DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

The molecular crosstalk between innate immunity and DNA damage repair/response: Interactions and effects in cancers

DNA damage can lead to erroneous alterations and mutations which in turn can result into wide range of disease condition including aging, severe inflammation, and, most importantly, cancer. Due to the constant exposure to high-risk factors such as exogenous and endogenous DNA-damaging agents, cells may experience DNA damage impairing stability and integrity of the genome. These perturbations in DNA structure can arise from several mutations in the genome. Therefore, DNA Damage Repair/Response (DDR) detects and then corrects these potentially tumorigenic problems by inducing processes such as DNA repair, cell cycle arrest, apoptosis, etc. Additionally, DDR can activate signaling pathways related to immune system as a protective mechanism against genome damage. These protective machineries are ignited and spread through a network of molecules including DNA damage sensors, transducers, kinases and downstream effectors. In this review, we are going to discuss the molecular crosstalk between innate immune system and DDR, as well as their potential effects on cancer pathophysiology.

A novel prognostic signature of coagulation-related genes leveraged by machine learning algorithms for lung squamous cell carcinoma

Coagulation-related genes (CRGs) have been demonstrated to be essential for the development of certain tumors; however, little is known about CRGs in lung squamous cell carcinoma (LUSC). In this study, we adopted CRGs to construct a coagulation-related gene prognostic signature (CRGPS) using machine learning algorithms. Using a set of 92 machine learning integrated algorithms, the CRGPS was determined to be the optimal prognostic signature (median C-index = 0.600) for predicting the prognosis of an LUSC patient. The CRGPS was not only superior to traditional clinical parameters (e.g., T stage, age, and gender) and its commutative genes but also outperformed 19 preexisting prognostic signatures for LUSC on predictive accuracy. The CRGPS score was positively correlated with poor prognoses in patients with LUSC (hazard ratio > 1, p < 0.05), indicating its suitability as a prognostic marker for this disease. The CRGPS was observed to be inversely correlated with the degree of infiltration of natural killer cells. For some tumors, patients with lower CRGPS scores are more likely to experience enhanced immunotherapy effects (area under the curve = 0.70), which implies that the CRGPS can potentially predict immunotherapy efficacy. A high CRGPS score is predictive of an LUSC patient being sensitive to several drugs. Collectively, these findings indicate that the CRGPS may be a reliable indicator of the prognoses of patients with LUSC and may be useful for the clinical management of such patients.

Immunogenic chemotherapy: great potential for improving response rates

The activation of anti-tumor immunity is critical in treating cancers. Recent studies indicate that several chemotherapy agents can stimulate anti-tumor immunity by inducing immunogenic cell death and durably eradicate tumors. This suggests that immunogenic chemotherapy holds great potential for improving response rates. However, chemotherapy in practice has only had limited success in inducing long-term survival or cure of cancers when used either alone or in combination with immunotherapy. We think that this is because the importance of dose, schedule, and tumor model dependence of chemotherapy-activated anti-tumor immunity is under-appreciated. Here, we review immune modulation function of representative chemotherapy agents and propose a model of immunogenic chemotherapy-induced long-lasting responses that rely on synergetic interaction between killing tumor cells and inducing anti-tumor immunity. We comb through several chemotherapy treatment schedules, and identify the needs for chemotherapy dose and schedule optimization and combination therapy with immunotherapy when chemotherapy dosage or immune responsiveness is too low. We further review tumor cell intrinsic factors that affect the optimal chemotherapy dose and schedule. Lastly, we review the biomarkers indicating responsiveness to chemotherapy and/or immunotherapy treatments. A deep understanding of how chemotherapy activates anti-tumor immunity and how to monitor its responsiveness can lead to the development of more effective chemotherapy or chemo-immunotherapy, thereby improving the efficacy of cancer treatment.

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

BACKGROUND

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making.

MATERIALS AND METHODS

We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively).

RESULTS

The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC.

CONCLUSIONS

The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

Clinical significance of DNA damage response mutations in stage I and stage IIIa NSCLC

BACKGROUND

DNA damage response (DDR) pathways are essential to sustain genomic stability and play a critical role in cancer development and progression. Here, we investigated the profile of DDR gene mutations in early-stage non-small cell lung cancer (NSCLC) and their prognostic values.

METHODS

We first examined 74 DDR genes involved in seven DDR pathways and then focused on six specific genes: ATM, BRCA1, BRCA2, CHEK1, BARD1, and BRIP1. A total of 179 stage I and IIIa NSCLC patients who received curative resection in Peking Union Medical College Hospital and their corresponding samples were collected for DNA sequencing, immunohistochemistry and survival analysis.

RESULTS

A total of 167 eligible patients were finally analyzed. Mutation frequencies were 82% and 26.3% for the selected 74 genes and six genes, respectively. Mismatch repair (MMR) and nucleotide excision repair (NER) alterations were observed more frequently in lung squamous cell carcinoma (LUSC) and smokers were more likely to develop the selected six DDR gene mutations than those who never smoked. Deleterious mutations in the six genes were independent prognostic indicators of significantly longer disease-free survival and overall survival. No association was found between DDR gene status and PD-L1 expression, CD8 positive lymphocyte and tumor-associated macrophage infiltration in tumor area. However, numbers of mutations were significantly increased among patients with DDR alterations.

