Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis

BackgroundDespite the high prevalence of lung cancer, with a five-year survival rate of only 23%, the underlying molecular mechanisms of non-small cell lung cancer (NSCLC) remain unknown. There is a great need to identify reliable candidate biomarker genes for early diagnosis and targeted therapeutic strategies to prevent cancer progression.MethodsIn this study, four datasets obtained from the Gene Expression Omnibus were evaluated for NSCLC- associated differentially expressed genes (DEGs) using bioinformatics analysis. About 10 common significant DEGs were shortlisted based on their p-value and FDR (DOCK4, ID2, SASH1, NPR1, GJA4, TBX2, CD24, HBEGF, GATA3, and DDR1). The expression of significant genes was validated using experimental data obtained from TCGA and the Human Protein Atlas database. The human proteomic data for post- translational modifications was used to interpret the mutations in these genes.ResultsValidation of DEGs revealed a significant difference in the expression of hub genes in normal and tumor tissues. Mutation analysis revealed 22.69%, 48.95%, and 47.21% sequence predicted disordered regions of DOCK4, GJA4, and HBEGF, respectively. The gene-gene and drug-gene network analysis revealed important interactions between genes and chemicals suggesting they could act as probable drug targets. The system-level network showed important interactions between these genes, and the drug interaction network showed that these genes are affected by several types of chemicals that could serve as potential drug targets.ConclusionsThe study demonstrates the importance of systemic genetics in identifying potential drug- targeted therapies for NSCLC. The integrative system- level approach should contribute to a better understanding of disease etiology and may accelerate drug discovery for many cancer types

Insect pollination as an agronomic input: strategies for oilseed rape production

1.Ecological intensification involves the incorporation of biodiversity based ecosystem service management into farming systems in order to make crop production more sustainable and reduce reliance on anthropogenic inputs, including fertiliser and insecticides. 2.The benefits of effectively managing ecosystem services such as pollination and pest regulation for improved yields have been demonstrated in a number of studies, however recent evidence indicates that these benefits interact with conventional agronomic inputs such as fertiliser and irrigation. Despite the important contribution of biodiversity‐based ecosystem services to crop production their management is rarely considered in combination with more conventional agronomic inputs. 3.This study combines a number of complementary approaches to evaluate the impact of insect pollination on yield parameters of Brassica napus and how this interacts with a key agronomic input, fertiliser. We incorporate data from a flight cage trial and multiple field studies to quantify the relationships between yield parameters to determine whether insufficient insect pollination may limit crop yield. 4.We demonstrate that, by producing larger seeds and more pods, B. napus has the capacity to modulate investment across yield parameters and buffer sub‐optimal inputs of fertiliser or pollination. However, only when fertiliser is not limiting can the crop benefit from insect pollination, with yield increases due to insect pollination only seen under high fertiliser application. 5.A non‐linear relationship between seed set per pod and yield per plant was found, with increases in seed set between 15 and 25 seeds per pod resulting in a consistent increase in crop yield. The capacity for the crop to compensate for lower seed set due to sub‐optimal pollination is therefore limited. 6.Synthesis and applications. Oilseed rape has the capacity to compensate for sub‐optimal agronomic or ecosystem service inputs although this has limitations. Insect pollination can increase seed set and so there are production benefits to be gained through effective management of wild pollinators or by utilising managed species. Our study demonstrates however that increased insect pollination cannot simply replace other inputs, and if resources such as fertiliser are limiting, then yield potential cannot be reached. We highlight the need to consider insect pollination as an agronomic input to be effectively managed in agricultural systems

Table_2_Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis.docx

BackgroundDespite the high prevalence of lung cancer, with a five-year survival rate of only 23%, the underlying molecular mechanisms of non-small cell lung cancer (NSCLC) remain unknown. There is a great need to identify reliable candidate biomarker genes for early diagnosis and targeted therapeutic strategies to prevent cancer progression.MethodsIn this study, four datasets obtained from the Gene Expression Omnibus were evaluated for NSCLC- associated differentially expressed genes (DEGs) using bioinformatics analysis. About 10 common significant DEGs were shortlisted based on their p-value and FDR (DOCK4, ID2, SASH1, NPR1, GJA4, TBX2, CD24, HBEGF, GATA3, and DDR1). The expression of significant genes was validated using experimental data obtained from TCGA and the Human Protein Atlas database. The human proteomic data for post- translational modifications was used to interpret the mutations in these genes.ResultsValidation of DEGs revealed a significant difference in the expression of hub genes in normal and tumor tissues. Mutation analysis revealed 22.69%, 48.95%, and 47.21% sequence predicted disordered regions of DOCK4, GJA4, and HBEGF, respectively. The gene-gene and drug-gene network analysis revealed important interactions between genes and chemicals suggesting they could act as probable drug targets. The system-level network showed important interactions between these genes, and the drug interaction network showed that these genes are affected by several types of chemicals that could serve as potential drug targets.ConclusionsThe study demonstrates the importance of systemic genetics in identifying potential drug- targeted therapies for NSCLC. The integrative system- level approach should contribute to a better understanding of disease etiology and may accelerate drug discovery for many cancer types.</p

Table_1_Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis.docx

BackgroundDespite the high prevalence of lung cancer, with a five-year survival rate of only 23%, the underlying molecular mechanisms of non-small cell lung cancer (NSCLC) remain unknown. There is a great need to identify reliable candidate biomarker genes for early diagnosis and targeted therapeutic strategies to prevent cancer progression.MethodsIn this study, four datasets obtained from the Gene Expression Omnibus were evaluated for NSCLC- associated differentially expressed genes (DEGs) using bioinformatics analysis. About 10 common significant DEGs were shortlisted based on their p-value and FDR (DOCK4, ID2, SASH1, NPR1, GJA4, TBX2, CD24, HBEGF, GATA3, and DDR1). The expression of significant genes was validated using experimental data obtained from TCGA and the Human Protein Atlas database. The human proteomic data for post- translational modifications was used to interpret the mutations in these genes.ResultsValidation of DEGs revealed a significant difference in the expression of hub genes in normal and tumor tissues. Mutation analysis revealed 22.69%, 48.95%, and 47.21% sequence predicted disordered regions of DOCK4, GJA4, and HBEGF, respectively. The gene-gene and drug-gene network analysis revealed important interactions between genes and chemicals suggesting they could act as probable drug targets. The system-level network showed important interactions between these genes, and the drug interaction network showed that these genes are affected by several types of chemicals that could serve as potential drug targets.ConclusionsThe study demonstrates the importance of systemic genetics in identifying potential drug- targeted therapies for NSCLC. The integrative system- level approach should contribute to a better understanding of disease etiology and may accelerate drug discovery for many cancer types.</p