

RESULT

There were 468 DEGs between the higher and lower-TMB subgroups of LUAD patients. The TMB levels were associated positively with the number of immune infiltrates in LUAD patients. Nine DEGs were related to immune infiltrates in the tumor environment. The higher signature scores (high-risk) were associated with poor prognosis of LUAD in the TCGA, which was validated in LUAD patients of the GSE30219 and GSE72094 datasets. Interestingly, the patients in the high-risk group had higher PD-L1 expression in their tumors and the risk scores in LUAD patients. The IPS of LUAD patients in the high-risk group were predicted to benefit from immunotherapy. Finally, the nomogram had high AUC values in predicting the OS of LUAD patients.

CONCLUSION

The TMB-related immune signature or nomogram is valuable for the prognosis of LUAD patients and evaluating their responses to ICIs. These relevant genes may participate into the pathogenesis, ICIs, and drug resistance of LUAD.

Identification of TYROBP and FCER1G as Key Genes with Prognostic Value in Clear Cell Renal Cell Carcinoma by Bioinformatics Analysis

The involvement of aberrantly expressed genes in the pathogenesis and progression of various human malignancies has been widely reported, including clear cell renal cell carcinoma (ccRCC). This study aimed to identify potential crucial genes in ccRCC and further investigate the role of these genes in ccRCC prognosis. Three gene expression profiles (GSE3, GSE6344 and GSE53000) were downloaded from GEO database. GEO2R was performed to identify the differentially expressed genes (DEGs) between ccRCC and normal samples. GO analysis and KEGG pathway enrichment analysis were applied for the function analysis. The DEGs were mapped into the PPI network, then the hub genes were identified and verified using the ONCOMINE database. Kaplan-Meier plotter was used to evaluate of the prognostic value of the identified hub genes. A total of 113 DEGs were identified from the three gene expression profiles, including 64 up-regulated genes and 69 down-regulated genes. DEGs were observed to be enriched in biological processes related to the progress and pathogenesis of human cancers. According to PPI network, 5 hub genes were collected, including TYROBP, C1QB, ITGB2, CD53 and FCER1G. Among them, CD53 was newly identified, and Kaplan-Meier survival curves suggested that high expression of CD53 was significantly associated with poor survival in ccRCC patients (log-rank P < 0.01). The present results may provide new insight into the understanding of molecular mechanisms and the clinical prognosis of ccRCC.