CONCLUSIONS

Deleterious mutations of these six genes were common in resected NSCLC and could serve as prognostic biomarkers.

MMP12 serves as an immune cell-related marker of disease status and prognosis in lung squamous cell carcinoma

Background

Worldwide, lung squamous cell carcinoma (LUSC) has wreaked havoc on humanity. Matrix metallopeptidase 12 (MMP12) plays an essential role in a variety of cancers. This study aimed to reveal the expression, clinical significance, and potential molecular mechanisms of MMP12 in LUSC.

Methods

There were 2,738 messenger RNA (mRNA) samples from several multicenter databases used to detect MMP12 expression in LUSC, and 125 tissue samples were validated by immunohistochemistry (IHC) experiments. Receiver operator characteristic (ROC) curves, Kaplan-Meier curves, and univariate and multivariate Cox regression analyses were used to assess the clinical value of MMP12 in LUSC. The potential molecular mechanisms of MMP12 were explored by gene enrichment analysis and immune correlation analysis. Furthermore, single-cell sequencing was used to determine the distribution of MMP12 in multiple tumor microenvironment cells.

Results

MMP12 was significantly overexpressed at the mRNA level (p < 0.05, SMD = 3.13, 95% CI [2.51-3.75]), which was verified at the protein level (p < 0.001) by internal IHC experiments. MMP12 expression could be used to differentiate LUSC samples from normal samples, and overexpression of MMP12 itself implied a worse clinical prognosis and higher levels of immune cell infiltration in LUSC patients. MMP12 was involved in cancer development and progression through two immune-related signaling pathways. The high expression of MMP12 in LUSC might act as an antigen-presenting cell-associated tumor neoantigen and activate the body's immune response.

Conclusions

MMP12 expression is upregulated in LUSC and high expression of MMP12 serves as a risk factor for LUSC patients. MMP12 may be involved in cancer development by participating in immune-related signaling pathways and elevating the level of immune cell infiltration.

Association of variations in the CAT and prognosis in lung cancer patients with platinum-based chemotherapy

PURPOSE: To explore the relationship between ATM, ATR and CAT polymorphisms and prognosis of lung cancer patients received platinum-based chemotherapy.

METHODS: 404 patients with lung cancer who received platinum-chemotherapy were enrolled and DNA typing was performed. Cox regression analysis and stratification analyses was performed to assess relationships between OS and PFS with SNPs genotypes. The prognosis of lung adenocarcinomaand squamous cell carcinomapatients was analyzed with The Cancer Genome Atlas (TCGA) database according to the grouping of CAT expression.

RESULTS:CAT rs769217 was significantly related to PFS of patients with lung cancer who received platinum-chemotherapy. In the Additive model, rs769217 was associated with PFS (HR = 0.747, 95% CI = 0.581–0.960, p = 0.023). In the Dominant model, CT and TT genotypes led to lung cancer progression 0.738 times more than CC genotype. In stratification analyses of association between CAT rs769217 polymorphisms and PFS, the HR of patients at stage IV in additive model was 0.73, and HR was 0.745 (p = 0.034) in dominant model. For OS analyses, HR was 0.672 in the older lung cancer patients (&gt;55 years old) in additive model. Meanwhile, in the Dominant model, it was found that the older patients with CT and TT genotypes had better prognosis, and the risk of death after receiving platinum-based chemotherapy was 0.692 times that of patients with CC genotype (p = 0.037). TCGA data shows that LUAD patients with high CAT expression have longer OS (p = 0.020).

CONCLUSION:CAT rs769217 is significantly related to PSF of platinum-based chemotherapy in lung cancer patients and may be a biomarker for predicting the prognosis of lung cancer patients with platinum-based chemotherapy.

The Mechanism of Quercetin in the Treatment of Lung Squamous Cell Carcinoma Based on a Protein-Protein Interaction Network

Background

Lung squamous cell carcinoma (LUSC) is characterized by poor prognosis and obvious limitations of therapeutic methods. The molecular target and mechanism of quercetin (QR), a natural anticancer product with extensive pharmacological activities, on lung squamous cell carcinoma is still unclear.

Method

The effects of QR on LUSC were examined using cell proliferation, migration, and invasion tests. Key target genes were screened using The Cancer Genome Atlas (TCGA) database, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) database, STRING website, topology, and prognosis analysis, molecular docking, and other bioinformatics methods for further analysis. Finally, the effects of QR on the expression of key targets in LUSC cells were detected using a cell cycle assay and western blotting.

Results

Our study demonstrates that QR not only inhibits the proliferation of LUSC but also affects the invasion and metastasis of LUSC. After downloading and analyzing the TCGA database, 2150 differentially expressed genes were identified. PLK1, CDC20, and BUB1B were identified using enrichment analysis, topological network analysis, cluster analysis, and molecular docking screening. Subsequent experiments showed that QR could interfere with the cell cycle and downregulate the expression of the target gene PLK1 at the protein level.

Conclusions

We found that QR not only inhibits the proliferation, migration, and invasion but also blocks the cell cycle progression of LUSC. QR downregulated the expression of the LUSC target gene PLK1 at the protein level.