Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients’ data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

Lung cancer is the second most frequent malignancy and the leading cause of cancer-associated death worldwide. Compared with patients diagnosed at advanced disease stages, early detection of lung cancer significantly improved the 5-year survival rate from 3.3% to 48.8%, which highlights the importance of early detection. Although multiple technologies have been applied to the screening and early diagnosis of lung cancer so far, some limitations still exist so they could not fully suit the needs for clinical application. Evidence show that autoantibodies targeting tumor-associated antigens(TAAs) could be found in the sera of early-stage patients, and they are of great value in diagnosis. Methods, we identified and screened TAAs in early-stage non-small cell lung cancer(NSCLC) samples using the serological analysis of recombinant cDNA expression libraries(SEREX). We measured the levels of the 36 autoantibodies targeting TAAs obtained by preliminary screeningvialiquid chip technique in the training set(332 serum samples from early-stage NSCLC patients, 167 samples from patients with benign lung lesions, and 208 samples from patients with no obvious abnormalities in lungs), and established a binary logistic regression model based on the levels of 8 autoantibodies to distinguish NSCLC samples. Results, We validated the diagnostic efficacy of this model in an independent test set(163 serum samples from early-stage NSCLC patients, and 183 samples from patients with benign lung lesions), the model performed well in distinguishing NSCLC samples with an AUC of 0.8194. After joining the levels of 4 serum tumor markers into its independent variables, the final model reached an AUC of 0.8568, this was better than just using the 8 autoantibodies (AUC:0.8194) or the 4 serum tumor markers alone(AUC: 0.6948). In conclusion, we screened and identified a set of autoantibodies in the sera of early-stage NSCLC patients through SEREX and liquid chip technique. Based on the levels of 8 autoantibodies, we established a binary logistic regression model that could diagnose early-stage NSCLC with high sensitivity and specificity, and the 4 conventional serum tumor markers were also suggested to be effective supplements for the 8 autoantibodies in the early diagnosis of NSCLC.

Background: Despite widespread application of minimally invasive video-assisted thoracic surgery (VATS), postoperative pain following this procedure is still a constant clinical challenge. Serratus anterior plane (SAP) block is one of the regional analgesic techniques with promising outcomes. However, due to the limited duration of action, optimal analgesia is often not achieved with a single injection. We tested whether in patients who have been subjected to routine SAP block under preoperative anesthesia, the addition of a second SAP block 24 hours after surgery, improves quality of recovery, lowers postoperative opioid consumption, and reduces the prevalence of chronic pain. Methods: The present study is a single institutional, prospective, randomized, triple-blinded, placebo-controlled study. Ninety patients undergoing VATS from January 2022 to April 2022 were randomized at 1:1 ratio to receive ultrasound-guided second SAP block with 15 mL 0.375% ropivacaine (SAP block group) or 15 mL normal saline (control group) 24 hours after both groups received routine SAP block with 15 mL 0.375% ropivacaine. The primary outcome was quality of patient recovery, measured using 40-item quality of recovery questionnaire (QoR-40) at postoperative day 2 (POD 2). Secondary outcomes included: postoperative pain scores at rest, postoperative opioid consumptions, number of times that patient controlled analgesia (PCA) pump button was pressed, perioperative complications and adverse effects, prevalence of chronic pain at 2^nd and 3^rd month postoperatively, and length of hospital stay (LOS). Results: A total of 83 patients completed the study: 43 patients in SAP block group and 40 patients in the control group. The global QoR-40 scores on POD 2 and POD 3 were significantly higher among SAP block group patients (180.07±11.34, 182.09±8.20) compared with the control group (172.18±6.15, 177.50±6.94) (P=0.01, P=0.008) respectively. Postoperative pain scores, opioid consumptions and incidence of postoperative nausea and vomiting were significantly lower among patients in SAP block group versus control group. There were no statistically significant differences in perioperative complications and LOS between the two groups. The prevalence of chronic pain at the 2^nd and 3^rd month postoperatively for patients in SAP block group and control group was 16.3%, 14%, and 32.5%, 27.5% respectively. Conclusions: In patients undergoing VATS, application of ultrasound-guided second SAP block 24 hours after surgery improved postoperative quality of life, reduced opioid consumption and related side effects, and lowered the prevalence of chronic pain.

Keratin 6 A (KRT6A) is a member of the keratin family and can participate in the occurrence and development of some tumors. However, there is still a lack of pan-cancer analysis of KRT6A in humans. We studied the pan-cancer role of KRT6A with the help of a variety of external public databases (mainly including TCGA, GTEx, HPA, TIMER2.0, UALCAN, TISIDB, GEPIA2, Kaplan-Meier Plotter, cBioPortal and TISCH2, etc.). We performed immunohistochemistry detection of KRT6A protein expression in lung adenocarcinoma (LUAD) and its adjacent normal tissues. The results showed that KRT6A mRNA and protein were differentially expressed in most tumors, and its expression and function may be regulated by DNA methylation, RNA methylation and protein phosphorylation, and were related to the prognosis and clinical subtypes of patients with different tumors. KRT6A has multiple genetic and epigenetic characteristics in different cancers, and the main type of its genetic variation is mutation. At the same time, KRT6A is closely related to tumor mutation burden (TMB), microsatellite instability (MSI), tumor stemness score (TSC), immune checkpoints, immunomodulatory genes, and tumor immune microenvironment (TIME) in different human tumors. Secondly, the expression level of KRT6A is associated with immunotherapy and drug sensitivity. In addition, bioinformatics analysis and immunohistochemistry experiments verified that KRT6A mRNA and protein were up-regulated in LUAD, and could promote the development process of LUAD by promoting processes such as epidermis development, intermediate filament cytoskeleton and cornified envelope. This is the first pan-cancer analysis of KRT6A, which provides a comprehensive and systematic understanding of its role in various human tumors, and also reveals that KRT6A is expected to become a potential prognostic biomarker and a new target for cancer immunotherapy.

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Despite advancements in treatment, drug resistance and limited therapeutic efficacy persist, underscoring the urgent need for novel and mechanistically informed therapeutic strategies. Identifying genetically supported drug targets may accelerate the development of precision therapies in NSCLC. We implemented an integrative multi-omics framework combining single-cell RNA sequencing (scRNA-seq), genome-wide association studies (GWAS), and molecular quantitative trait locus (QTL) datasets including expression (eQTL), protein (pQTL), and DNA methylation (mQTL) QTLs. Druggable candidates were systematically evaluated using a suite of Mendelian randomization (MR) approaches—including summary data-based MR (SMR), generalized SMR (GSMR), and genetic risk score (GRS) analysis. Epigenetic regulation and downstream signaling were further explored through mediation MR analysis. C4BPA, a complement-regulatory macromolecule, emerged as a risk factor for NSCLC across multiple MR models, with consistent findings validated at both transcriptomic and proteomic levels. Epigenetic activation of C4BPA via DNA methylation was observed, and C4BPA expression was shown to promote NSCLC progression through the inflammatory chemokine CCL8 signaling axis. Sensitivity analyses confirmed the robustness of association inference. Our findings identify C4BPA as a genetically validated and biologically plausible therapeutic target for NSCLC. This study demonstrates the power of integrating single-cell transcriptomics with population-scale omics and association inference to uncover actionable targets, offering a scalable framework for advancing precision oncology in lung cancer.